diff --git a/code/estimark/scf.py b/code/estimark/scf.py
index 4c95a3f..b7b6cde 100644
--- a/code/estimark/scf.py
+++ b/code/estimark/scf.py
@@ -15,15 +15,16 @@
 csv_file_path = Path(__file__).resolve().parent / ".." / "data" / "SCFdata.csv"
 
 # Define the variables to keep
-keep_vars = ["age", "age_group", "wealth_income_ratio", "weight"]
+keep_vars = ["age", "age_group", "wealth_income_ratio", "weight", "wave"]
 
 # Read the CSV file and filter data in one step
 scf_data = pd.read_csv(csv_file_path)
-scf_data = scf_data.loc[
+scf_data_full = scf_data.loc[
     (scf_data.norminc > 0.0)
     & (scf_data.education == education)
     & (scf_data.age > initial_age)
-    & (scf_data.age <= final_age_data)
-    & (~scf_data.age.isin(remove_ages_from_scf)),
+    & (scf_data.age <= final_age_data),
     keep_vars,
 ]
+
+scf_data = scf_data_full.loc[~scf_data.age.isin(remove_ages_from_scf)]
diff --git a/code/notebooks/Portfolio.ipynb b/code/notebooks/Portfolio.ipynb
index 7f2e5a0..8126499 100644
--- a/code/notebooks/Portfolio.ipynb
+++ b/code/notebooks/Portfolio.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,12 +11,13 @@
     "from HARK.utilities import plot_funcs\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
-    "from estimark.snp import snp_data, snp_data_full"
+    "from estimark.snp import snp_data, snp_data_full\n",
+    "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,16 +28,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(6.374030002146488, 1.0)"
+       "(6.29657511421741, 1.0)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -49,81 +50,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "95"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "portfolio_agent.T_cycle"
+    "portfolio_agent.solve()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "(1.03, 1.06, 0.2)"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "portfolio_agent.Rfree, portfolio_agent.RiskyAvg, portfolio_agent.RiskyStd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "portfolio_agent.solve()"
+    "plot_funcs([sol.cFunc for sol in portfolio_agent.solution[:-1:5]], 0, 20)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "array([0.45562135])"
       ]
      },
+     "execution_count": 34,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "plot_funcs([sol.cFunc for sol in portfolio_agent.solution[:-1:5]], 0, 20)"
+    "portfolio_agent.ShareLimit"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -133,12 +114,19 @@
     }
    ],
    "source": [
-    "plot_funcs([sol.ShareFuncAdj for sol in portfolio_agent.solution[:-1:5]], 0, 100)"
+    "plot_funcs(\n",
+    "    [\n",
+    "        *[sol.ShareFuncAdj for sol in portfolio_agent.solution[:-1:5]],\n",
+    "        lambda m: portfolio_agent.ShareLimit * np.ones_like(m),\n",
+    "    ],\n",
+    "    0,\n",
+    "    100,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -164,7 +152,7 @@
        "96"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -175,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,12 +185,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -224,12 +212,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/code/notebooks/SCF_notebook.ipynb b/code/notebooks/SCF_notebook.ipynb
index 09c21cc..ce5faa3 100644
--- a/code/notebooks/SCF_notebook.ipynb
+++ b/code/notebooks/SCF_notebook.ipynb
@@ -2,24 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from estimark.scf import scf_data\n",
+    "from estimark.scf import scf_data_full\n",
     "from estimark.parameters import age_mapping\n",
-    "from estimark.estimation import (\n",
-    "    get_empirical_moments,\n",
-    "    get_weighted_moments,\n",
-    "    weighted_median,\n",
-    ")\n",
+    "from estimark.estimation import get_weighted_moments\n",
     "import matplotlib.pyplot as plt\n",
     "from statsmodels.stats.weightstats import DescrStatsW"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -47,6 +43,7 @@
        "      <th>age_group</th>\n",
        "      <th>wealth_income_ratio</th>\n",
        "      <th>weight</th>\n",
+       "      <th>wave</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -56,6 +53,7 @@
        "      <td>(30,35]</td>\n",
        "      <td>6.697993</td>\n",
        "      <td>3676.299028</td>\n",
+       "      <td>1995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
@@ -63,6 +61,7 @@
        "      <td>(30,35]</td>\n",
        "      <td>6.697993</td>\n",
        "      <td>3822.532451</td>\n",
+       "      <td>1995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
@@ -70,6 +69,7 @@
        "      <td>(30,35]</td>\n",
        "      <td>6.697993</td>\n",
        "      <td>3779.582462</td>\n",
+       "      <td>1995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
@@ -77,6 +77,7 @@
        "      <td>(30,35]</td>\n",
        "      <td>6.697993</td>\n",
        "      <td>3570.089875</td>\n",
+       "      <td>1995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
@@ -84,6 +85,7 @@
        "      <td>(30,35]</td>\n",
        "      <td>6.697993</td>\n",
        "      <td>3803.353076</td>\n",
+       "      <td>1995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -91,6 +93,7 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>232510</th>\n",
@@ -98,6 +101,7 @@
        "      <td>(40,45]</td>\n",
        "      <td>9.602461</td>\n",
        "      <td>6283.187315</td>\n",
+       "      <td>2019</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>232511</th>\n",
@@ -105,6 +109,7 @@
        "      <td>(40,45]</td>\n",
        "      <td>11.444635</td>\n",
        "      <td>6639.658020</td>\n",
+       "      <td>2019</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>232512</th>\n",
@@ -112,6 +117,7 @@
        "      <td>(40,45]</td>\n",
        "      <td>11.547022</td>\n",
        "      <td>6580.343722</td>\n",
+       "      <td>2019</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>232513</th>\n",
@@ -119,6 +125,7 @@
        "      <td>(40,45]</td>\n",
        "      <td>10.413175</td>\n",
        "      <td>6515.081945</td>\n",
+       "      <td>2019</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>232514</th>\n",
@@ -126,77 +133,47 @@
        "      <td>(40,45]</td>\n",
        "      <td>10.317024</td>\n",
        "      <td>6663.876722</td>\n",
+       "      <td>2019</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>76067 rows × 4 columns</p>\n",
+       "<p>94529 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "        age age_group  wealth_income_ratio       weight\n",
-       "30       31   (30,35]             6.697993  3676.299028\n",
-       "31       31   (30,35]             6.697993  3822.532451\n",
-       "32       31   (30,35]             6.697993  3779.582462\n",
-       "33       31   (30,35]             6.697993  3570.089875\n",
-       "34       31   (30,35]             6.697993  3803.353076\n",
-       "...     ...       ...                  ...          ...\n",
-       "232510   43   (40,45]             9.602461  6283.187315\n",
-       "232511   43   (40,45]            11.444635  6639.658020\n",
-       "232512   43   (40,45]            11.547022  6580.343722\n",
-       "232513   43   (40,45]            10.413175  6515.081945\n",
-       "232514   43   (40,45]            10.317024  6663.876722\n",
+       "        age age_group  wealth_income_ratio       weight  wave\n",
+       "30       31   (30,35]             6.697993  3676.299028  1995\n",
+       "31       31   (30,35]             6.697993  3822.532451  1995\n",
+       "32       31   (30,35]             6.697993  3779.582462  1995\n",
+       "33       31   (30,35]             6.697993  3570.089875  1995\n",
+       "34       31   (30,35]             6.697993  3803.353076  1995\n",
+       "...     ...       ...                  ...          ...   ...\n",
+       "232510   43   (40,45]             9.602461  6283.187315  2019\n",
+       "232511   43   (40,45]            11.444635  6639.658020  2019\n",
+       "232512   43   (40,45]            11.547022  6580.343722  2019\n",
+       "232513   43   (40,45]            10.413175  6515.081945  2019\n",
+       "232514   43   (40,45]            10.317024  6663.876722  2019\n",
        "\n",
-       "[76067 rows x 4 columns]"
+       "[94529 rows x 5 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "scf_data"
+    "scf_data_full"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n",
-      "/home/alujan/micromamba/envs/estimatingmicrodsops/lib/python3.12/site-packages/statsmodels/stats/weightstats.py:308: FutureWarning: The provided callable <function sum at 0x7fb65411ef20> is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
-      "  dfg = df.groupby(\"vec\").agg(np.sum)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "moments = get_weighted_moments(\n",
-    "    data=scf_data,\n",
+    "    data=scf_data_full,\n",
     "    variable=\"wealth_income_ratio\",\n",
     "    weights=\"weight\",\n",
     "    groups=\"age_group\",\n",
@@ -206,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -219,6 +196,8 @@
        " '(45,50]': array([3.23681528]),\n",
        " '(50,55]': array([4.24488131]),\n",
        " '(55,60]': array([5.32876747]),\n",
+       " '(60,65]': array([6.45894082]),\n",
+       " '(65,70]': array([7.92872889]),\n",
        " '(70,75]': array([8.80298421]),\n",
        " '(75,80]': array([9.85313601]),\n",
        " '(80,85]': array([8.75530344]),\n",
@@ -226,7 +205,7 @@
        " '(90,95]': array([9.9756071])}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,45 +216,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "([<matplotlib.axis.XTick at 0x7fb5524c86b0>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb5524c8650>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb5529db680>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb5529e6000>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb552500bf0>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb552501550>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb552501e80>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb5525027b0>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb552503170>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb552502150>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb552503a40>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb5525283e0>],\n",
-       " [Text(0, 0, '(25,30]'),\n",
-       "  Text(1, 0, '(30,35]'),\n",
-       "  Text(2, 0, '(35,40]'),\n",
-       "  Text(3, 0, '(40,45]'),\n",
-       "  Text(4, 0, '(45,50]'),\n",
-       "  Text(5, 0, '(50,55]'),\n",
-       "  Text(6, 0, '(55,60]'),\n",
-       "  Text(7, 0, '(70,75]'),\n",
-       "  Text(8, 0, '(75,80]'),\n",
-       "  Text(9, 0, '(80,85]'),\n",
-       "  Text(10, 0, '(85,90]'),\n",
-       "  Text(11, 0, '(90,95]')])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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Y9TnCg3yx/uGeaFzXB2ev5GL0ol24kGHfArL9ZBrOXsmFv7c77u7cyK7P7cpYPoiIqJQr2fkYs3gX9idlQOfjgc8f6oEezWxzSu7GdX2x/uFeCA/yQeK1AnLejgVk5bWJpiO7hsPPi6e+sheWDyIisriYacDIT3ciPkWPenU8sW5aT3Sy8WnGGwX6YN20XmgS5Iuk9FyMXrQT567m2vQ5AeDs5Rz8diINGg0wgYfX2hXLBxERAQCS03Nx/6d/4XRaDhrqvPHFw73QNizALs9dXEB6ommwL5LT8zB60S4kp9u2gKzalQgRoH+r+oio52fT56LSWD6IiAinUrNx/yc7kZyeh6bBvvhiei80q1/HrhkaXisgEcG+OHfVtgUkJ78IX+xNBuCcV69VO5YPIiIXd+RCJkZ9uhMX9Qa0alAH/3u4FxrX9VUkS5iueBdMZD0/nM+wXQH5+sB5ZBmKEBHsi5tb1rf641PlWD6IiFxYXNJVjFm0C1dyChDVKADrpvVCSICyF30L1Xlj7dSeaFaigCRdsV4BERHLGU0n9IqAmxsPr7U3lg8iIhf11+nLGP/ZbugNRejatC7WTO2JID9PpWMBuFZAppUsIDuReCXHKo+960w6TlzKhq+nFiO61O6EaVQzLB9ERC7ol2OXMHnZHuQWGNG3RT2snNIdAd4eSscqpUGAN9ZN64lm9f1wIdOA0Yt2WaWAmLd63BPdCDofdf0/uwqWDyIiF/Pd3ymYtnIf8otMGNS2AT6b1BW+nuo8x0XItQLSvL4fUjINGPXpLpy9XPMCcj4jD9viLwLgdVyUxPJBRORC/rc3GY+tjUORSXBnp4ZYOD4G3h5apWNVKsTfG+um9UKLkDq4qDdg1KKdSKhhAfl8VyJMAvRqFoxWDfytnJSqiuWDiMhFrNx5Fv/a8DdMAozuFo7/jOoMD61jfA3U9/fC2qk90TKkDi7p8zF60U6cScuu1mMYCo1Yt6f48Fpu9VCWY6x1RERUKx//dgovf3MEAPBgn0jMu7cDtA52lEd9fy+sndYTrRqYC8gunK5GAdn8dwrScwrQUOeNQW2te50aqh6WDyIiJyYiePuHY3hr63EAwOO3tMC/h7V12Ku31qtTvAWkdQN/pGYVF5BTqTcuICUPrx3fqyncHWSLj7Pi6BMROSmTSTBnUzw++vU0AOCF29rg6VtbO2zxMAuu44U1U3ugTag/0rKKL4J3KjWr0t/Zn5yBQ+cz4enuhtHdmtgpKVWE5YOIyAkZTYLnv/oby6/9tf/aXe3x8M3NlQ1lRcUFpKelgIxetBsnL1VcQMxbPe7s1FA15zJxZSwfREQOzmgS7Dx9Bd8cOI+dp6/AUGjEE+v244u95+CmAd69vxMm9IpQOqbVBfl5Yu3UnmgbFoDL2cVbQE6UU0BSswz4/lAKAOABTjRVBXUe2E1ERFWy9XAK5myKR0qmwXKbl7sb8otM8NBq8MHoaNzWIUzBhLZV188Tax7qgXGf7UZ8ih5jFu3Cmqk90TrUH0aTIDYhHSt3nkWhURDdJBBRjXRKRyawfBAROayth1MwY3Uc5Lrb84tMAICH+zVz6uJhVtfPE2umFheQIxf0GLt4Fx7p3xyf/ZFQqpQlpOVg6+EUDI1y/jFRO+52ISJyQMZrk0mvLx4lfRl3HkZTZUs4j0BfT3z+UA9ENQrAlZwCvPbd0VLFAwAy8goxY3Ucth5OUSglmbF8EBE5oNiE9DJfrtdLyTQgNiHdTomUF+jriZUP9oCHtvKjeeZsineZUqZWLB9ERA4oNavy4lHd5ZzF8YtZKDRWXCwErlfK1Ijlg4jIAdWr41Wl5UL8vW2cRF1YyhwDJ5wSETkYvaEQy/5MqHQZDYBQnTe6RwbZJ5RKVLVsuVopUxuWDyIiB3LiUhYeXrUPCZdz4O6mQZFJoAFKTTw1z3iYPbydw12/pba6RwYhTOeNi5mGcifjumopUxvudiEichDf/Z2Cuz/6EwmXc9BQ542vHumNT8bHIFRX+q/4UJ03Fo6PcclDSrVuGswe3g7APyXMzJVLmdpoRERVU371ej10Oh0yMzMREBCgdBwiIsUVGU14e9txfLr9DACgd/NgfDgmGsHX5n2YT6aVmmVAiH/xX/Wu/uVa3snXwnTemD28nUuWMnuozvc3ywcRkYql5xTg8bX78cepywCAaf2a4dkhrXlV1ipgKbOv6nx/c84HEZFKHT6fiYdX7cP5jDz4emrx1oiOGNaxodKxHIbWTYNezYOVjkHlYPkgIlKhDfvOYdbGQ8gvMiEi2BefTuiK1qH+SscisgqWDyIiFSkoMmHud/FYuTMRADCwTQjeG9UZOh8PhZMRWQ/LBxGRSqTqDXjk8zjsTbwKAHhiYEs8MbAl3DhPgZwMywcRkQrsS0zHjNVxSM3Kh7+3O94f1RkD2zZQOhaRTbB8EBEpSESwelciXt0cj0KjoFWDOvh0QldE1vNTOhqRzbB8EBEpxFBoxEtfH8aGfecAAHd0DMNb93WEnxc/msm5cQ0nIlLAuau5mL56Hw6f18NNAzx/WxtMvakZNBrO7yDnx/JBRGRnf5y8jMfWxuFqbiHq+npgwdgY9GlRT+lYRHbD8kFEZCcigkU7zmD+1mMwCdChkQ4Lx8egcV1fpaMR2RXLBxGRHeTkF+HZDX/ju0MpAIARXRpj7t1R8PbQKpyMyP5YPoiIbCzhcg6mrdyLk6nZ8NBq8PLw9hjfownnd5DLYvkgIrKhn+Iv4an1B5CVX4QQfy8sHB+DLk2DlI5FpCiWDyIiGzCZBO//fBIf/HwSANC1aV18PC4GIQHeCicjUp7Vr8lcVFSEl156CZGRkfDx8UGzZs3w6quvwmQyWfupiIhUKTOvEFNW7LEUj0m9mmLN1J4sHkTXWH3Lx/z58/HJJ59gxYoVaN++Pfbu3YvJkydDp9PhiSeesPbTEREpxmgSxCakIzXLgBB/b3SPDMLJ1Cw8vGofEq/kwsvdDa/f0wEjujRWOiqRqli9fOzcuRN33XUX7rjjDgBAREQE1q5di71791r7qYiIFLP1cArmbIpHSqbBclugjwdyC4woMJrQKNAHn07ogqhGOgVTEqmT1Xe79O3bFz///DNOnDgBADh48CD++OMP3H777eUun5+fD71eX+ofEZGabT2cghmr40oVDwDIyCtEgdGENqH+2PRYXxYPogpYfcvHc889h8zMTLRp0wZarRZGoxGvv/46xowZU+7y8+bNw5w5c6wdg4jIJowmwZxN8ZBKlsnILYDOx8NumYgcjdW3fKxfvx6rV6/GmjVrEBcXhxUrVuCdd97BihUryl3+hRdeQGZmpuVfcnKytSMREVlNbEJ6mS0e17uoz0dsQrqdEhE5Hqtv+fjXv/6F559/HqNHjwYAdOjQAYmJiZg3bx4mTZpUZnkvLy94eXlZOwYRkU2kZlVePKq7HJErsvqWj9zcXLi5lX5YrVbLQ22JyCmE+FftcNmqLkfkiqy+5WP48OF4/fXX0aRJE7Rv3x779+/He++9hwcffNDaT0VEZHfdI4MQ4O0OvaGo3Ps1AEJ1xYfdElH5rF4+PvzwQ/z73//GI488gtTUVDRs2BAPP/wwXn75ZWs/FRGR3R1N0SO3wFjufeYrtcwe3g5aN163hagiGhGpbNK23en1euh0OmRmZiIgIEDpOEREFlmGQgz78A8kXslFx0Y6pGYZcFGfb7k/TOeN2cPbYWhUmIIpiZRRne9vXtuFiKgKRATPf3kIiVdy0SjQByundIe/t0eZM5xyiwfRjbF8EBFVwepdifjuUArc3TT4cGw0An09AQC9mgcrnIzI8Vj9aBciImdz6FwmXtt8FADw/G1tENOkrsKJiBwbywcRUSX0hkLMXBOHAqMJg9s1wJS+kUpHInJ4LB9ERBUQETy34W8kpRfP83hnRCdoNJzTQVRbLB9ERBVYuTMRWw5fhIdWg4/GxUDny+u1EFkDywcRUTn+PpeBud/FAwBeuK0tOocHKhuIyImwfBARXSczr3ieR6FRMKR9A0zuE6F0JCKnwvJBRFSCiODZDQeRnJ6HxnV98BbneRBZHcsHEVEJy/86ix+OXCqe5zE2BjofzvMgsjaWDyKiaw4mZ+CN74vP5zHr9rboxHkeRDbB8kFEBCAz9595HrdFhWJS7wilIxE5LZYPInJ5IoJ/bTiIc1fz0CTIF/NHdOQ8DyIbYvkgIpe39M+z2BZ/CZ5aN3w0NgYB3pznQWRLLB9E5NIOJGfgzS3X5nnc0RYdGusUTkTk/Fg+iMhlZeQWYObnxfM8bu8Qiom9miodicglsHwQkUsSEfzf//7G+Yw8NA32xZv3cZ4Hkb2wfBCRS1ryRwJ+Osp5HkRKYPkgIpcTl3QVb245BgD497C2iGrEeR5E9sTyQUQuJSO3AI+t2Y8ik+COjmEY35PzPIjsjeWDiFyGiOCZLw7ifEYeIoJ98ea9HTjPg0gBLB9E5DIW/34GPx9Lhae7GxaMjYE/53kQKYLlg4hcwr7EdMzfehwA8PKwdpznQaQglg8icnpXc4rneRhNguGdGmJcjyZKRyJyaSwfROTUTCbBM/87iAuZBkTW88Mb90RxngeRwlg+iMipLf79DH65Ns/jI87zIFIFlg8iclp7z6bjrR+K53m8Mrw92jUMUDgREQEsH0TkpNJzCvDY2uJ5Hnd1bogx3cOVjkRE17B8EJHTMZkET39xACmZBjSr54fX7+H5PIjUhOWDiJzOpzvO4LfjafByd8NH42JQx8td6UhEVALLBxE5lT1n0/HOtuJ5HnPubI+2YZznQaQ2LB9E5DSuZOdbzudxd+eGGNWN8zyI1Ijlg4icgskkeOqLg7ioN6BZfc7zIFIzlg8icgoLt5/GjhNp8PZww8fjYuDHeR5EqsXyQUQOb/eZK3j32jyPV++MQptQzvMgUjOWDyJyaJez8/H4uv0wCXBvdCPc37Wx0pGI6AZYPojIYZlMgqfWH8AlfT5ahNTBXF63hcghcKcoETkUo0kQm5CO1CwD/jx1Bb+fvAxvj+Lrtvh68iONyBHwnUpEDmPr4RTM2RSPlExDqdtHdg1H61B/hVIRUXVxtwsROYSth1MwY3VcmeIBAKt2JmLr4RQFUhFRTbB8EJHqGU2COZviIZUsM2dTPIymypYgIrVg+SAi1YtNSC93i4eZAEjJNCA2Id1+oYioxlg+iEj1UrMqLh41WY6IlMXyQUSqV7+OV5WWC/H3tnESIrIGHu1CRKomIthy+GKly2gAhOq80T0yyD6hiKhWuOWDiFRLRDD3u6NYtSsR5lOHXX8KMfPPs4e3g9aNJxgjcgQsH0SkSiKC+VuPY8kfCQCAefd2wCfjYxCqK71rJVTnjYXjYzA0KkyJmERUA9ztQkSq9J8fT+CT7acBAK/dHYXR3ZsAAAa3C7Wc4TTEv3hXC7d4EDkWlg8iUp0Pfj6JD345BaB4d8qEnk0t92ndNOjVPFipaERkBdztQkSqsvC303jvxxMAgFm3t8XkPpEKJyIia2P5ICLV+Oz3M5i/9RgA4F9DWmNqv2YKJyIiW2D5ICJVWPHXWcz97igA4MlBLTFzQAuFExGRrdikfJw/fx7jx49HcHAwfH190blzZ+zbt88WT0VETuDz3YmY/e0RAMCjA1rgiYEtFU5ERLZk9QmnV69eRZ8+fTBgwABs2bIFISEhOH36NAIDA639VETkBL7Yk4xZGw8DAB7u1wzP3NoKGg2PXiFyZlYvH/Pnz0d4eDiWLVtmuS0iIsLaT0NETuCruHN47qu/AQCT+0Tg+dvasHgQuQCr73b59ttv0bVrV9x///0ICQlBdHQ0Fi9eXOHy+fn50Ov1pf4RkfP79uAF/N//DkIEmNCzKV4e1o7Fg8hFWL18nDlzBgsXLkTLli3xww8/YPr06Xj88cexcuXKcpefN28edDqd5V94eLi1IxGRynx/KAVPrT8AkwBjuodjzp3tWTyIXIhGRMSaD+jp6YmuXbvir7/+stz2+OOPY8+ePdi5c2eZ5fPz85Gfn2/5Wa/XIzw8HJmZmQgICLBmNCJSgW1HLuKRz+NQZBKM6NIYb93XEW48QymRw9Pr9dDpdFX6/rb6lo+wsDC0a9eu1G1t27ZFUlJSuct7eXkhICCg1D8ick6/HkvFzDXFxePuzg0xn8WDyCVZvXz06dMHx48fL3XbiRMn0LRp0wp+g4hcwY4TaXh49T4UGgV3dAzDO/d34jVZiFyU1cvHU089hV27duGNN97AqVOnsGbNGixatAgzZ8609lMRkYP469RlTF25FwVFJgxp3wDvj+oMdy3PcUjkqqz+7u/WrRs2btyItWvXIioqCq+99href/99jBs3ztpPRUQOYPeZK5iyYi/yi0wY2CYEH46JgQeLB5FLs/qE09qqzoQVIlK3fYnpmLAkFrkFRtzcqj4WTewCL3et0rGIyAYUnXBKRAQAB5IzMGnpHuQWGNG3RT18OoHFg4iKsXwQkdUdPp+JCUt2Izu/CD2bBWHxxK7w9mDxIKJiLB9EZFXxF/QY99luZBmK0C2iLpZM6gYfTxYPIvoHywcRWc3xi1kYv2Q3MvMKEd0kEMsmd4efl9UvIUVEDo7lg4is4lRqNsZ9tgvpOQXo2FiH5ZO7ow6LBxGVg+WDiGot4XIOxi7ehcvZBWgXFoCVD3aHzsdD6VhEpFIsH0RUK4lXcjBm0S6kZuWjTag/Vj/UA4G+nkrHIiIVY/kgoho7dzUXYxfvxkW9AS1C6mD1Qz0Q5MfiQUSVY/kgohq5kJGHMYt34XxGHprV88Oah3qgXh0vpWMRkQPgbDAiuiGjSRCbkI7ULANC/L3RNNgXYxfvQnJ6HpoG+2LN1J4ICfBWOiYROQiWDyKq1NbDKZizKR4pmQbLbVo3DYwmQeO6PlgztSdCdSweRFR1LB9EVKGth1MwY3Ucrr8AlNFUfMv0m5uhUaCP/YMRkUPjnA8iKpfRJJizKb5M8Sjpo19PW4oIEVFVsXwQUbliE9JL7WopT0qmAbEJ6XZKRETOguWDiMqVmlV58ajuckREZiwfRFSuEP+qTSKt6nJERGYsH0RUrohgX2jdNBXerwEQpvNG98gg+4UiIqfA8kFEZVzMNGDcZ7srnExqriSzh7ertKAQEZWH5YOISjmfkYdRi3bizOUcNAr0wWt3tUfYdefxCNV5Y+H4GAyNClMoJRE5Mp7ng4gsktNzMWbxLpy7mofwIB+sndoTjev6YmyPpqXOcNo9MohbPIioxlg+iAhA8dVpxy7ejfMZeYi4dsr0htdOIKZ106BX82CFExKRs2D5ICIkXM7BmEW7cFFvKL5IHE+ZTkQ2xPJB5OJOpWZj7OJdSM3KR4uQOlgztQcPnyUim2L5IHJhJy5lYezi3bicnY/WDfzx+dQeqFfHS+lYROTkWD6IXNTRFD3Gf7YbV3IK0DYsAJ8/1ANBfp5KxyIiF8DyQeSCjlzIxPjPduNqbiGiGgVg9ZQeCPRl8SAi+2D5IHIxh85lYvyS3cjMK0SnxjqsfLAHdL4eSsciIhfC8kHkQvYnXcXEpbHIMhQhpkkglj/YHQHeLB5EZF8sH0QuYl9iOiYt3YPs/CJ0i6iLZZO7o44XPwKIyP74yUPkAmIT0jF5WSxyCozo2SwISyZ1gx+LBxEphJ8+RE7ur9OXMWX5XuQVGtGnRTA+m9gNPp5apWMRkQtj+SByYn+cvIyHVu6BodCEfq3qY9GELvD2YPEgImWxfBA5qd+Op2Laqn0oKDLhljYh+HhcDIsHEakCyweRE/r56CXMWB2HAqMJg9s1wIKx0fByZ/EgInVg+SByMj8cuYhH18Sh0CgY2j4UH4yJhqe7m9KxiIgsWD6InMj3h1Lw+Nr9KDIJ7ugYhvdHdYaHlsWDiNSF5YPISWw6eAFPrj8Ao0lwV+eGePf+TnBn8SAiFWL5IHICG/efwzNfHIRJgPtiGuOtER2hddMoHYuIqFwsH0QO7n97k/Hsl39DBBjVNRzz7u0ANxYPIlIxbpMlcmDrYpMsxWNcjyYsHkTkELjlg8hBrd6ViJe+PgwAmNSrKV65sz00GhYPIlI/lg8iB7Tir7OY/e0RAMCUvpF46Y62LB5E5DBYPogczGe/n8Hc744CAB6+uRmeH9qGxYOIHArLB5FKGU2C2IR0pGYZEOLvje6RQVj8+xm8ueUYAODRAS3wzK2tWDyIyOGwfBCp0NbDKZizKR4pmQbLbXW83JGdXwQAeHJQSzwxsCWLBxE5JJYPIpXZejgFM1bHQa673Vw87uwUhicHtbJ/MCIiK+GhtkQqYjQJ5myKL1M8Stpz9iqMpsqWICJSN5YPIhWJTUgvtaulPCmZBsQmpNspERGR9bF8EKlIalblxaO6yxERqRHLB5GKhPh7W3U5IiI1YvkgUpFWDerAQ1vxESwaAGG64sNuiYgclc3Lx7x586DRaPDkk0/a+qmIHFpKZh5GL9qFQmP5k0nNlWT28Ha8Yi0ROTSblo89e/Zg0aJF6Nixoy2fhsjhnUrNxn0f/4WTqdkIDfDGy8PaIkxXetdKqM4bC8fHYGhUmEIpiYisw2bn+cjOzsa4ceOwePFizJ0711ZPQ+Tw9iddxYPL9+BqbiGa1ffDqik90CjQB5N6R5Y5wym3eBCRM7DZlo+ZM2fijjvuwKBBg2z1FEQOb/uJNIxdvBtXcwvRqbEOG6b3RqNAHwCA1k2DXs2DcVfnRujVPJjFg4ichk22fKxbtw5xcXHYs2fPDZfNz89Hfn6+5We9Xm+LSESq882B83jmi4MoMglualkPn4zvAj8vnnSYiJyf1bd8JCcn44knnsDq1avh7X3jwwHnzZsHnU5n+RceHm7tSESqs+zPBDyx7gCKTILhnRpiyaRuLB5E5DI0ImLV8zR//fXXuOeee6DVai23GY1GaDQauLm5IT8/v9R95W35CA8PR2ZmJgICAqwZjUhxIoJ3t53Agl9PAQAe6B2Bl4e1gxt3qRCRg9Pr9dDpdFX6/rb6n1oDBw7EoUOHSt02efJktGnTBs8991yp4gEAXl5e8PLysnYMItUpMprw728OY21sMgDg/25thZkDWvDKtETkcqxePvz9/REVFVXqNj8/PwQHB5e5nchVGAqNeHztfmyLvwQ3DTD37g4Y26OJ0rGIiBTBncxENqY3FGLqir3YnZAOT60bPhjTmefqICKXZpfy8dtvv9njaYhUJzXLgElL9+Boih51vNyxaGIX9G5eT+lYRESK4pYPIhtJvJKDCUtikZSei3p1vLB8cjdENdIpHYuISHEsH0Q2cPh8Jh5YtgeXs/PRJMgXq6Z0R9NgP6VjERGpAssHkZXtPH0FU1fuRXZ+EdqGBWDFg90Q4n/jc94QEbkKlg8iK9p6OAWPrz2AAqMJ3SOD8Nmkrgjw9lA6FhGRqrB8EFnJ2tgkzNp4CCYBbm3XAB+MiYa3h/bGv0hE5GJYPohqSUSw4JdTePfHEwCA0d3CMffuKLhrbXbdRiIih8byQVQLJpPg1c3xWP7XWQDAowNa4JlbW/GspURElWD5IKqhgiITnvnfQWw6eAEAMHt4O0zuE6lwKiIi9WP5IKqBnPwiTF+9D7+fvAx3Nw3eHdkJd3VupHQsIiKHwPJBVE3pOQWYvHwPDiZnwMdDi4XjY9C/dYjSsYiIHAbLB1E1nLuai4lLY3EmLQeBvh5Y9kA3RDepq3QsIiKHwvJBVEUnLmVh4pJYXNQb0FDnjZVTuqNFiL/SsYiIHA7LB1EV7EtMx4PL9yIzrxAtQupg5YPd0TDQR+lYREQOieWD6DpGkyA2IR2pWQaE+Hsjp6AIj66Jg6HQhOgmgVg6qRvq+nkqHZOIyGGxfBCVsPVwCuZsikdKpqHMff1b18fH42Lg68m3DRFRbfBTlOiarYdTMGN1HKSC++/v0pjFg4jICnj+ZyIU72qZsym+wuIBAHO/OwqjqbIliIioKlg+iADEJqSXu6ulpJRMA2IT0u2UiIjIebF8EAFIzaq8eFR3OSIiqhjLB7k8EcHB5MwqLRvi723jNEREzo+z58il5RYU4cWvDuHrAxcqXU4DIFTnje6RQfYJRkTkxLjlg1zWqdRs3P3Rn/j6wAVo3TS4N7r4wnCa65Yz/zx7eDto3a6/l4iIqotbPsglbTp4Ac9/+TdyCoyo7++FBWOi0aNZMG5t36DMeT5Cdd6YPbwdhkaFKZiYiMh5sHyQSykoMuGN749i+V9nAQA9mwXhgzHRlrkcQ6PCMLhdaKkznHaPDOIWDyIiK2L5IJdxPiMPMz+Pw4HkDADAjP7N8czgVnDXlt77qHXToFfzYAUSEhG5BpYPcgnbT6ThyXX7cTW3EAHe7nhvZGcMatdA6VhERC6J5YOcmtEk+O/PJ/HhLychAkQ1CsDCcV0QHuSrdDQiIpfF8kFO60p2Pp5cfwC/n7wMABjbowleHtYO3h5ahZMREbk2lg9ySvsSr+LRNXFIyTTA28MNb9zTAffGNFY6FhERgeWDnIyIYNmfZ/HG90dRZBI0q+eHheO7oHWov9LRiIjoGpYPchpZhkI8/+UhfHcoBQBwR4cwvHlfB/h7eyicjIiISmL5IKdw7KIej6yOw5nLOXB302DWHW3xQO8IaDQ8PwcRkdqwfJDD+yruHF7ceAiGQhPCdN5YMDYGXZrWVToWERFVgOWDHJah0IhXN8djze4kAMBNLevh/VGdEVzHS+FkRERUGZYPckjJ6bmY8fk+HD6vh0YDPH5LSzw+sCVPg05E5ABYPsjh/BR/CU9/cQB6QxHq+nrg/dHRuLlVfaVjERFRFbF8kMMoMprw7o8nsPC30wCAzuGB+HhcDBoG+iicjIiIqoPlgxxCapYBj6/dj11n0gEAD/SOwIu3t4Wnu9sNfpOIiNSG5YNUb/eZK3h07X6kZeXDz1OL+SM6YljHhkrHIiKiGmL5IFUwmgSxCelIzTIgxN8b3SOD4KYBFu04g7d+OA6jSdCqQR18PK4LWoTUUTouERHVAssHKW7r4RTM2RSPlEyD5bYGAV4IDfDGwXOZAIB7ohvh9Xui4OvJVZaIyNHxk5wUtfVwCmasjoNcd/slfT4u6fPh7qbBnLvaY2z3JjxbKRGRk+BsPVKM0SSYsym+TPEoKdDXA6O7sXgQETkTlg9STGxCeqldLeW5nF2A2IR0OyUiIiJ7YPkgxaRmVV48qrscERE5BpYPUkRBkQlxiRlVWjbE39u2YYiIyK444ZTsSkSw9fBFzN96DGev5Fa6rAZAqK74sFsiInIeLB9kN/sS0/H6d0cRl5QBAKhXxxO3tmuANbHJ0AClJp6ap5fOHt6OF4sjInIyLB9kc2fSsvHW1uPYeuQiAMDHQ4up/ZphWr9mqOPljn6t6pc5z0eozhuzh7fD0KgwpWITEZGNsHyQzVzJzsd/fz6JNbuTUGQSuGmAkV3D8dTgVmgQ8M88jqFRYRjcLrTMGU65xYOIyDmxfJDV5RUYsfTPBCz87TSy84sAALe0CcHzt7VBqwb+5f6O1k2DXs2D7RmTiIgUwvJBVmM0Cb6MO4f3tp3ARX3xLpSoRgF48fa26N28nsLpiIhILaxePubNm4evvvoKx44dg4+PD3r37o358+ejdevW1n4qUgkRwfYTaXhzyzEcu5gFAGgU6INnh7bG8I4N4cbdJ0REVILVy8f27dsxc+ZMdOvWDUVFRZg1axZuvfVWxMfHw8/Pz9pPRwo7ciET874/hj9OXQYABHi749FbWmBirwh4e2gVTkdERGqkEZHKLq1Ra2lpaQgJCcH27dvRr1+/Gy6v1+uh0+mQmZmJgIAAW0ajWjifkYd3tx3Hxv3nIQJ4at0wsVdTPHpLCwT6eiodj4iI7Kw63982n/ORmVl8SfSgoPJPFJWfn4/8/HzLz3q93taRqBb0hkJ8/OtpLP0zAQVFJgDAnZ0a4l9DWiM8yFfhdERE5AhsWj5EBE8//TT69u2LqKiocpeZN28e5syZY8sYZAUFRSas3pWID385iau5hQCAHpFBePH2tugUHqhsOCIicig23e0yc+ZMfPfdd/jjjz/QuHHjcpcpb8tHeHg4d7uohIjg+0MX8dYPx5B47XToLULq4PmhbTCwbQgvdU9ERABUstvlsccew7fffosdO3ZUWDwAwMvLC15eXraKQZUwmqTSE3vtOVt8OvQDyRkAgHp1vPD04FYY2bUx3LW8JiEREdWM1cuHiOCxxx7Dxo0b8dtvvyEyMtLaT0FWsPVwSplTmoddO6V5ywb+mL/lGLbFXwIA+HpqMa1fM0y9qRn8vHhqGCIiqh2rf5PMnDkTa9aswTfffAN/f39cvFh8PQ+dTgcfHx9rPx3VwNbDKZixOg7X729LyTRg+uo4uGkAkwBuGmBUtyZ4alBLhATwsvZERGQdVp/zUdEcgGXLluGBBx644e/zUFvbMpoEfef/UmqLR3kGXjsdessKTodORERUkqJzPmx82hCqpdiE9BsWDwB46KZmLB5ERGQTnDXoYlKzblw8qrMcERFRdbF8uJgQ/6rN3ajqckRERNXFQxdciIjgVGp2pctoAITqig+7JSIisgWWDxeRmVeIF776G98fuljhMuapwrOHtyt1vg8iIiJrYvlwAfsSr+LxtftxPiMP7m4aPDe0DRrV9cZrm4+Wmnwaeu08H0OjwhRMS0REzo7lw4kZTYJPtp/Gez+egNEkaBLkiw/HRFuuxTKkfVilZzglIiKyBZYPJ5WqN+CpLw7gz1NXAAB3dW6IuXdHwd/bw7KM1k2DXs2DlYpIREQuiuXDCf16PBX/98VBXMkpgI+HFq/e1R4jujTmReCIiEgVWD6cSEGRCW9tPYbP/kgAALQNC8CHY6LRIqSOwsmIiIj+wfLhJM5ezsHj6/bj73OZAIAHekfg+dvawNtDq3AyIiKi0lg+nMDX+89j1sZDyCkwItDXA2/d1xG3tg9VOhYREVG5WD4cWE5+EV7+5gi+jDsHAOgeGYT/ju6MMB2vHkxEROrF8uGgjlzIxGNr9uPM5Ry4aYDHB7bEY7e05KGyRESkeiwfDkZEsPyvs5j3/TEUGE0IDfDGf0d3Ro9mPGSWiIgcA8uHA0nPKcCzGw7ip6OpAIBBbRvg7REdUdfPU+FkREREVcfy4SB2nr6CJ9fvxyV9Pjy1bph1R1tM7NWU5+4gIiKHw/KhckVGEz74+SQ+/PUURIBm9f3w4ZhotG+oUzoaERFRjbB8qNiFjDw8sW4/9py9CgAY2bUxXrmzPXw9+bIREZHj4reYSv1w5CKe3fA3MvMKUcfLHa/fE4W7OjdSOhYREVGtsXyojKHQiNe/O4pVuxIBAJ0a6/DBmGg0DfZTOBkREZF1sHyoyKnULDy6Zj+OXcwCADzcrxmeubU1PN3dFE5GRERkPSwfCjCaBLEJ6UjNMiDE3xvdIuriy7hzeOXbeOQVGlGvjifeHdkZN7eqr3RUIiIiq2P5sLOth1MwZ1M8UjINltu8PdxgKDQBAPq2qIf3RnVCiL+3UhGJiIhsiuXDjrYeTsGM1XGQ6243F497ohvi3fs7w42nSCciIifGyQR2YjQJ5myKL1M8Stp1Jr3S+4mIiJwBy4cdmEyCdbFJpXa1lCcl04DYhHQ7pSIiIlIGd7vYSEZuAXacvIzfjqdix4k0XM4uqNLvpWZVXlCIiIgcHcuHlZhMgiMX9PjteCp+O5GG/UlXYSqxD6XkpNLKcKIpERE5O5aPWsjMLcSOk2n47Xgatp9Iw+Xs/FL3t2pQB/1bh6B/6/qIDq+LW979DRczDeXO69AACNV5o3tkkF2yExERKYXloxpMJkF8SvHWjV+Pl9264eepRZ8W9dC/dQhubl0fjQJ9Sv3+7OHtMGN1HDRAqQKiKXG/lke6EBGRk2P5uIHM3EL8fioNvx67wdaNVvXRNSKo0rORDo0Kw8LxMWXO8xGq88bs4e0wNCrMZv8fREREauEy5eP6s4p2jwwqdytDya0bvx1PQ1w5Wzd6t6iH/q3ro3/rkDJbN25kaFQYBrcLrVIWIiIiZ+QS5aO8s4qGldjaYN66YZ67kZZVeutGy5A6GNCmals3qkLrpkGv5sG1egwiIiJH5fTlo6KziqZkGjB9dRxa1PdDwpVcGEts3vC1zN2o2dYNIiIiqphTl4+qnFX0VFoOgOKtG+ay0TWiLrzctfYJSURE5GKcunzEJqTf8KyiAPDB6Gjc2bmhHRIRERGRU59evapnCxVeUYWIiMhunLp8VPVsoTyrKBERkf04dfnoHhmEMJ03KjqIVYPio154VlEiIiL7ceryoXXTYPbwdgBQpoDwrKJERETKcOryAfxzVtFQXeldK6E6bywcH8OzihIREdmZUx/tYsazihIREamHS5QPgGcVJSIiUgun3+1CRERE6sLyQURERHbF8kFERER2xfJBREREdsXyQURERHbF8kFERER2xfJBREREdsXyQURERHalupOMiRRf3l6v1yuchIiIiKrK/L1t/h6vjOrKR1ZWFgAgPDxc4SRERERUXVlZWdDpdJUuo5GqVBQ7MplMuHDhAvz9/aHRWPfaK3q9HuHh4UhOTkZAQIBVH9tRs6glB7OoOwezqD+LWnIwi7pz2DKLiCArKwsNGzaEm1vlszpUt+XDzc0NjRs3tulzBAQEKP7im6kli1pyAMyi5hwAs1RELVnUkgNgFjXnAGyT5UZbPMw44ZSIiIjsiuWDiIiI7MqlyoeXlxdmz54NLy8vpaOoJotacjCLunMwi/qzqCUHs6g7h1qyqG7CKRERETk3l9ryQURERMpj+SAiIiK7YvkgIiIiu2L5ICIiIrti+SAiIiK7YvkgIiIiu2L5qMRvv/2GX3/9VekYqskBMIuacwDMouYcALOoOQfALBWxSRahci1YsEC0Wq306tVLfv75Z5fPwSzqzsEs6s7BLOrOwSz2z8KTjJXj3LlzuPXWWzF8+HCcOXMGGRkZeP755zFw4ECXzMEs6s7BLOrOwSzqzsEsCmWxWo1xIgUFBZKamioiItu2bZNhw4bJoEGD5KeffnLJHMyi7hzMou4czKLuHMyiTBaWjxJMJpPlv41Go+W/f/rpJ7utACUzKJmjvDxKZlHTuHBMbpyH75+yebiulM2ilhxKZ7k+jyuMC8uHlB7ckgoKCiz/bY8VoKioSEREcnJyZPv27ZaVoOTKYK8VUS1jIqKeceGYlE8t48IxKZ9axoVjUj5XHReXLx/mFz4rK0teeukleeKJJ+TFF1+UixcvllnWliuA+UXPzMyU4OBgefXVV0vdb883hVrGREQ948IxKZ9axoVjUj61jAvHpHyuPC4uXz5Eil/4yMhIueWWW2TQoEHSuXNnCQwMlM8//1z0en2pZW2xApR80Zs2bSp33XVXucvl5+fbNEdJSo+JiPrGhWNSPqXHhWNSPrWNC8ekfK46LiwfIvLss8/KTTfdJCIihYWFUlRUJDNmzBA/Pz/55JNPJCcnp9Ty5ok3Q4YMkR9++MEqGfR6vTRv3lzuvvtuy22//PKLrF69WtasWSMGg0FE/llJbJXDTA1jIqKuceGYlE8N48IxKZ+axoVjUj5XHReWDxGZNm2ajB07VkRKD+zTTz8tfn5+smXLFhEpvQ8uPj5eWrRoIbfffrukp6fXOsNbb70lGo3G8lyTJk2SmJgYCQkJkaZNm0rTpk3l5MmTNs9hpoYxEVHXuHBMyqeGceGYlG/+/PmqGRe1jAnXlfLZe1xYPkTkiSeekMjISMvP5oYnIjJ+/Hhp0qSJpX2a93t98803otFo5LvvvrNajkmTJklQUJD0799funfvLjt37pTk5GQ5duyYDBo0SFq0aCG5ubk2zyGinjERUWZcSu7fNP+3UmNy/exzEeXWFbWMi5rGpOQXhplS60p5WdTyufL444+r5jNl4sSJqhgTEdf9rHWp8lHRrOIjR45I+/btZfr06ZYBNa8AJ0+elPDwcNm6datl+aKiIvnpp59K3WaNHCLFL75Op5Pdu3eXun3nzp1Sr1492bZtm9VymB9DpOyHeXx8vF3HpLwsJT9I7Tku5uc1GAySlpZmuV3JMbk+i4hy60peXp4kJSVZbrf3uFSUQ0S5McnOzpZ169ZZbrf3Z0plWUTsOy4Vfb4dPXpU2rVrZ9cxqeyzduLEiXZdVyrKEh8f75Lj4jLlw/zGzM3NlQ0bNshnn30me/fuFZHiw4rmz58vXbp0kWeffbbUC3Pu3Dlp1qxZqQEXKf8vr9rmMNu8ebNcvny51PPExsZKeHi47Nu3zyo5SsrKypIHHnhADh48aLktLy/PbmNSWZaSz2uPcTG/PhkZGdK1a1dZv3695T6DwWDXMakoS8lSZq91xfz/qtfrpXfv3vLwww/L1atXRcS+41JejoyMjFJjsmnTJruMifk59Xq9REREiEajkcOHD4uI/d8/5WU5cuRIqWXs+f7Jzs6WefPmyfTp02X58uWSm5ur2Pvn+izm9VbEfu+fkp/7W7dulW+//VYOHDggIsXzPN555x2Jjo6267iUzFLys19E5Ntvv7X5uLhE+TC/mJmZmRITEyPR0dHSsmVL8fDwkN9++01Eij/gX3jhBenSpYuMGjVKsrKy5OLFi7JixQpp0KCB/P333zbNsWPHjkp/d8GCBRITEyOJiYm1zmFmXmkefvhh0Wg00rVrV4mPj7fcf/XqVXnhhRckJibGZmNSlSzlbUo2s+a4lJzx3aRJE7n99tvLLJOenm6XMalKlorYYl0RKf4Qb926tYwYMULOnz9f6kMyPT1dZs2aJdHR0TZfVyrLURFrj0nJ16dx48bSt29f6dWrl7z88suWPBkZGfLiiy/a9DPlRllESh+hcD1rjov5PazX66Vt27bSp08fGTJkiGg0Gvnoo49ExH6fKZVlWbhwYaW/a+11pWSWqKgoiYmJkQ4dOoinp6c8++yzkpSUJHl5efLvf//b5u+fyrI8//zzlT6PtcfFJcqHSPEHVocOHWTkyJFy6dIlSU5OlsGDB8trr71mWSYjI0M+++wziYqKEl9fX4mKipLg4GBZu3atzXPMnTu33OUTExPlo48+El9fX/nyyy+tlqOkVatWyfTp02XIkCHSrFkzOXTokOW+3NxcWbZsmU3HpKpZSrLVuOj1eomMjJQRI0ZYbktISJDjx49LQkKCiBR/yC9evNjmY1JZlut3N4jYfl35+OOPZciQIZaft2zZIkuWLJHNmzdLXl6e5Ofny9KlS6V9+/Y2HZeKcmzZskUuXbpUallbjIn5AzwjI0MiIyNl5MiRIiLy2GOPSdOmTUvtszevKx06dLDJmFQ1y/V/ndpqXSkoKJChQ4fKuHHjLJMSn332WZk0aVKpLRH2WE8qy1IeW75/8vPz5eabb5axY8dKRkaG6PV6WbJkiWg0Ghk1apQcPXpU8vLybP79c6MsY8eOLbO7xVbj4hLlw2g0ypQpU2TYsGGlDluaPHmyPPPMM/Lmm2/K119/bbk9Ly9P1q9fLz/++KPs379fRKyzS6EqOTZt2mSZ0HPy5EmZNWuWNG7cWDZs2GC1HNdbvXq19OvXTzIyMqRnz57SsmVLSU9Pl6VLl1o299lqTKqaZcWKFZbXyFbjYjQapX///hIYGCjnzp0TkeLJYD179pR69epJvXr1LH/BFRUVSW5urs3GpCpZPvnkE8uH6okTJ2y+rjz66KMybtw4ERGZMGGCtGnTRtq3by+enp5y3333SWxsrIgU74ax5bpSWY6RI0fK9u3bRcS27x+DwSAhISGlDks8e/asNGnSRN544w0R+WdLp9FotOn7pypZSrLluKSmpkq3bt3km2++sdz24osvyt133y3333+/vPbaa5b///z8fFm3bp3N1pMbZXnjjTfkr7/+EhGR06dP2/T9k5iYKD169LDsYi8qKpKUlBRp3ry51K9fXyZNmiR5eXlSWFho08+VG2UJCQmRyZMny5UrV0TEtp8rLlE+RER27Ngh69evtwzchg0bRKPRyM033yy33XabaDQamTVrlipy/Pvf/xaR4q0OO3futOyPM5lMNvnCT0pKkltvvVVEiv/aHjBggAQFBYmPj4/8/vvvVn++mmb5888/RaS4CNlqXNatWyeRkZHy9NNPy7Bhw6RDhw7yxRdfyPfffy9z584VjUYjixYtsspzWSPLZ599JiL2WVdee+01mTFjhmzZskU6d+4s8fHxYjKZ5M8//5SuXbvKhAkTSh2CZys3ymH+y9aWY5KWliarVq0qdVt2draMHDlSBg8eXKp42FpVs5gZDAabjcvp06fF19dX3nvvPbl06ZJs3LhR3N3dZdKkSfLoo49K69at5d5775ULFy5Y5flqk6VNmzYyYsQISUtLs+lniohIXFycaDQay2eYSPFrdNddd1kOcV2zZo3Vnq+2WcwTlm05Li5TPkT+aWynTp2S7t27y4cffmjZArFs2TLx9PSUU6dOqSLHiRMnbJ7DLC8vT1q1amWZSDRr1izx8PCQsLAwy3HdrpTliy++kICAAImOjpajR4+Wuu+5556TiIiIMkeeKJnl+l0NtrJhwwbRarUybtw4mT59eqn7fvrpJ3FzcyszcU2pHOa/GO2h5Afyzp07RaPRyBdffGG351dbFvO5RQYNGiS+vr7yn//8x3Lf7t27xd3d3eqHiNYmy6ZNm2yew2QyyX333SedO3eWLVu2SFxcnLRr105uu+02ERF55JFH5N5775WCggKb/IFZkywldx/agkuVD7OCggLLrHSzH374QVq2bGnZxO0qOcxn1Lvlllvk5MmTln17n3/+ufTv31/CwsLKzJp31iwl3/Tbtm2T999/v8wb8J133pE2bdqUOeugM2cpacaMGaLRaOSOO+6QvLw8y+1JSUnSoUOHMuuzs+coyWQySV5enmWzfnZ2ts2/SNSUpeTjHzx4UE6ePCndunWTgwcPSmFhoRiNRklLS5NOnTrJ999/7zJZzH755Re5//77xd3dXSIiImTEiBGWnNOnT5d+/frZJYdasrjDxYgIPDw80L59+1K3x8fHIzw8HJ6eni6Vw929eBXo0qUL+vXrh6tXr2Lt2rW4++67cdttt+G2225Damoq2rVr5/RZNBoNRAQajQaDBw9Gv3794OXlBQCW27Ozs9G+fXuYTCbLbc6epaRHH30UmZmZ2LBhA5YuXYrRo0cjKCgIP//8M3JycuDj42PzDGrKUZJGo4G3tzeGDh2Kf/3rX0hKSkLbtm3tnkOpLCXX2Y4dOyIpKQnnzp2D0Wi0vLc3b96M9PR0NG3a1GWymA0YMAD9+vVDfHw83NzcSn32e3t7o3PnzjCZTNBoNDZ/L6sii83rjZ1UdLKsG8nKypJly5aJr6+vfPvtt7XOUd7zV2W/r7VziFRvTBYtWiRdunQp81dAYWFhlR+jMtUZF1tmqel6kp2dLcuXL5c6depY7S+lmq4rtshSnXE5deqUzJw5UzQajXTu3Fn69+8vOp3OKpv3qzMmtswhUv11peRyHTp0kNGjR1slR0UZKltXbJWlumMyduxYqVOnjjz++OMybdo0CQwMtNrrU90xsWWWmn6uXL58WZYuXSq+vr5Wu05LTT9XbJGlMk5RPkqejOnFF1+U1NTUKv3ekSNHZObMmRISEmJZCa3xxVZYWCiXL1+WjIwMy32VPa61c5TMUp0xOXv2bK2e80ZZqjMutshS0/Xk0KFDMnXqVAkKCrKc6Mtar0911xVbZqnuuPzwww/y7rvvyvvvv2+ZvKbE+8faOUpmqe6YiBR/0L/88svy66+/1irD9VmqOy7WzlLTz5Tnn3/eMiHZ/KWmxPvH1lmqu66kpaXJk08+KQ0bNrRM8FTqc8UWWW5EIyJim20q9mE0GqHVaqHX69G6dWt07doVmzZtqtLvJicnY9u2bWjZsiX69esH81DUZDOTyWSCm5sb9Ho9Ro8ejeTkZHh6eqJ169b4+OOPERgYaJccQPXHxLy8LVR3XGyVpTbrSWJiIjZv3ox27dphwIABtX59arOuWDtLdcdFbrB750b3V6S6Y2KrHEDt1pXrn782OYDarSvWzFLb9SQ3Nxeenp5wd3e3+/vHlllqu67s3bsXIoJu3bop+rli7SxV4dDlw7xS6fV6dOzYEV27dsWGDRvKXda8kphfoMLCQnh4eFj1yy4vLw/du3dH69atMWrUKCQnJ2PFihXIzs7G2rVr0b17d8uytspRmzHJz8+3zCuwppqMizWzWGM9KSoqsuwrtobarCvWylKbcTEYDPD29rbqXJOajIm1c/D9U5Y11hNrq826Yi0cl1qy6XYVOzAYDNKwYUO55ZZbLLe99957MnnyZLnnnntk9uzZltvNm6RiY2Pl3nvvtfpmpd9//13atm1bapfBlStXZPDgwdK4cWPLYYi2zlHTMbnvvvtssqmtJuNi7SxqWk9EuK6URw3riYi6xkREHePCMSkfx6Xm3Oxfd6wrNzcX9erVQ2ZmJtLS0jBx4kQsWbIEWq0WWq0Wn3/+OW6++WYAsGxZKCoqwsaNG7F8+XKrZklLS0NSUhIaNGhgeZ6goCBs3rwZLVu2xJgxYyAiNs9R0zH56quvrJ4FqNm4WDuLmtYTgOtKedSwngDqGhNAHePCMSkfx6UW7F53rMg8gzctLU26desmGo1G+vTpI8eOHbMss23bNomIiJD33ntPRP6ZRDN37lyZP39+pRctqyrzY6anp0uTJk1KnSnV/PinT5+WVq1ayfz5822WQ0T5MSnZoM3/ffXqVbuPS8kc5jG5fPmyImNS3kxzJcakoixKjIvax0Sp94/5d6+/aJ+9x6W8HGr4TDFLT0+X8PBwu68r5WVJS0uTrl27KvL9Y3488+uk1LjUhEOVj8oOF7p06ZJMmDBBFi9eLCL/DGxBQYF06tRJZs6cWWr5PXv21PgUv+Yc5ucwXzUyKytLnn/+eenbt68sX77csrzJZJKioiIZNGiQTJs2zWo5KssiYt8xKZml5MmwioqKJC8vT1544QXp06ePXcalohwixdd7sOeYlLyY1qZNmyynHzePiT3XleuzlFxX7DkuJXN88MEHlutIKD0mJbOIKLeu6PV6uemmmyxn+c3JybHruFSUQ0S5MSksLBS9Xm85eiM3N1exdcWc5erVq5b77D0u138Xmn/Ozs6WF154QXr37m23cakphznJmHnCTk5ODj777DMkJSWhadOmuO+++9CoUSOEhIRgwYIFpWbpmk+S0rRpU7Rq1QrAP5NsunbtWqsc2dnZeOmll3Dp0iUYjUa8/vrraNmyJaZNm4b4+HgsW7YMhYWFeOihh6DRaKDVatGkSRN4eXlBRGAymaDVamuco7Isb7zxBlq0aGG3MTFzc3NDbm4umjVrhq5du2Lz5s2WzY8PP/wwjhw5YpdxqSiH0WhE/fr1sWDBAsuythyTkjPhIyIi8NBDD2HYsGEAik/kM23aNLuNSWVZTCaTZVxsva7ItU2+er0ePXv2RMeOHdGzZ0/UrVsX3t7emDp1Ko4cOYKlS5fafEwqyhIUFFRqXTGZTDYdE6D069OrVy8cPXoUixYtwvvvvw9fX1+7vX8qyvHf//4Xnp6edltPzI+h1WqRlZWF8ePHIy0tDSdPnsSSJUtw55134qGHHrLb+6eiLEuXLsXw4cPtPi5ubm7Izs7Ga6+9hszMTGi1WkyZMgUxMTGYOXMm4uPjsWTJEpuPS63Yve7UgLlF6vV6adOmjQwZMkS6du0qPXv2lOHDh0t6enqFv7ts2TJp0KCB7Nq1q9Y5zO1Sr9dLixYtZPjw4TJ16lQZNGiQ9OvXT/R6vYiIHD9+XO6//36Jjo6WSZMmyTfffCNz584VT09Py1VibZ2lstMpW3NMrnf48GEJCgoSf3//Upc9Fym+QuLIkSOlc+fONhuXG+Ww15iY/0rKzMyUJk2ayD333FPqfnOOo0eP2nxduVGW65cryRbrisFgkJ49e8qdd95Z5naR4pOHjRo1yi7rSUVZKrtAni3XlcaNG8vIkSPl5ZdflubNm5f66/rkyZMycuRIm60rVclh78+UrKwsad26tYwYMUJWrFgh06dPl7p161qurXTs2DG7faZUlMW8tay8sbHluLRs2VLuvPNOmTJligwfPly0Wq28++67UlhYKImJiTZdV6zBIcqHSPEm2f79+8vIkSPFYDCI0WiUL774Qjp27FjuRaQOHjwos2fPFj8/P6teWCknJ0d69eolo0aNshSApUuXyl133SUixZsDRUSSk5Pl448/lk6dOknLli2la9eu8uWXX4qI9U7ecqMsJTeri9huTMyKiork9OnT0qdPH9m6datERkZaLlZkzpOenm7zcblRjpSUFMt/23JM9Hq9RERElPpi27x5s3z44Yfy4YcfypkzZ0SkeF356KOPbDomlWVZsGCBJCYmllreluNy4MAB6d27t+ULZM6cOTJ+/Hjp3r27LF++3HKCpI8//lg6duxoszGpLEuPHj1k1apVcvHiRcuythyTjIwMadiwodx///0iUvx5FxISIs8++2yp5c6fP2/T909Vc5jZ+jPlueeeK3UkycmTJ2XIkCGSnp5uuaBiWlqaLFy40ObrSkVZrl69KpcvXy617IEDB2w2LiaTSWbOnCnDhg2z3Jafny/9+/cXPz8/eeWVV0SkeF2x9edKbai+fJi/zL/99lvp379/qatmGo1GadWqlXz66adlfufTTz+VYcOGyTfffCMitR9s88W7Nm3aJEOGDJHTp09b7vvkk0+kTZs2MnjwYOnTp0+ZKzamp6db9lWarHBJ4qpm6du3r2zZskVEipuytcekZJaSjzVs2DDZs2eP/PLLLxIWFib33XefvPnmmzJlypRShcia41KdHNOmTZP09HTJycmx6ZisWrVKNBqNLFmyREREJkyYINHR0dKsWTNp27at+Pn5yY8//ljqd221rtwoS506deSXX34RkeKiYu1xMb+PRUS2b98uLVu2lJycHJk4caJER0fL008/LQ888ID4+vrKv/71r1JbHqw9JlXN4ufnJ88995xkZGTYbF0xZ3nggQdKFcP8/Hx57LHH5KabbpLU1FTLPvuSrDkuVc1x/e/YckyMRqOMGzdOHnroIct9n3/+ufj7+0uXLl0kMDBQnnvuuVJbh2y1rlQly4svvijnz5+XvLw8m36uFBQUyB133CFz584VEbFcWPGpp56S3r17i5ubm6xZs6bU71p7XKxB1eXjjz/+kJ49e4rRaJR9+/bJ22+/bdk0a34j9uzZU95///0yv3v16lXLXy21Hew//vhDevToYXmM/fv3W64zsmnTJtFoNPLMM8/IBx98INOnTxetVltqkpY1VTeLu7u7JUtWVpbVxsScpWfPnqVmXBcUFMiAAQNk2bJlIlK8C0qn04lGo7FcurqyTdr2yLF582bL79piTHr06GH5ec6cOeLp6SkxMTHSu3dv2bt3r1y5ckUuXLggkydPFp1OV2arg7XUJos1x+X612f37t0SHBwsW7dulbFjx0p8fLxl2aVLl4qHh4fNri1Rmyx6vd4m75+ioiLLVjDzY4uI7Nq1Szw8PGT16tWlfq8q1+mwRw4R23+mPPfcc+Lv7y+LFy+WefPmiVarlTfeeEN2795tuQ5JyYmV1lSbLLZYV0p+7o8aNUoGDhxYal1o3bq1bNmyRR577DFp0qSJZGRkqKJkVETV5SMnJ0duueUWiYuLE5PJZCkeJQf0zjvvLFU+vv/+e0lISLB6joEDB8ru3btLPb/BYJAnnnii1Mpv3hrz0ksvWTVDbbKUPOzKllnMhfDVV1+1vCavvPKKeHl5SXBwsGV3kLPmKJnFfI0RkeJD2YKCgspcX+Po0aPSoEEDm2yurmkW8/VibJHD/PqIiIwYMUICAwMlLCxMjh8/Xmr5/v37y4wZM6yeQ41ZBgwYIHv37i1zn/l9/dBDD0nfvn0tuxicOYc5y8CBAy1zJNLT02Xq1Kly++23S48ePcocqTFs2DAZMmSITQ4TrWkWa/9xVV6WtWvXSu/evaVXr14yf/58iYiIkAEDBohI8dErkZGRVv8etDZVn2TMaDQCANavXw+NRgMvLy/LDGKz/Px8GAwGAMDKlStxxx134NixY1bPISL4+uuvAfxzuWYvLy+89dZbmDRpkmWW85UrV1C/fn1ERUVZNUNtsnTo0MEuWcwnrgkKCsKRI0fw2muv4c0338T69euxefNm/PLLLxgyZIjT5iiZZfPmzZbbZs2ahU2bNlnWCfPr4+bmBn9/f9SvX181WUJCQmyWw/z6AMDMmTMRExODjIwMpKamllo+MDAQkZGRVs+hxiwajQZffvllmfvMn3H9+vXDsWPHkJycDACWI2+cMYc5S8nXp27duli0aBE2b96MyMhItG3bFgAsn/kBAQFo3769za4HVZMsHh4eNsuyceNGAMD999+Pp556CuHh4di+fTvuuece/PLLLwCKvxMLCwst35+qZfe6U00//vijBAYGWuYumJkbea9evWTBggWyefNm0Wg0ZfZ12TJHeZvTli5dKi1atCg1N8XVsvz0009Sv359qVu3rnz99deW23fs2CE//fSTU+eoKEt5Fi5cKO3bt5dTp045fRZzDvN8KJPJJF9++aV07txZgoKC5Ouvv5YdO3bI8uXLJTg4WLZv326THGrNUtnrc9NNN5U5csxZc5TM8v3335e6/bHHHpPo6GjJzs4WvV4va9askaCgILu8l9WUxbz72qzkHCYRkbffflu6detWrSsxK0H15SMvL0+mTJkiw4YNKzWPwvxFO3r0aOncubO4u7vLqlWrLPdZe19XRTnMzpw5I//973/F19dX/ve//1n1uR0tS2FhoTz66KMV7re3xX5IteSoKEtJp06dkv/85z/i6+srGzZssEkGtWUpmWPPnj0iUjz++/btk/Hjx0tQUJA0a9ZMWrZsaZNdP46Q5frXx7w//+2335bWrVtLcnKy0+eoLMuPP/4ovXr1Ei8vL+ndu7eEhoZadlna+72sdJaSu8jMr09sbKy888474uPjYzmqRc1UXz5Eis8PcdNNN8kDDzxQ5rDae+65RzQajWzcuFFEbDuTt6IcCQkJ8uabb0r79u3lq6++suSwJbVmKflhbm9qyXF9lpKvz5kzZ+TVV1+VZs2aWb7slVpX7J2lZI7rv+AOHjwoZ86cseyntvWMfLVmKfn6mL9UUlJS5K+//rLZ86stx/VZ4uLiRKT4dThx4oT897//lZUrV1q+gO35+qgpy/Xfhd9//71ERUXZ7XO/thyifIgUfyD06dNHhg8fXmoT2JdffmmZPGePQ4jKy2EymeT06dOWmfL2OpRJrVmuP9TYntcMUEuO67OUfH2OHj0qhw4dsvys5Lpi7yyVvT72ptYs12/id8Uc12cpeYSaiP2/WNWa5frX6Ny5c5ZMLB9WdPz4cXnwwQclMDBQZs+eLX///Xep++012NfnMLdhJag1yyuvvGLZ+uCqOa7P8vLLL6vm9VEyi1pfH2ZRV47yssTGxjJLOVlscbZqW3Oo8iFSvN/rm2++kX79+kmfPn3kwQcfLHXGSqVyTJkyRZEcas7St29fxbKoJcf1WdT0+iiZRa2vD7OoKwezOEaWmtCIXDvWzsHk5OQgOzsbhw8fRo8ePVCnTh2XzsEs6s7BLOrOwSzqzsEsjpGlOhy2fBAREZFjUvVJxoiIiMj5sHwQERGRXbF8EBERkV2xfBAREZFdsXwQERGRXbF8EBERkV2xfBAREZFdsXwQERGRXbF8EBERkV2xfBAREZFd/T8/BIh3Wb7+HgAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -286,12 +232,14 @@
    ],
    "source": [
     "plt.plot(moments.values())\n",
-    "plt.xticks(range(len(moments)), moments.keys(), rotation=45)"
+    "plt.scatter(range(len(moments)), moments.values())\n",
+    "plt.xticks(range(len(moments)), moments.keys(), rotation=45)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -300,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -513,7 +461,7 @@
        "75   95  0.321274   (90,95]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -524,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -539,31 +487,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axis.XTick at 0x7fb552537290>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb552536360>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb55252bbc0>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb552346900>,\n",
-       "  <matplotlib.axis.XTick at 0x7fb552347290>],\n",
-       " [Text(0, 0, '(70,75]'),\n",
-       "  Text(1, 0, '(75,80]'),\n",
-       "  Text(2, 0, '(80,85]'),\n",
-       "  Text(3, 0, '(85,90]'),\n",
-       "  Text(4, 0, '(90,95]')])"
+       "{'(70,75]': 0.37021211462000003,\n",
+       " '(75,80]': 0.33578140508,\n",
+       " '(80,85]': 0.32127446132,\n",
+       " '(85,90]': 0.32127446132,\n",
+       " '(90,95]': 0.32127446132}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "share_moments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
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Gzz//fAu8RdsxL9IfX/x8CTt/zcWi/DL06txB7khERERtxuL7tFgrW71Py/We2ngY35+6gsnB3vjnI4FyxyEiIrotrXafFpJf7N29AQA7jl3EpaJKmdMQERG1HZYWhRnm0wmhvdxRbRRYl5IhdxwiIqI2w9KiQLGja1dbkg5loaBMf4u9iYiIbANLiwKF93bHEC9XVFWb8O6PmXLHISIiahMsLQokSRLm/We1ZVPaBZRUVcuciIiIqPWxtCjUff090adLB5Tqa7B5/wW54xAREbU6lhaFUqkkzBvtDwDYkJqJSgO/EZuIiGwbS4uCjQvsDq9OjigsN2Db4axbv4CIiEjBWFoUzE6tQkxE7WrL2n0ZMNSYbvEKIiIi5WJpUbhHgrzQ2VmDS8VV+PR4jtxxiIiIWg1Li8Jp7dV4epQfAGD1nnQYTTbxrQxEREQNsLTYgKkhvnB1tEdGQTm+/i1P7jhEREStgqXFBnTQ2GFmWE8AQPzuc7CR78AkIiKqh6XFRswK7wknBzVO5JZgz5l8ueMQERG1OJYWG9HRyQHTRvoCABJ2n5M5DRERUctjabEhs+/0g4NahcPnr+FgRqHccYiIiFoUS4sN6eKixaRgLwBA/J50mdMQERG1LJYWGxN9lz/UKgn7zuTj14vFcschIiJqMSwtNsbH3QkPDukOAEjYw3NbiIjIdrC02KC5kbW39v/69zycu1IqcxoiIqKWwdJig/p6OiNqgCeEABL3ZMgdh4iIqEWwtNioeaN7AwA+PZ6D7KsVMqchIiK6fSwtNmqod0fc2dsDRpPAuhSuthARkfKxtNiweaNrz23ZejgbV0qrZE5DRER0e1habFhoL3fc4dMRhhoTNqSelzsOERHRbWFpsWGSJCE2svbcli0HLqC4olrmRERERM3H0mLj7g7ogoCuzijT12DT/vNyxyEiImo2lhYbp1JJ5iuJNvyYiQpDjcyJiIiImoelpR0YO7gbero74VpFNT44mCV3HCIiomZhaWkH1CoJMRG1VxKtS8mAvsYocyIiIiLLsbS0ExOG9UBXFy0ul+ix46ccueMQERFZjKWlndDYqfH0Xb0AAKv3pqPGaJI5ERERkWVYWtqRKSO80cnJHhcKK7Dz11y54xAREVmEpaUdcXKww5PhfgCAhN3pMJmEzImIiIiajqWlnXkitCc6aOxw+nIpfjh1Re44RERETcbS0s64Otlj2khfAMDbu89BCK62EBGRMrC0tENP3ekHjZ0Kx7OLsD+jUO44RERETcLS0g51dtZg8nBvALXnthARESkBS0s7NeeuXrBTSUg9V4Dj2UVyxyEiIrollpZ2yquTEx4a2gMAkLD7nMxpiIiIbo2lpR2bG+kPSQK+PXEZZy6Xyh2HiIjoplha2rHeXTpgzKCuALjaQkRE1o+lpZ2bF9kbAPD5z5eQVVghcxoiIqLGsbS0c4N6uCKib2eYBLB6H68kIiIi68XSQogdXbva8vGRi7hcUiVzGiIiohtjaSGM8HPD8J6dYDCa8E5KhtxxiIiIboilhQAA8/6z2vL+wSxcKzfInIaIiKghlhYCAET27YwB3VxQYTBiY9p5ueMQERE1wNJCAABJkszntmxMO48yfY3MiYiIiOpjaSGzPw3qil4eOhRXVuODgxfkjkNERFQPSwuZqVUSYiL9AQDrUjJRVW2UOREREdF/sbRQPeOH9kB3Vy3yS/X4+OhFueMQERGZsbRQPQ52Ksy5qxcAYPXedNQYTTInIiIiqsXSQg08NsIHHh0ccPFaJb745ZLccYiIiACwtNANaO3VePJOPwBAwu50mExC5kREREQsLdSIaSN94ay1w9krZfj2xGW54xAREbG00I25aO0xI7QnACBhzzkIwdUWIiKSF0sLNWpWeE9o7VX45WIxUs8VyB2HiIjauWaVloSEBPj5+UGr1SIoKAgpKSmN7puamorw8HC4u7vD0dERAQEBWLlyZb19Nm7cCEmSGjyqqviNw3Jy76DBlBE+AID43edkTkNERO2dnaUv2LZtGxYuXIiEhASEh4djzZo1GDNmDE6cOAEfH58G++t0OsyfPx+BgYHQ6XRITU1FdHQ0dDod5syZY97PxcUFp0+frvdarVbbjLdELenpUb2w5cAFHMi4iqMXriLI103uSERE1E5JwsKTFUJCQjBs2DAkJiaat/Xv3x/jx49HXFxck44xceJE6HQ6bN68GUDtSsvChQtRVFRkSZR6SkpK4OrqiuLiYri4uDT7ONTQ8x//gm1HsnFPQBesnzlc7jhERGRDLPn9bdHHQwaDAUePHkVUVFS97VFRUUhLS2vSMY4dO4a0tDRERETU215WVgZfX194eXnhgQcewLFjx256HL1ej5KSknoPah0xkf5QScD3p67gxCX+dyYiInlYVFoKCgpgNBrh6elZb7unpyfy8vJu+lovLy9oNBoEBwcjNjYWs2fPNj8XEBCAjRs34vPPP0dSUhK0Wi3Cw8Nx9uzZRo8XFxcHV1dX88Pb29uSt0IW8PPQ4c+DuwEAEvemy5yGiIjaq2adiCtJUr0/CyEabLteSkoKjhw5gtWrV2PVqlVISkoyPzdy5EhMmzYNQ4YMwahRo/Dhhx+ib9++eOuttxo93uLFi1FcXGx+ZGdnN+etUBPNi+wNANj5yyVkFpTLnIaIiNoji07E9fDwgFqtbrCqcuXKlQarL9fz86u9w+rgwYNx+fJlvPzyy5gyZcoN91WpVBg+fPhNV1o0Gg00Go0l8ek2DOjugrsDuuCHU1ewZm86lj8cKHckIiJqZyxaaXFwcEBQUBCSk5PrbU9OTkZYWFiTjyOEgF6vv+nzx48fR7du3SyJR60sdrQ/AGD7TxeRW1wpcxoiImpvLL7kedGiRZg+fTqCg4MRGhqKtWvXIisrCzExMQBqP7bJycnBpk2bAADx8fHw8fFBQEAAgNr7tqxYsQILFiwwH/OVV17ByJEj0adPH5SUlODf//43jh8/jvj4+JZ4j9RCgnzdMLKXGw5kXMXafRl4adxAuSMREVE7YnFpmTx5MgoLC7F06VLk5uZi0KBB2LVrF3x9fQEAubm5yMrKMu9vMpmwePFiZGZmws7ODv7+/li+fDmio6PN+xQVFWHOnDnIy8uDq6sr7rjjDuzbtw8jRoxogbdILSl2dG8cyDiEpENZmD+6N9w78CM6IiJqGxbfp8Va8T4tbUMIgYfif8QvF4sxf3Rv/PX+fnJHIiIiBWu1+7QQSZJkvpLovf3nUVJVLXMiIiJqL1hayGJRAzzRu0sHlFbVYMuBC3LHISKidoKlhSymUkmYF1l7JdH6lExUGowyJyIiovaApYWaZdyQ7vDq5IjCcgM+PMIb+xERUetjaaFmsVerEB1Ru9qyZm86DDUmmRMREZGtY2mhZpsU5AWPDhpcKq7CZ8dz5I5DREQ2jqWFmk1rr8bTo2q/niFxbzqMJpu4ep6IiKwUSwvdlsdH+sJFa4eM/HJ88/vNv+mbiIjodrC00G3poLHDzPDa1Zb43edgI/cqJCIiK8TSQrdtVlhPODmo8fulEuw9ky93HCIislEsLXTbOukc8HiID4Da1RYiIqLWwNJCLWL2qF5wUKtw+Pw1HMq8KnccIiKyQSwt1CI8XbR4JNgLAFdbiIiodbC0UIuJucsfKgnYeyYfv+UUyx2HiIhsDEsLtRgfdyc8OKQ7ACBhD1dbiIioZbG0UIuaG9kbAPDVb3k4d6VM5jRERGRLWFqoRfXr6oz7BnhCCGD13nS54xARkQ1haaEWNy+y9osUPz2Wg4vXKmROQ0REtoKlhVrcHT6dEN7bHTUmgXX7MuSOQ0RENoKlhVpF7H/Obdl6OBv5pXqZ0xARkS1gaaFWEervjqHeHaGvMWHDj5lyxyEiIhvA0kKtQpIkxI6uXW3ZvP8CiiurZU5ERERKx9JCreaegC7o5+mMMn0NNu8/L3ccIiJSOJYWajUqlYR5o2uvJFqfmokKQ43MiYiISMlYWqhVjR3cDb7uTrhWUY2kQ9lyxyEiIgVjaaFWZadWISaidrVl3b4M6GuMMiciIiKlYmmhVjdxWA94umiQV1KFT37KkTsOEREpFEsLtTqNnRpPj+oFAEjcm44ao0nmREREpEQsLdQmpozwQScne1worMCu3/LkjkNERArE0kJtQqexw6xwPwBAwu5zEELInIiIiJSGpYXazIzQntA5qHEqrxQ/nLoidxwiIlIYlhZqM65O9pgW6gsAeJurLUREZCGWFmpTT93pBwc7FY5lFeFAxlW54xARkYKwtFCb6uKsxeRgbwBAwp5zMqchIiIlYWmhNjfnrl5QqySknC3Az9lFcschIiKFYGmhNuft5oSHhnYHwNUWIiJqOpYWksW8SH9IEvDN75dx5nKp3HGIiEgBWFpIFr27OONPA7sCABL3pMuchoiIlIClhWQzL7I3AODzny8hq7BC5jRERGTtWFpINoO9XHFX384wmgTW7ONqCxER3RxLC8kqNtIfAPDRkYu4UlIlcxoiIrJmLC0kqxF+bgj27QSD0YR3UjPljkNERFaMpYVkJUkSYkfXntuy5cAFFFUYZE5ERETWiqWFZBfZrzP6d3NBhcGIjWnn5Y5DRERWiqWFZFe72lJ7bsu7P55Hmb5G5kRERGSNWFrIKowZ1A1+HjoUV1Yj6WCW3HGIiMgKsbSQVVCrJMyNqF1tWZeSgapqo8yJiIjI2rC0kNUYf0cPdHPV4kqpHtt/uih3HCIisjIsLWQ1HOxUmHNXLwDA6r3pqDGaZE5ERETWhKWFrMpjw33grnNA9tVKfPHLJbnjEBGRFWFpIavi6KDGk3f6AQASdqfDZBIyJyIiImvB0kJWZ3qoL5w1djh7pQzJJy/LHYeIiKwESwtZHRetPZ4I8wUAJOw+ByG42kJERCwtZKVmhftBa6/CzxeL8eO5QrnjEBGRFWBpIavk0UGDx4b7AADid5+TOQ0REVkDlhayWnPu6gU7lYT9GYU4euGa3HGIiEhmLC1ktbp3dMTEYT0AAIl7uNpCRNTesbSQVYuJ8IckAd+dvIKTuSVyxyEiIhk1q7QkJCTAz88PWq0WQUFBSElJaXTf1NRUhIeHw93dHY6OjggICMDKlSsb3X/r1q2QJAnjx49vTjSyMb06d8CfB3cDACTuSZc5DRERycni0rJt2zYsXLgQS5YswbFjxzBq1CiMGTMGWVk3/mZenU6H+fPnY9++fTh58iReeOEFvPDCC1i7dm2DfS9cuIC//vWvGDVqlOXvhGzWvMjaL1L88pdLOF9QLnMaIiKSiyQsvAlGSEgIhg0bhsTERPO2/v37Y/z48YiLi2vSMSZOnAidTofNmzebtxmNRkRERGDWrFlISUlBUVERPv300ybnKikpgaurK4qLi+Hi4tLk15EyzHr3EHafzseUEd6ImxgodxwiImohlvz+tmilxWAw4OjRo4iKiqq3PSoqCmlpaU06xrFjx5CWloaIiIh625cuXYrOnTvjqaeeatJx9Ho9SkpK6j3IdsWO7g0A+PjoReQVV8mchoiI5GBRaSkoKIDRaISnp2e97Z6ensjLy7vpa728vKDRaBAcHIzY2FjMnj3b/NyPP/6I9evXY926dU3OEhcXB1dXV/PD29vbkrdCChPc0w0j/NxQbRRYuy9D7jhERCSDZp2IK0lSvT8LIRpsu15KSgqOHDmC1atXY9WqVUhKSgIAlJaWYtq0aVi3bh08PDyanGHx4sUoLi42P7Kzsy1/I6Qo8/+z2pJ0KAuFZXqZ0xARUVuzs2RnDw8PqNXqBqsqV65cabD6cj0/v9pv7h08eDAuX76Ml19+GVOmTEF6ejrOnz+PcePGmfc1mUy14ezscPr0afj7+zc4nkajgUajsSQ+KdyoPh4Y3MMVv+YUY2Paefy/qH5yRyIiojZk0UqLg4MDgoKCkJycXG97cnIywsLCmnwcIQT0+tp/KQcEBODXX3/F8ePHzY8HH3wQo0ePxvHjx/mxD5lJkoTY0bUFdmPaeZRWVcuciIiI2pJFKy0AsGjRIkyfPh3BwcEIDQ3F2rVrkZWVhZiYGAC1H9vk5ORg06ZNAID4+Hj4+PggICAAQO19W1asWIEFCxYAALRaLQYNGlTv7+jYsSMANNhOFDWgK/w765CeX44tB7IwN7LhKhwREdkmi0vL5MmTUVhYiKVLlyI3NxeDBg3Crl274OvrCwDIzc2td88Wk8mExYsXIzMzE3Z2dvD398fy5csRHR3dcu+C2g2VSsK8yN74fx/9jPWpGZgV3hNae7XcsYiIqA1YfJ8Wa8X7tLQf1UYTIt/Yg5yiSix9aCCeCO0pdyQiImqmVrtPC5E1sFerEBPRCwCwZm8Gqo0mmRMREVFbYGkhRZoU7A2PDhrkFFXis+OX5I5DRERtgKWFFElrr8bsUbWX0SfsOQejySY+5SQioptgaSHFejzEBy5aO2Tkl+Pb329+R2YiIlI+lhZSLGetPWaG9QQAxO85Bxs5p5yIiBrB0kKKNjPcD472avyWU4J9ZwvkjkNERK2IpYUUzU3ngKkhPgCA+B/OyZyGiIhaE0sLKd7To3rBQa3CofNXcSjzqtxxiIiolbC0kOJ1ddXi4SAvALVXEhERkW1iaSGbEBPRCyoJ2HM6H7/lFMsdh4iIWgFLC9kEX3cdxg3pDgBI3JMucxoiImoNLC1kM+q+8XnXb7lIzy+TOQ0REbU0lhayGQFdXXBvf08IAazmagsRkc1haSGbMm907WrLJ8dykFNUKXMaIiJqSSwtZFOG+XRCmL87akwC6/ZlyB2HiIhaEEsL2ZzY0b0BAEmHslBQppc5DRERtRSWFrI5Yf7uGOLdEfoaEzakZsodh4iIWghLC9kcSZIQ+58riTbvv4DiymqZExERUUtgaSGbdG9/T/T17IBSfQ22HLggdxwiImoBLC1kk1QqCfMia89tWZ+aiQpDjcyJiIjodrG0kM16ILAbfNyccLXcgK2HsuWOQ0REt4mlhWyWnVqFmIjac1sS96Yjs6Bc5kRERHQ7WFrIpj0c1AO9OuuQX6rHw4lp+CnrmtyRiIiomVhayKZp7NTYNicUg3u44mq5AVPXHcC3v+fJHYuIiJqBpYVsXmdnDbbOGYnR/TqjqtqEmC1HsZlXFBERKQ5LC7ULOo0d1j0RjMeGe8MkgBc//Q3//PoUTCYhdzQiImoilhZqN+zUKsRNHIxF9/UFACTuSceiD4/DUGOSORkRETUFSwu1K5Ik4S/39MHrjwRCrZLw6fFLmPnuIZRU8a65RETWjqWF2qVHg72xYeZw6BzUSEsvxKOr9yO3uFLuWEREdBMsLdRuRfTtjG3RoejsrMGpvFJMTEjD6bxSuWMREVEjWFqoXRvUwxU75obBv7MOucVVeGR1GtLSC+SORUREN8DSQu2et5sTts8Nw/CenVBaVYMZGw7hs+M5csciIqLrsLQQAejo5IDNT4VgzKCuqDYKPLP1ONbsTYcQvCSaiMhasLQQ/YfWXo34qcPwZLgfACDuq1N4+fPfYeS9XIiIrAJLC9EfqFQS/nfcALwwtj8kCXhv/wXM3XIUVdVGuaMREbV7LC1ENzB7VC+8PWUYHNQqfHviMqauO4Cr5Qa5YxERtWssLUSNGBvYDZufGgEXrR1+yirCI4lpyCqskDsWEVG7xdJCdBMhvdyxfW4YenR0REZBOSYm/ohfLhbJHYuIqF1iaSG6hT6eztgxLwz9u7mgoMyAyWsOYPepK3LHIiJqd1haiJrA00WLD6NHYlQfD1RWGzF70xFsPZQldywionaFpYWoiZy19tgwczgeHuYFo0ngbzt+xb+Sz/BeLkREbYSlhcgC9moVVkwKxF/u7g0A+Pf3Z/E/H/+CaqNJ5mRERLaPpYXIQpIkYVFUPyybMBgqCfj46EU8ufEwyvQ1ckcjIrJpLC1EzTQ1xAfrngiGo70aKWcLMHnNflwpqZI7FhGRzWJpIboN9/T3xNY5I+Guc8Dvl0owISEN566Uyh2LiMgmsbQQ3aYh3h2xY14Y/Dx0yCmqxMOJ+3Eo86rcsYiIbA5LC1EL8HXXYfvcMNzh0xHFldWYtv4gdv2aK3csIiKbwtJC1ELcdA74YPZI3DfAE4YaE2I/+AnrUzPljkVEZDNYWohakKODGqunBWH6SF8IAbz65Qm8+uUJmEy8lwsR0e1iaSFqYWqVhKUPDcTzfwoAAKxPzcSCpGOoqjbKnIyISNlYWohagSRJmBvpj1WTh8JeLWHnr7l4Yv0hFFUY5I5GRKRYLC1ErWj8HT3w3qwRcNbY4dD5q3hk9X5cvFYhdywiIkViaSFqZWG9PfDR3FB0ddHi3JUyTEhIw285xXLHIiJSHJYWojYQ0NUFn8SGoZ+nM/JL9Zi8Zj/2ncmXOxYRkaKwtBC1kW6ujvgwJhShvdxRbjDiyY2H8fHRi3LHIiJSDJYWojbk6miPjU8Ox0NDu6PGJPDXj37GW9+fhRC8JJqI6FZYWojamMZOjZWPDkVMhD8A4M3kM/j7J7+ixmiSORkRkXVrVmlJSEiAn58ftFotgoKCkJKS0ui+qampCA8Ph7u7OxwdHREQEICVK1fW22fHjh0IDg5Gx44dodPpMHToUGzevLk50YgUQaWS8LcxAVj60EBIEpB0KBtzNh9FhaFG7mhERFbLztIXbNu2DQsXLkRCQgLCw8OxZs0ajBkzBidOnICPj0+D/XU6HebPn4/AwEDodDqkpqYiOjoaOp0Oc+bMAQC4ublhyZIlCAgIgIODA7788kvMmjULXbp0wf3333/775LISj0R2hOeLlr8JekYfjh1BY+tPYD1M4ajs7NG7mhERFZHEhZ+mB4SEoJhw4YhMTHRvK1///4YP3484uLimnSMiRMnQqfT3XQ1ZdiwYRg7dixeffXVJh2zpKQErq6uKC4uhouLS5NeQ2Qtjl64htnvHca1imr4uDlh46zh6NW5g9yxiIhanSW/vy36eMhgMODo0aOIioqqtz0qKgppaWlNOsaxY8eQlpaGiIiIGz4vhMD333+P06dP46677rIkHpFiBfl2wva5YfB2c0TW1Qo8nJiGoxeuyR2LiMiqWFRaCgoKYDQa4enpWW+7p6cn8vLybvpaLy8vaDQaBAcHIzY2FrNnz673fHFxMTp06AAHBweMHTsWb731Fu67775Gj6fX61FSUlLvQaRkvTp3wI654Qj0csW1impMXXcA3/x+83lFRNSeNOtEXEmS6v1ZCNFg2/VSUlJw5MgRrF69GqtWrUJSUlK9552dnXH8+HEcPnwY//jHP7Bo0SLs2bOn0ePFxcXB1dXV/PD29m7OWyGyKp2dNdg6ZyTuDugCfY0Jc7ccxab95+WORURkFSw6p8VgMMDJyQkfffQRJkyYYN7+zDPP4Pjx49i7d2+TjvPaa69h8+bNOH36dKP7zJ49G9nZ2fjmm29u+Lxer4derzf/uaSkBN7e3jynhWxCjdGEFz/7DUmHsgEAMRH+eO7+flCpbv6PAyIipWm1c1ocHBwQFBSE5OTketuTk5MRFhbW5OMIIeoVjubso9Fo4OLiUu9BZCvs1CosmzAY/+++vgCA1XvT8eyHx6GvMcqcjIhIPhZf8rxo0SJMnz4dwcHBCA0Nxdq1a5GVlYWYmBgAwOLFi5GTk4NNmzYBAOLj4+Hj44OAgAAAtfdtWbFiBRYsWGA+ZlxcHIKDg+Hv7w+DwYBdu3Zh06ZN9a5QImpvJEnCgnv6oKurFot3/IrPjl/ClRI9Vk8PgqujvdzxiIjanMWlZfLkySgsLMTSpUuRm5uLQYMGYdeuXfD19QUA5ObmIisry7y/yWTC4sWLkZmZCTs7O/j7+2P58uWIjo4271NeXo558+bh4sWL5hvQbdmyBZMnT26Bt0ikbJOCveHposXcLUexP6MQj67ej3dnDUf3jo5yRyMialMW36fFWvE+LWTrfr9UjJnvHkZ+qR5dXbTY+ORwBHTl/+tEpGytdk4LEclnYHdXfDIvDL27dEBeSRUmJe5H2rkCuWMREbUZlhYiBfHq5ISPY0IxoqcbSvU1mPHuIXx2PEfuWEREbYKlhUhhOjo5YNNTIzB2cDdUGwWe2XociXvSYSOf9BIRNYqlhUiBtPZqvDXlDjx1px8A4J9fn8L/fvY7jCYWFyKyXSwtRAqlUkl48YEBeGFsf0gSsPnABcRsOYpKA+/lQkS2iaWFSOFmj+qFt6cMg4OdCsknLmPqOwdwtdwgdywiohbH0kJkA8YGdsOWp0Lg6miPY1lFeDgxDRcKy+WORUTUolhaiGzECD83bJ8bih4dHZFZUI6JCWn4ObtI7lhERC2GpYXIhvTu4oxP5oVhYHcXFJYb8NjaA/jh1GW5YxERtQiWFiIb08VFi23Robirb2dUVhsx+70j+OBg1q1fSERk5VhaiGxQB40d1s8IxiNBXjAJ4O+f/Io3vz3Ne7kQkaKxtBDZKHu1Cm88Eoi/3NMHAPDWD+fw149+QbXRJHMyIqLmYWkhsmGSJGHRfX2xfOJgqFUStv90EU9uPIzSqmq5oxERWYylhagdeGyED955IhiO9mqknC3A5DUHcLmkSu5YREQWYWkhaidGB3TBtuiR8OjggBO5JZiYkIazl0vljkVE1GQsLUTtSKBXR+yYGw4/Dx1yiirxcGIaDmYUyh2LiKhJWFqI2hkfdydsnxuGYT4dUVJVg+nrD2HnL7lyxyIiuiWWFqJ2yE3ngA+eHomoAZ4wGE2I/eAnvJOSIXcsIqKbYmkhaqe09mokTgvCE6G+AIDXdp7E0i9OwGTivVyIyDqxtBC1Y2qVhFceHIjFYwIAABt+zMT8pJ9QVW2UORkRUUMsLUTtnCRJiI7wx/89NhT2agm7fs3D9PUHUVRhkDsaEVE9LC1EBAB4aGgPvPfkCDhr7XD4/DU8nJiG7KsVcsciIjJjaSEiszB/D3wUE4purlqk55djYmIafsspljsWEREAlhYiuk5AVxfsmBeGgK7OyC/VY/Ka/dh7Jl/uWERELC1E1FA3V0d8GBOKMH93lBuMeHLjYXx4JFvuWETUzrG0ENENuWjtsXHWCIwf2h1Gk8BzH/+C//vuLITgJdFEJA+WFiJqlIOdCv96dCjmRvoDAFZ+dwaLd/yKGqNJ5mRE1B6xtBDRTalUEp7/UwBefWggVBKw9XA2nt50BOX6GrmjEVE7w9JCRE0yPbQnVk8LgtZehd2n8/HY2gPIL9XLHYuI2hGWFiJqsqiBXfHB0yPhpnPArznFmJj4I9Lzy+SORUTtBEsLEVlkmE8nbJ8bBl93J2RfrcQjiWk4euGq3LGIqB1gaSEii/l56LB9bhiGeLniWkU1pq47iK9/y5M7FhHZOJYWImoWjw4aJM0ZiXsCukBfY8Lc94/ivbTzcsciIhvG0kJEzebkYIc104MwZYQPhABe+vx3xO06CZOJ93IhopbH0kJEt8VOrcKyCYPwP/f3AwCs2ZeBhduOQ19jlDkZEdkalhYium2SJCF2dG+8OWkI7FQSPv/5EmZsOITiymq5oxGRDWFpIaIW83CQF96dNRwdNHY4kHEVk1an4VJRpdyxiMhGsLQQUYsa1acztkWPRBdnDc5cLsOEhB9xMrdE7lhEZANYWoioxQ3s7opPYsPRp0sHXC7R49HV+/HjuQK5YxGRwrG0EFGr6NHRER/HhGGEnxtK9TWY+e4hfHLsotyxiEjBWFqIqNW4Otlj05MjMDawG6qNAs9u+xnxu89BCF4STUSWY2kholaltVfjrcfuwNOj/AAAb3xzGi9+9huMvJcLEVmIpYWIWp1KJWHJ2AH43wcGQJKALQeyEL35KCoNvJcLETUdSwsRtZkn7/RDwtRhcLBT4buTlzFl3QEUlunljkVECsHSQkRtaszgbnh/dghcHe1xPLsIDyem4UJhudyxiEgBWFqIqM0N7+mG7XPD0KOjI84XVmBiQhqOZxfJHYuIrBxLCxHJoneXDvgkNgwDu7ugsNyAx9bux3cnLssdi4isGEsLEcmmi7MW26JDcVffzqiqNmHO5iN4/+AFuWMRkZViaSEiWXXQ2GH9jGBMCvKCSQBLPvkNb3xzivdyIaIGWFqISHb2ahVefyQQz9zTBwAQvzsd/++jn2GoMcmcjIisCUsLEVkFSZLw7H198c+HB0OtkrDjpxw8ufEwSquq5Y5GRFaCpYWIrMrk4T54Z0YwnBzUSD1XgEmr9yOvuEruWERkBSRhIx8cl5SUwNXVFcXFxXBxcZE7DhHdpl8vFmPWxsMoKNOju6sWT97pB0mS5I5F1K6NC+yGLi7aFj2mJb+/7Vr0byYiaiGDvVzxybwwzNhwCBkF5Xht50m5IxG1e3f4dGzx0mIJlhYislrebk7YPjcMCXvO4Uopb/dPJLdOTg6y/v0sLURk1TrpHLBk7AC5YxCRFeCJuERERKQILC1ERESkCM0qLQkJCfDz84NWq0VQUBBSUlIa3Tc1NRXh4eFwd3eHo6MjAgICsHLlynr7rFu3DqNGjUKnTp3QqVMn3HvvvTh06FBzohEREZGNsri0bNu2DQsXLsSSJUtw7NgxjBo1CmPGjEFWVtYN99fpdJg/fz727duHkydP4oUXXsALL7yAtWvXmvfZs2cPpkyZgt27d2P//v3w8fFBVFQUcnJymv/OiIiIyKZYfJ+WkJAQDBs2DImJieZt/fv3x/jx4xEXF9ekY0ycOBE6nQ6bN2++4fNGoxGdOnXC22+/jSeeeKJJx+R9WoiIiJTHkt/fFq20GAwGHD16FFFRUfW2R0VFIS0trUnHOHbsGNLS0hAREdHoPhUVFaiuroabm1uj++j1epSUlNR7EBERke2yqLQUFBTAaDTC09Oz3nZPT0/k5eXd9LVeXl7QaDQIDg5GbGwsZs+e3ei+f/vb39CjRw/ce++9je4TFxcHV1dX88Pb29uSt0JEREQK06wTca+/lbYQ4pa3105JScGRI0ewevVqrFq1CklJSTfc7/XXX0dSUhJ27NgBrbbxu+4tXrwYxcXF5kd2drblb4SIiIgUw6Kby3l4eECtVjdYVbly5UqD1Zfr+fn5AQAGDx6My5cv4+WXX8aUKVPq7bNixQosW7YM3333HQIDA296PI1GA41GY0l8IiIiUjCLVlocHBwQFBSE5OTketuTk5MRFhbW5OMIIaDX178l9xtvvIFXX30VX3/9NYKDgy2JRURERO2AxbfxX7RoEaZPn47g4GCEhoZi7dq1yMrKQkxMDIDaj21ycnKwadMmAEB8fDx8fHwQEBAAoPa+LStWrMCCBQvMx3z99dfx4osv4oMPPkDPnj3NKzkdOnRAhw4dbvtNEhERkfJZXFomT56MwsJCLF26FLm5uRg0aBB27doFX19fAEBubm69e7aYTCYsXrwYmZmZsLOzg7+/P5YvX47o6GjzPgkJCTAYDHjkkUfq/V0vvfQSXn755Wa+NSIiIrIlFt+nxVrxPi1ERETKY8nvb5v5lue67sX7tRARESlH3e/tpqyh2ExpKS0tBQDer4WIiEiBSktL4erqetN9bObjIZPJhEuXLsHZ2fmW94yxRElJCby9vZGdnc2PnRSKY6h8HENl4/gpX2uOoRACpaWl6N69O1Sqm1/UbDMrLSqVCl5eXq12fBcXF042heMYKh/HUNk4fsrXWmN4qxWWOs26Iy4RERFRW2NpISIiIkVgabkFjUaDl156iV8ZoGAcQ+XjGCobx0/5rGUMbeZEXCIiIrJtXGkhIiIiRWBpISIiIkVgaSEiIiJFYGkhIiIiRWBpISIiIkVgaSEiIiJFYGlpYXv27MHu3bvljkG3gWOobBw/5eMYKl9rjSFLSwuKj4/HvffeiyVLluCHH36QOw41A8dQ2Th+yscxVL7WHEPeXK6FXLx4EVFRURg3bhwyMjJQVFSEv/3tb7jnnnvkjkZNxDFUNo6f8nEMla+1x5ClpYVUV1ejqKgInTt3RnJyMv7973+jqqqKE05BOIbKxvFTPo6h8rX2GLK03AYhBCRJMv/ZZDJBpar9xO3777/HqlWrOOEU4I/jyDFUFs5B28A5qGxtOQ95TkszGY1GSJKEiooK7Nu3D0IIqFQq1HXAe+65BwsXLoRWq8Xy5cvx/fffy5yYrmcymQCg3mRTqVSorq4GwDG0dpyDysc5qHxtPQ9ZWprBaDRCrVajpKQEPj4+2Lt3r3nSSZLEH5oKUPcvgbKyMrz44otYuHAhlixZgsuXL8Pe3t68H8fQOnEOKh/noPLJMQ/58ZCF/jhIgYGBGDp0KD799NMG+xkMBjg4OADgEqe1KisrQ2BgIPz8/KBSqVBQUIDz588jPj4e48aNg7Ozs3lfjqH14By0HZyDyiXbPBRksZKSEuHv7y/Gjx9v3vbDDz+ILVu2iA8++EBUVVUJIYSoqakxP//tt9+KBx54QNx///3im2++afPM1NBzzz0nRo0aJYQQorq6WtTU1Ii5c+cKnU4nVq9eLcrLy+vtzzG0HpyDtoFzUNnkmIcsLc3wz3/+U0iSJL766ishhBAzZswQw4YNE126dBG+vr7C19dXnD17VgghhMFgML/uxIkTonfv3uLPf/6zuHr1qizZ6b/mzJkjpk6dKoSoP6kWLVokdDqdeXw5htbn9ddf5xy0AZyDyibHPGRpaYI/TqY6M2bMEG5ubiIyMlKMGDFC7N+/X2RnZ4tTp06Je++9V/Tu3VtUVFQIIYQwmUxCCCE+++wzIUmS2LlzZ5vmpxv7y1/+Ivz8/Mx/rvtXgRBCTJs2Tfj4+Jj/pccxtD5PPPEE56DCPfPMM5yDCtfWvwtZWm6hrrCUlZWJrVu31ntuxowZwtXVVRw8eLDe9v379wsPDw/x7bff1jvOd999J77++uvWD031GI3GG24/efKkGDBggIiJiTFPprofmmfPnhXe3t71xotjKI/Gxk+I2uLCOWj9GhvDEydOcA4qhLXMQ7vbPBfHptWdaFRaWorAwEBcuHABgwcPxoABAwAAGzduxM6dO+Hv7w/gv9eqq9VqODo6wt3d3XwstVqNu+++u96lfdT66sawvLwcb731Fi5cuICRI0fi0UcfhZ+fH2bMmIEPP/wQf/vb3xAXFweNRgMAcHR0hL29vfleAwDHUA6Njd9DDz2Ejh074r333uMctHJ1Y1hZWYl9+/bBYDDAx8cHQ4YMQZ8+ffDkk0/i/fff5xy0YjcaQ19fXwQGBgIA3nvvPXzxxRdtMg9ZWhrxxzOjBw4ciJ49e6Jbt27Ytm0bXnnlFfMZ0WPHjjW/pm4QDh06hM6dO8PDw6PeMTnR2pYQwlw6Q0JC4Obmhg4dOmDNmjUoLy/HvHnzMGfOHBQVFeGbb77BhQsX8M4776C8vBzff/89ysvL0bVr13rH5Bi2nZuNX0JCAmJiYgCAc9CK/XEMw8LC4ODggOrqapw+fRoLFy7E/PnzERsbi+LiYnz55Zecg1boZmO4aNEiTJ06FYMHD8a4cePMr2nVedis9RkbV7dMWVRUJPz8/MSjjz4qhBBiwYIFwtfX17x8WbdfnQsXLoj4+Hjh5OQktm/f3rah6YYMBoP405/+JB5//HHziWDPPfecmDFjRr2P/jZs2CAGDhwonJycxKBBg4S7u7tISkqSMzqJm4/fjXAOWh+9Xi8iIiLE1KlTRVFRkSgpKRHr168XkiSJyZMni5MnT4rKykrxzjvviEGDBnEOWqGbjeHUqVMbfCzUmvOQpaURVVVVokuXLvUu5Tp//rzw8fERy5Yta7D/2bNnxZIlS4SXl5f4+OOPhRANSw21vStXrojhw4eLzz77zLzt73//uxg/fryYNGmSePXVV8WxY8eEELUTc+vWrSI5Odm8jWMor1uN37Jly0RaWpoQQoj09HTOQSt04cIFERISIo4cOSKEqD2nITc3V/j7+4vOnTuLGTNmiMrKSlFdXS0qKirEtm3bOAetzM3GsEuXLmLWrFmisLBQCCHEmTNnWnUesrQ0Ij8/X2zevLnetrKyMvHoo4+K++67r8FJSVVVVWL//v3i559/FkLUDhInm/zS09OFk5OT+Ne//iUuX74sPvnkE2FnZydmzJgh5s+fL/r16ycmTpwoLl26JHdUuoFbjV9AQIB45JFHRH5+vqisrOQctEI//fSTkCRJ/Pjjj+ZtZWVl4qGHHjJfMvvBBx/ImJBupSljWHehSmvPQ5aWJvjjf/T9+/cLSZLEhx9+KHMqaqq6++rce++9wsnJSaxcudL83MGDB4WdnR0vn7RiTRm/L774Qr6AdFMmk0k8/PDDYujQoeKrr74SP/30kxgwYIAYM2aMEEKIefPmiYkTJwqDwcCSaaWaOoZ/vGS9tfC7h5pAkiTz9ygMHToUDz30ED766COUl5ebv1uBrE/d2Dz33HM4fvw4EhMTMXDgQNx9992oqamByWRCr169MHDgQJ7cZ4UsGT+1Wi1zWmqMJEmIjY1Fnz59MG7cOEycOBEDBgzAzp07AdR+B1FBQQHs7e05D61UU8ew7sqv1sSrhywgSRK0Wi3+9Kc/4X/+53+QlZWF/v37yx2LGlFXNCVJQmBgILKysnDx4kUYjUbY2dX+r//ll1/i6tWr8PX1lTktXY/jZztGjx6Nu+66CydOnIBKpcLAgQPNz2m1WgwdOhQmk8n8D0SyPtYyhu36CxPrfiD+Ud03j95q/8DAQAwcOBBJSUmtnpMaV3dp+o3G8kYef/xxfP7553jyySdRVVWFDz/8EGvXrsWkSZPaIC1dz9I5yPGzPpbOwTqFhYX4/PPPMX/+fHzyySeIiopqxZR0M5bOwzpyjGG7/XjIaDRCkiTU1NSgsLAQxcXFAACVStXoRz51g2oymTBhwgRER0e3WV5qqO6HZXFxMV544QXk5+ff8jXLli3D/PnzkZaWhsrKSmzbtg2TJk3ix3wyaM4c5PhZl+bMQQAoKCjAa6+9hhdeeAEbNmxAVFQUx1AmzZmHgHxj2C5XWuoaZElJCR577DFkZ2fDwcEB/fr1Q0JCAjp27HjLY9Q1U0v/dUEt4483/+vXrx+Cg4PxxRdfNLr/9eNUUVEBBwcH2NnZmScax7HtWDoHOX7Wx9I5eL0jR45ACIHhw4dzDGVyu78L5RjDdllaAKCyshIjRoxAv379MHnyZGRnZ+O9995DWVkZkpKSMGLECPO+dQOr1+vb5EQjurm6X2AlJSUIDAxEcHAwPv744xvuW/eDtW4Mq6qqoNVq2zgx3Uhz5iDHzzpwDtoOxc3DVr02yYqlpKSI/v37i/Pnz5u3FRYWivvuu094eXmZrzGvu2vqoUOHxMMPP8xL8qxEVVWV6N69u7j77rvN2/71r3+JWbNmiQkTJoiXXnrJvJ1jaJ04B5WNc9A2KG0etttzWvLz85GVlQVPT08AQE1NDdzc3PDll1+iT58+mDJlivk7F+qe37FjBzZu3ChjaqpTUVEBDw8PFBcXIz8/H0888QTWr18PtVoNtVqN999/HxEREQDAMbRSnIPKxjloG5Q2D9tFaTEajQBql7bqREZGwt3dHa+99hoAwM7ODkajEQ4ODnjnnXdQU1ODN954A0DtUmhoaCheffVV5Ofnm49Hbef6MezUqRO+//57qFQqeHp6IiMjA9u3b8e6devw0UcfISEhAVlZWVi5ciUAjqHcxA0+hY6MjISbmxvnoEJcP4Z1c1CSJM5BBakbx7qfpUqbhzZfWuo+Ty0tLUVkZCR++uknAIBGo8Hjjz+OvXv34r333gMA82V7vr6+8PHxQXp6OoD/nlh0//33Y/r06byRVRtrbAw9PDywc+dOTJs2DTNnzkS/fv3MEzIyMhKurq4cQyvwx6sTSktLzVcnaLVaTJs2jXNQAa4fw6KiIgC1c3DXrl2cgwpQV1KuP1HWwcEB06ZNw+7duxUxD2365nJ/PLs9NDQUJ0+exNq1a7Fq1So4OTkhOjoav//+O959911UV1dj9uzZkCQJarUaPj4+0Gg0EELAZDJBrVYjODhY7rfU7jQ2hv/3f/8HBwcHdO7cGW+//Xa9M9frbnDk6+uLvn37AvjvCWQcw7ZVN3dKS0sxbdo05Ofn4+zZs1i/fj0efPBBzJ49m3PQyjU2hhs2bMC4ceM4BxWg7r99WVkZXn31VRQXF0OtVuOpp57CsGHDEBsbixMnTmD9+vVWPw9tdqXlj7/sBg4ciEGDBuHFF1/Ed999h6qqKgCAr68v3nzzTXh6eiIhIQEzZ87E559/jn/84x/YsmULxo0bZx44ans3G8PKykrzJecuLi5wdXU1v06lUmHLli04ePAgQkJCzNuo7dX9oBw+fDgcHBwQExODRx55BDNnzkRBQQF69eqF119/HZ6enoiPj+cctEKNjeGMGTNw9epVAICzszPnoBWrG8Nhw4bh1KlTqKmpQXZ2NkaMGIF//etf8PT0xL///W9069bN+n8XtuVZv22tqKhIdO/eXUyaNEkIUfvtk126dBHPPfdcvf1ycnJEQkKCGDJkiOjTp48IDg4W27dvF0Lwa9Hl1tQxrPPzzz+Ll156Seh0On6ppZV4/vnn611hcvbsWXH//feLq1evisuXLwshar9VPTExUQQGBnIOWqHGxvDatWuioKCg3r7Hjx/nHLQyJpNJxMbGigceeMC8Ta/Xi8jISKHT6cTLL78shKj9XRgfH2/VvwttsrRUVFQIIYSYOXOmePDBB83b9Xq9WLBggRg1apS4cuWKMJlM5su46ly9elUUFRUJIfjV9nJq6hhe/5o1a9aIBx54QHz22WdCCOuZaO1N3fgZjUbx+OOPi9mzZ5ufe//994Wzs7MICgoSHTt2FM8//7y4du2a+XnOQetgyRj+/e9/Fzk5OaKyspJz0IqUl5cLIYQwGAxi7Nix4rXXXhNC1P7jTwghnn32WREWFiZUKpX44IMP6r3WWuehzZWW1NRUMXLkSFFTUyMyMjLM2+v+gx84cEDY29uLLVu21Hud0Whs05zUuOaOoRBClJaWiry8PPP+1jLR2pO68av7b//8888LZ2dnsW7dOhEXFyfUarVYtmyZOHjwoNiwYYNwcnISGzdulDk1/dHtjGFJSQnnoBVITU0VISEh5v/+kydPFvfcc0+933X9+vUTX331lViwYIHw8fERRUVFVj9eNldaysvLxejRo8WRI0caPFc3GLNnzxZ33nmneWmarAvHUNnKy8vFPffcIw4cOCCEqP0X29NPPy3+/Oc/i5CQEDFnzpx6+z/wwAPi/vvvb7DqSfJp7hgaDAY54tINXD+GSUlJIiwsTISGhop//vOfomfPnmL06NFCCCEOHz4s/Pz8RGZmpoyJm8bmzoyquzRv+/btDZ6ru1zrrrvuwqlTp5CdnQ2g/v1bSH4cQ2UzGo0QQuDTTz8FUHs/j7Vr1+LLL7+En58f+vfvDwDmE+JdXFwwcOBA6zjJjwA0fwzt7e3likzXqRvDTz75BAAwadIkPPvss/D29sbevXsxYcIE/PDDDwAAvV6P6upqZdw7R+bS1CqSk5NFx44dxVdffdXoPqNGjRL3339/G6YiS3AMla1u/Hbt2lVv+4IFC8Qdd9whysrKRElJifjggw+Em5ub+O6772RKSo3hGCpf3Rh+8cUX9bbXna9U54033hDDhw9vcJ6gNbLJ0lJZWSmeeuop8cADD4ijR4/We67u87w33nhD9OvXT2RnZ8sRkW6BY6hsjY1fcnKyCA0NFRqNRoSFhYmuXbuarzCx9s/S2xuOofL9cQz/+HF73c/QQ4cOiRUrVghHR0fzVULWzma/5fns2bN46qmn4O/vj2eeeQZDhw4F8N+b7OTl5SEzMxOhoaHyBqVGcQyV7Y/j95e//AV33HEHhBA4d+4cvvrqK3Tq1AkDBgxAUFBQm32tPVmGY6h8jf0cBYCvvvoKzz33HJYuXYoJEyaYv73bmtlsaQGAX375BfPmzYObmxvmzp2LMWPGyB2JLMQxVLY/jl90dDTGjh1rfk4JPyCJY2gLbvZzNCcnBz169FBM6bS5E3H/KDAwEBs2bEDnzp0xdepUvPLKKzhy5IjcscgCHENl++P4TZs2Da+88goOHz4MwPp/OFItjqHy3ejn6MGDBwEAPXr0AFA7lkoYT5teaalTVVWFb7/9Fm+++SZMJhP69euH1157DV27dpU7GjURx1DZOH7KxzFUPlsYw3ZRWuqUl5ejrKwMv/32G0JCQtChQwe5I5GFOIbKxvFTPo6h8il5DNtVaSEiIiLlsulzWoiIiMh2sLQQERGRIrC0EBERkSKwtBAREZEisLQQERGRIrC0EBERkSKwtBAREZEisLQQERGRIrC0EBERkSKwtBAREZEi/H95E//WRMC3kQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -574,7 +527,156 @@
    ],
    "source": [
     "plt.plot(share_moments.values())\n",
-    "plt.xticks(range(len(share_moments)), share_moments.keys(), rotation=45)"
+    "plt.xticks(range(len(share_moments)), share_moments.keys(), rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_763100/3227000379.py:5: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  temp = scf_data_full.groupby([\"age_group\", \"wave\"]).apply(weighted_median, var=\"wealth_income_ratio\", weights=\"weight\").reset_index()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age_group</th>\n",
+       "      <th>wave</th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>(25,30]</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>0.829978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>(25,30]</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>0.463712</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>(25,30]</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>0.992973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>(25,30]</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.759365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>(25,30]</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>0.753445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>(90,95]</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>8.989320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>(90,95]</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>13.095266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>(90,95]</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>2.550180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>(90,95]</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>15.249137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>(90,95]</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>14.999761</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>126 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    age_group  wave          0\n",
+       "0     (25,30]  1995   0.829978\n",
+       "1     (25,30]  1998   0.463712\n",
+       "2     (25,30]  2001   0.992973\n",
+       "3     (25,30]  2004   0.759365\n",
+       "4     (25,30]  2007   0.753445\n",
+       "..        ...   ...        ...\n",
+       "121   (90,95]  2007   8.989320\n",
+       "122   (90,95]  2010  13.095266\n",
+       "123   (90,95]  2013   2.550180\n",
+       "124   (90,95]  2016  15.249137\n",
+       "125   (90,95]  2019  14.999761\n",
+       "\n",
+       "[126 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def weighted_median(data, var, weights):\n",
+    "    dsw = DescrStatsW(data[var], weights=data[weights])\n",
+    "    return dsw.quantile(0.5, return_pandas=False)[0]\n",
+    "\n",
+    "\n",
+    "temp = (\n",
+    "    scf_data_full.groupby([\"age_group\", \"wave\"])\n",
+    "    .apply(weighted_median, var=\"wealth_income_ratio\", weights=\"weight\")\n",
+    "    .reset_index()\n",
+    ")\n",
+    "\n",
+    "temp"
    ]
   },
   {
diff --git a/code/notebooks/WarmGlowPortfolio.ipynb b/code/notebooks/WarmGlowPortfolio.ipynb
index 444359f..c6ef760 100644
--- a/code/notebooks/WarmGlowPortfolio.ipynb
+++ b/code/notebooks/WarmGlowPortfolio.ipynb
@@ -33,7 +33,7 @@
                 {
                     "data": {
                         "text/plain": [
-                            "(4.705614650734349, 46.46325681615148, 16.964074896995744)"
+                            "(4.705676392381167, 46.465318252398745, 16.966223080560113)"
                         ]
                     },
                     "execution_count": 3,
@@ -71,7 +71,7 @@
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -91,7 +91,7 @@
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -150,7 +150,7 @@
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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odu7cCcDZs2eJiIjIl2wUpnHjxtStW5f58+dbts2fPx8vLy86d+5s2ZaZmcmECRMs/abyPueSkpKK/TqIkiN9VATNmjWjbt26zJ07l7fffpu5c+eiKEq+X/QfffQR48ePZ9SoUbz//vt4eHig1WoZN25coZ0+i1KbYjQa6datG4mJibzxxhvUrVsXJycnLl++zIgRIwo9bnHlHePxxx8v0D6dp1GjRkU+XqtWrQgJCWHcuHFERUUxZMiQ25731VdfveUv+lq1ahX5vLcye/ZsRowYQd++fXnttdfw8fFBp9MxZcoUzp49W2D/vMTzv27+hTpu3Dj69OnD0qVLWbNmDePHj2fKlCls3LiRpk2b3vK52NjYcOTIkXt+ToUpStzWqiixP/bYY/zzzz+89tprNGnSBGdnZ0wmEz169CiR/4OSiPFOXF1d6datG926dUOv1zNr1ix2795Nhw4dbtnH5lbz6tzq86NPnz44OjqyYMEC2rRpw4IFC9BqtZbO0LczcOBAPvzwQxISEnBxceHvv/9m8ODB2Nj8+zU4duxYZsyYwbhx4wgPD8fNzQ2NRsOgQYPK5HUQhZNERQDmWpXx48dz+PBh5syZQ2hoaL4OawsXLqRTp0788ssv+R6XlJSEl5fXXZ3zyJEjnDp1ilmzZuUbHr1u3bpC9zeZTJw7d85SiwJw6tQpgFt28vT29sbFxQWj0XjLCduKa/DgwXzwwQfUq1evQGfVPHk1P3q9/o7nDQoK4vTp0wW2nzx58o6xLFy4kODgYBYvXpzvyyCv1uZuhYSE8Morr/DKK69w+vRpmjRpwhdffMHs2bML3d/R0ZHOnTuzceNGoqOjCQwMvO3xg4KCWL9+PampqflqVU6cOGG5vzi8vb1xcHC463IsTmfVoKAgDh8+jMlkylercrexX79+nQ0bNjB58mQmTJhg2V7YcylqfFD48z5x4gReXl44OTnd1bGLqnnz5syaNYuYmBjg3xqapKSkfPv9t/bpTpycnHjwwQf5888/+fLLL5k/fz7t2rUr0Jm/MAMHDmTy5MksWrQIX19fUlJSGDRoUL59Fi5cyPDhw/niiy8s27KysgrELcqWNP0I4N/mnwkTJnDw4MEC/SN0Ol2BX1d//vmnpZ/F3cj7FXfzcRVF4X//+98tHzN16tR8+06dOhW9Xm/pS1PYOQYMGMCiRYsKncMjPj6+2HE/9dRTTJw4Md+H2X/5+PjQsWNHfvzxR8uH9a3O26tXL3bt2sWePXvy3f/HH3/cMZbCynD37t2WqvHiysjIKFBdHxISgouLC9nZ2bd97MSJE1EUhSeeeIK0tLQC90dERFiGiPbq1Quj0Zjv9QT46quv0Gg09OzZs1hx63Q6unfvztKlS7l48aJl+/Hjx1mzZs0dH5/3xV2UL6RevXoRGxubrxkhNzeXb7/9FmdnZ0vTZXFih4K1F19//XWxjpPH39+fJk2aMGvWrHzP5+jRo6xdu5ZevXrd1XH/KyMj45bvs7w+RnnNT3n9i7Zu3WrZx2g08tNPPxX7vAMHDuTKlSv8/PPPHDp06I7NPnnq1atHw4YNmT9/PvPnz8ff35/27dvn26ewz7lvv/3WamdUriykRkUA5j4lbdq04a+//gIokKg8+OCDvPfee4wcOZI2bdpw5MgR/vjjj1vOa1AUdevWJSQkhFdffZXLly/j6urKokWLbtkubm9vz+rVqxk+fDitWrVi1apVrFixgrfffhtvb+9bnufjjz9m06ZNtGrViqeffpr69euTmJjI/v37Wb9+PYmJicWKOygoqEhrCX333Xfcf//9NGzYkKeffprg4GCuXr3Kzp07uXTpkmUOmtdff53ff/+dHj168OKLL1qGJ+f9cr+dBx98kMWLF9OvXz969+5NVFQUP/zwA/Xr1y80WbiTU6dO0aVLFx577DHq16+PjY0NS5Ys4erVqwV+ff5XmzZt+O6773j++eepW7duvplpN2/ezN9//80HH3wAmKvwO3XqxDvvvMP58+dp3Lgxa9eu5a+//mLcuHH5Os4W1eTJk1m9ejXt2rXj+eeftyQPYWFhdyzHJk2aoNPp+OSTT0hOTsbOzo7OnTvnmwskzzPPPMOPP/7IiBEjiIiIoEaNGixcuJAdO3bw9ddfF7lDcR5XV1dLXyCDwUDVqlVZu3YtUVFRxTrOzT777DN69uxJeHg4Tz75pGV4spubW4mtg5WRkUGbNm1o3bo1PXr0IDAwkKSkJJYuXcq2bdvo27evpakwLCyM1q1b89Zbb5GYmIiHhwfz5s0jNze32Oft1asXLi4uvPrqq5YfIkU1cOBAJkyYgL29PU8++WSBfkYPPvggv//+O25ubtSvX5+dO3eyfv36EpmZW9yDMh5lJKzYd999pwBKy5YtC9yXlZWlvPLKK4q/v7/i4OCgtG3bVtm5c2eBoZt5w/b+/PPPAscobEhfZGSk0rVrV8XZ2Vnx8vJSnn76acvQ0xkzZlj2Gz58uOLk5KScPXtWeeCBBxRHR0fF19dXmThxYoFh0/xneLKiKMrVq1eV0aNHK4GBgYper1f8/PyULl26KD/99NMdyyVvePLtFDY8WVEU5ezZs8qwYcMUPz8/Ra/XK1WrVlUefPBBZeHChfn2O3z4sNKhQwfF3t5eqVq1qvL+++8rv/zyyx2HJ5tMJuWjjz5SgoKCFDs7O6Vp06bK8uXLleHDhytBQUGW/fKGyhY27Pjm8kpISFBGjx6t1K1bV3FyclLc3NyUVq1aKQsWLLhjOeWJiIhQhgwZogQEBCh6vV6pUqWK0qVLF2XWrFn5XqvU1FTlpZdesuwXGhqqfPbZZ4rJZCoQ3+jRowuc579DUBVFUbZs2aI0a9ZMsbW1VYKDg5UffvjBMgT3To+dPn26EhwcbBnOnPc+/W+ZK4r5/TRy5EjFy8tLsbW1VRo2bJjv/aooRS9zRVGUS5cuKf369VPc3d0VNzc35dFHH1WuXLlSYL+iDk9WFEVZv3690rZtW8XBwUFxdXVV+vTpo0RGRubbJ69sbh7eX9TzGAwGZfr06Urfvn0t7z9HR0eladOmymeffVZg2PzZs2eVrl27KnZ2doqvr6/y9ttvK+vWrSt0eHJYWNhtn9vQoUMVQOnatWuh9xf2+iqKopw+fVoBFEDZvn17gfuvX79ueV2dnZ2V7t27KydOnChwPBmeXLY0ilIOeqMJIYQQolKSPipCCCGEsFqSqAghhBDCakmiIoQQQgirJYmKEEIIIayWJCpCCCGEsFqSqAghhBDCapXrCd9MJhNXrlzBxcWlWFNgCyGEEEI9iqKQmppKQEBAgYn3/qtcJypXrly545oiQgghhLBO0dHRVKtW7bb7lOtEJW+q6ujoaMsy86JsGAwG1q5dywMPPIBer1c7nEpHyl9dUv7qkbJXV0mVf0pKCoGBgUVacqJcJyp5zT2urq6SqJQxg8GAo6Mjrq6u8mGhAil/dUn5q0fKXl0lXf5F6bYhnWmFEEIIYbUkURFCCCGE1ZJERQghhBBWq1z3URFCiMrOZDKRk5OjdhhlxmAwYGNjQ1ZWFkajUe1wKp2ilr9er0en05XIOSVREUKIcionJ4eoqChMJpPaoZQZRVHw8/MjOjpa5s9SQXHK393dHT8/v3t+nSRREUKIckhRFGJiYtDpdAQGBt5x0qyKwmQykZaWhrOzc6V5ztakKOWvKAoZGRnExcUB4O/vf0/nlERFCCHKodzcXDIyMggICMDR0VHtcMpMXlOXvb29JCoqKGr5Ozg4ABAXF4ePj889NQPJqyyEEOVQXv8AW1tblSMRonB5CbTBYLin40iiIoQQ5Zj00xDWqqTem5KoCCGEEMJqSaIihBCiwpk5cybu7u5qhyFKgCQqQgghysyIESPQaDQ8++yzBe4bPXo0Go2GESNGlH1g/7F582Y0Gg1JSUlqh1LpSaIihCiSuNQsYpOzSEjLJikjh7TsXIwmRe2wRDkUGBjIvHnzyMzMtGzLyspizpw5VK9e/Z6Pf6+dN++F0Wi02nlt1CyXeyGJihDijv63/jQtP9xA6ykbaP7Bepq8t44GE9cQNnE1T/yym2mbz3L4UpIkLqJI7rvvPgIDA1m8eLFl2+LFi6levTpNmzbNt+/q1au5//77cXd3x9PTkz59+hAVFWW5//z582g0GubPn0+HDh2wt7fnjz/+KHDO+Ph4mjdvTr9+/cjOzsZkMjFlyhRq1qyJg4MDjRs3ZuHChZZjdurUCYAqVarctpYnr4np77//pn79+tjZ2XHx4kWys7N59dVXqVq1Kk5OTrRq1YrNmzdbHnfhwgX69OlDlSpVcHJyIiwsjJUrV1ru37JlCy1btsTOzg5/f3/efPNNcnNzLffXqFGDr7/+Ol8sTZo0YdKkSZbbGo2GadOm8dBDD+Hk5MSHH34IwLJly2jRogX29vZ4eXnRr18/y2OKEvegQYPw9PQsNO7SIPOoCCHuaNXRGAA0GlBuykWyDCa2nU5g2+kEAFztbejZwJ9XutfGx8VejVArLUVRyDSoM6W8g15X7BEeo0aNYsaMGQwdOhSAX3/9lZEjR+b7UgRIT0/n5ZdfplGjRqSlpTF+/Hgef/xxDh06lG8ejzfffJMvvviCpk2bYm9vz5o1ayz3RUdH061bN1q3bs0vv/yCTqfjww8/ZPbs2fzwww+EhoaydetWHn/8cby9vbn//vtZtGgRAwYM4OTJk7i6ulrmBSlMRkYGn3zyCT///DOenp74+PgwZswYIiMjmTdvHgEBASxZsoQePXpw5MgRQkNDGT16NDk5OWzduhUnJyciIyNxdnYG4PLly/Tq1YsRI0bw22+/ceLECZ5++mns7e3zJSJFMWnSJD7++GO+/vprbGxsWLFiBf369eOdd97ht99+IycnJ1+icae4x4wZQ05ODps3b8bFxSVf3KVFEhUhxG1l5ORy6moqALve6oKPix25JgWjSeHCtQx2nEngn7MJ7D6XSEpWLvP3RbPySAwvP1CbJ1oHYaOTituykGkwUn/CmjvvWAoi3+uOo23xvk4ef/xx3nrrLS5cuADAjh07mDdvXoFEZcCAAflu//LLL/j6+hIZGUmjRo0s28eNG0f//v0LnOfkyZN069aNfv368fXXX6PRaMjOzuajjz5i/fr1hIeHAxAcHMz27dv58ccf6dChAx4eHgD4+PjcsVOuwWDg+++/p3HjxgBcvHiRGTNmcPHiRQICAgB49dVXWb16NTNmzOCjjz7i4sWLDBgwgIYNG1rOn+f7778nMDCQqVOnotFoqFu3LleuXOGNN95gwoQJxZrobsiQIYwcOdJye9CgQQwaNIjJkydbthUn7ujoaHr37k3Dhg3RarX54i4tkqgIIW7r6OUUTAr4udrj62quJdHrNOh1UMfPhTp+Loy6vya5RhN7z19nyqrjHL6UzORlkfy57xLv9w2jWZCHys9CWBtvb2969+7NzJkzURSF3r174+XlVWC/06dPM2HCBHbv3k1CQoKl/8fFixfzJSrNmzcv8NjMzEzatWvHkCFD8jWTnDlzhoyMDLp165Zv/5ycnAJNT0Vha2ubL5YjR45gNBqpXbt2vv2ys7Px9PQE4IUXXuC5555j7dq1dO3alQEDBliOcfz4ccLDw/PVUrVt25a0tDQuXbpUrH48/y2XgwcP8vTTTxe6b1HiHjNmDKNHj2br1q0F4i4tkqgIIW7rUHQSAI0D3W67n41OS3iIJ0ueb8vcPRf5bM1JImNSGDBtJ21redK2lhdtQ7xoUNUNnVYmKStpDnodke91V+3cd2PUqFGMGTMGgO+++67Qffr06UNQUBDTp08nICCA3NxcGjVqVGDFaCcnpwKPtbOzo2vXrixfvpzXXnuNqlWrApCWlgbAihUrLNtufkxxOTg45Esq0tLS0Ol0REREFJg6Pq+Z5KmnnqJ79+6sWLGCtWvXMmXKFL744gvGjh1bpHNqtVoUJX+fsMI6y/63XG7XhFXUuNu0acPWrVtZv359seO+G5KoCCFu6+ClJAAaB7oXaX+dVsPjrYPo2cCPT1efZP6+aHacucaOM9eAk7ja2xAe4smYTqE0rHb75EcUnUajKXbzi9p69OhBTk4OGo2G7t0LJlnXrl3j5MmTTJ8+nXbt2gGwdevWIh9fq9Xy+++/M2TIEDp16sTmzZsJCAjI1+m1Q4cOhT42b2mCvKUKiqNp06YYjUbi4uIscRcmMDCQZ599lmeffZa33nqL6dOnM3bsWOrVq8eiRYtQFMWSAO3YsQMXFxeqVasGmGukYmJiLMdKSUnJ18n4Vho1asSGDRvyNQcVN+5q1arx7LPP8vzzz+eLu7RI47EQ4rbyalSaVHMv1uM8ne345JFGbHylA5P61KdbfV9c7G1IycplzbGrDPjhHxZGXCr5gEW5odPpOH78OJGRkYUuWlelShU8PT356aefOHPmDBs3buTVV18t9jn++OMPGjduTOfOnYmNjcXFxYVXX32Vl156iVmzZnH27Fn279/Pt99+y6xZswAICgpCo9GwfPly4uPjLbUwRVG7dm2GDh3KsGHDWLx4MVFRUezZs4cpU6awYsUKwNynZs2aNURFRbF//342bdpEvXr1AHj++eeJjo5m7NixnDhxgr/++ouJEyfy8ssvW/qndO7cmd9//51t27Zx5MgRhg8fXqSF/yZOnMjcuXOZOHEix48f58iRI3zyySdFjvull15iw4YNhcZdWiRREULcUkJaNpeuZ6LRQIO7rP0I9nZmRNuaTB/WnAPju7F0dFu61vMhJ9fEq38eYuJfRzEYrXPeCVH6XF1dcXV1LfQ+rVbLvHnziIiIoEGDBrz00kuWL9XisLGxYe7cuYSFhdG5c2fi4uJ4//33GT9+PFOmTKFevXr06NGDFStWULNmTQCqVq3K5MmTefPNN/H19bU0URXVjBkzGDZsGK+88gp16tShb9++7N2719K/xGg0Mnr0aMu5a9euzffff28598qVK9mzZw+NGzfm2Wef5cknn+Tdd9+1HP+tt96iQ4cOPPjgg/Tu3Zu+ffsSEhJyx7g6duzIn3/+yd9//02TJk3o3Lkze/bsKVbcr732GmFhYQXiLi0a5b+NXOVISkoKbm5uJCcn3/KNLkqHwWBg5cqV9OrVC71er3Y4lU5Zlf+G41d5ctY+avk4s/7lwqvI74bJpPDNxtN8vf40AC1rePDd0Pvwdil+/wA1WMP7Pysri6ioKGrWrIm9feUZCm4ymUhJScHV1bVYo19EyShO+d/uPVqc7295lYUQt2TpSFvMZp870Wo1jOtam+nDmuNiZ8Oe84n0/mYbn685ScSF6zJxnBDConz1vBJClKmDl5IBaHKHET93q1t9X5aOacszv+3jbHw6UzedYeqmM3g42dKhtjcPNQmgUx2fUjm3EKJ8ULVGpUaNGmg0mgKX0aNHqxmWEALzTKf/Dk12L7XzhHg7s2zs/XzxaGN6N/LHxd6GxPQclhy4zMgZe/lzX3SpnVsIYf1UrVHZu3dvvqFfR48epVu3bjz66KMqRiWEALhwLYPkTAO2Oi11/Uq3D5ijrQ0DmlVjQLNqGIwmIi5cZ8HeaBYfuMw7S49S29elVJMlIYT1UrVGxdvbGz8/P8tl+fLlhISE3HJcuxCi7By6MX9K/QBXbG3K7qNCr9PSOtiTzx9tTLf6vuTkmnh2dgTxqdllFoMQwnpYTWfanJwcZs+ezahRo4q9uJUQouQdzJs/RaWaDK1Ww5ePNSbE24mY5CxG/7FfhjELUQlZTWfapUuXkpSUdMultMG83kB29r+/qlJSUgDzUMHCpg4WpSevvKXc1VEW5X/w4nUAGvg7q/Y62+vg+8FNGPDjbvacT2Ty30eZ+GDpTi5VFNbw/jcYDCiKgslksqx/UxnkzaiR99xF2SpO+ZtMJhRFwWAwFJiMrjj/O1Yzj0r37t2xtbVl2bJlt9xn0qRJ+VZ8zDNnzhwcHR1LMzwhKhWjCV7foyNX0fBOk1x8br08SJk4mqhh+knzB92gYCPhvlbxsaUqGxsb/Pz8CAwMtEz3LoQ1ycnJITo6mtjYWHJzc/Pdl5GRwZAhQ4o0j4pVJCoXLlwgODiYxYsX8/DDD99yv8JqVAIDA0lISJAJ38qYwWBg3bp1dOvWTSZ8U0Fpl/+xKyn0nbYLV3sb9r7VCa0VLCL47aazfLPxLABPtg3ipa6h2JVh35mbWcP7Pysri+joaGrUqFGpJnxTFIXU1FRcXFykm4AKilP+WVlZnD9/nsDAwEInfPPy8ipSomIVTT8zZszAx8eH3r1733Y/Ozu7Qle21Ov18mWpEil7dZVW+R+NMa9r0jjQHTs76/i1Pq5rHZIzc5m18wK/7LjAznPX+WZwE2r5uKgWk5rvf6PRiEajQavVVqoZWvOaG/Kee0Wi0WhYsmQJffv2VTuUWypO+Wu1WjQaTaH/J8X5v1H9VTaZTMyYMYPhw4djY2MVeZMQlV5pzUh7L7RaDZMfbsD0Yc3xcLIlMiaF3t9s5/ddFwosdy+sX2xsLGPHjiU4OBg7OzsCAwPp06cPGzZsUDu0Ujdp0iSaNGlSYHtMTAw9e/Ys+4CsnOqJyvr167l48SKjRo1SOxQhxA15Q5Otce6SbvV9Wf1iO9qFepGda2L80qO8ueiIJCvlyPnz52nWrBkbN27ks88+48iRI6xevZpOnTpV6gk//fz8Cm01qOxUT1QeeOABFEWhdu3aaocihADSsnM5HXej6ecuV0wubT6u9swa2ZIJD9ZHp9Uwf180f+67pHZYooief/55NBoNe/bsYcCAAdSuXZuwsDBefvlldu3aBcDFixd5+OGHcXZ2xtXVlccee4yrV69ajjF58mSaNGnC77//To0aNXBzc2PQoEGkpqZa9lm4cCENGzbEwcEBT09PunbtSnp6OmBeRXjcuHH54urbt2++kac1atTggw8+YNiwYTg7OxMUFMTff/9NfHy8JbZGjRqxb98+y2NmzpyJu7s7S5cuJTQ0FHt7e7p37050dLTl/smTJ3Po0CHLbOwzZ84EzM0pS5cutRzryJEjdO7c2RL/M888Q1pamuX+ESNG0LdvXz7//HP8/f3x9PRk9OjRFW40puqJihDCuhy5lIyiQICbPT6u1ttJU6vVMOr+mrzczfwjZ8LfRzl1NfUOj6rAFAVy0tW5FKM2KzExkdWrVzN69GicnJwK3O/u7o7JZOLhhx8mMTGRLVu2sG7dOs6dO8fAgQPz7Xv27FmWLl3K8uXLWb58OVu2bOHjjz8GzM0ogwcPZtSoURw/fpzNmzfTv3//Yte8ffXVV7Rt25YDBw7Qu3dvnnjiCYYNG8bjjz/O/v37CQkJYdiwYfmOm5GRwYcffshvv/3Gjh07SEpKYtCgQQAMHDiQV155hbCwMGJiYoiJiSnwvADS09Pp3r07VapUYe/evfz555+sX7+eMWPG5Ntv06ZNnD17lk2bNjFr1ixmzpxpSXwqCukUIoTIZ82xWMA6m30K81yHEHZHJbL1VDzP/7Gfv8e0xdG2En60GTLgowB1zv32FbAtmHQU5syZMyiKQt26dW+5z4YNGzhy5AhRUVEEBgYC8NtvvxEWFsbevXupU6cOYO7jOHPmTFxczB2qn3jiCTZs2MCHH35ITEwMubm59O/fn6CgIAAaNmxY7KfWq1cv/u///g+ACRMmMG3aNFq0aGFZ6uWNN94gPDycq1ev4ufnB5hHhU2dOpVWrVoBMGvWLOrVq8eePXto2bIlzs7OluHltzJnzhyysrL47bffLAnd1KlT6dOnD5988gm+vr4AVKlShalTp6LT6ahbty69e/dmw4YNPP3008V+rtZKalSEEBYnY1P5fdcFAAa2CFQ5mqLJm8HWx8WOM3FpTPjrmNohidsoSo3G8ePHCQwMtCQpAPXr18fd3Z3jx49bttWoUcOSpAD4+/sTFxcHQOPGjenSpQsNGzbk0UcfZfr06Vy/fr3Y8TZq1Mjyd15ycHPCk7ct77xgnuOmRYsWltt169YtEPudHD9+nMaNG+erdWrbti0mk4mTJ09atoWFheWbTO3mMqgoKuHPDiFEYRRFYfxfRzGaFLqH+dKxjo/aIRWZl7Md/xvUlKE/72JhxCXCgz0Z0Kya2mGVLb2juWZDrXMXUWhoKBqNhhMnTtz7af8zxFWj0ViGz+p0OtatW8c///zD2rVr+fbbb3nnnXfYvXs3NWvWRKvVFkiaCuvbcfM58uYNKWybWrPk3q4MKgqpURFCAPDXwSvsiUrEXq9l/IP11Q6n2MJDPHmxi7m/yrtLj3ImrpL1V9FozM0valyKMfGah4cH3bt357vvvrN0bL1ZUlIS9erVIzo62tIBFSAyMpKkpCTq1y/6e1Oj0dC2bVsmT57MgQMHsLW1ZcmSJYB5UdyYmBjLvkajkaNHjxb52LeTm5ubr4PtyZMnLc8LwNbWFqPReNtj1KtXj0OHDuUrox07dqDVai1NX5WFJCpCCFKyDHy40lwtPbZzKNWqlM8lKcZ0rkWbEE8yDUZGzdwnKy5bqe+++w6j0UjLli1ZtGgRp0+f5vjx43zzzTeEh4fTtWtXGjZsyNChQ9m/fz979uxh2LBhdOjQgebNmxfpHLt37+ajjz5i3759XLx4kcWLFxMfH29JFjp37syKFStYsWIFJ06c4LnnniMpKalEnp9er2fs2LHs3r2biIgIRowYQevWrWnZsiVgbrKKiori4MGDJCQk5JtxPc/QoUOxt7dn+PDhHD16lE2bNjF27FieeOIJS3NTZSGJihCCr9edJj41m5peTjzVrqba4dw1nVbDN4ObUt3DkYuJGYyauZf07Nw7P1CUqeDgYPbv30+nTp145ZVXaNCgAd26dWPDhg1MmzYNjUbDX3/9RZUqVWjfvj1du3YlODiY+fPnF/kcrq6ubN26lV69elG7dm3effddvvjiC8uEaqNGjWL48OGWBCg4OJhOnTqVyPNzdHTkjTfeYMiQIbRt2xZnZ+d8sQ8YMIAePXrQqVMnvL29mTt3bqHHWLNmDYmJibRo0YJHHnmELl26MHXq1BKJsTyxirV+7lZKSgpubm5FWitAlCyDwcDKlSvp1auXTKGvgpIs/+MxKTz47XaMJoVZo1rSobZ3CUWpnqiEdAZM+4fE9Bw61vFm+rDm6HUl97vMGt7/WVlZREVFUbNmzUq11o/JZCIlJQVXV1ernEJ/5syZjBs3rsRqZ6xNccr/du/R4nx/W9+rLIQoM4qiMOFGB9qeDfwqRJICUNPLiV+GN8der2XzyXjeWSIz1wpRXkmiIkQlZTQpvL3kKHvPX8dBr+PdctiB9naaVq/Cd0PuQ6uBBfsu8dX602qHJIS4C5KoCFEJ5RpNvLLgIHP3XESrgQ/7NaCqu4PaYZW4LvV8+bCfec6LbzacZtPJijW/hLA+I0aMqLDNPmqRREWISiY718jzf+xn6cEr2Gg1/G9QU/rfV3HnHBncsjrDw80zk/645azK0QghiksSFSEqkcwcI0/N2sfayKvY2mj54fFm9Gms0rTrZejZjiHotBp2nUvk2JVktcMpUdL3RlirknpvSqIiRCWRnWtk5Mw9bDudgINex4wRLehav3LMx+Dv5kCvhv4AzNhxXt1gSkjetOk5OTkqRyJE4TIyMoCCs+cWl0yhL0Ql8eGK4+w6l4iLnQ0zRrageQ0PtUMqUyPb1mDZoSv8ffAKb/Soi7eLndoh3RMbGxscHR2Jj49Hr9db5VDd0mAymcjJySErK6vSPGdrUpTyVxSFjIwM4uLicHd3z7cW0d2QREWISmDpgcv8ttO82OD/BjepdEkKwH3Vq9Ak0J2D0UnM2X2RF7uGqh3SPdFoNPj7+xMVFcWFCxfUDqfMKIpCZmYmDg4OlnV2RNkpTvm7u7vfdoXoopJERYgK7mRsKm8tPgLAC51r0blu5WjuKcyo+2vywtwD/L7rAs92DMbO5t5+6anN1taW0NDQStX8YzAY2Lp1K+3bt5fJJlVQ1PLX6/X3XJOSRxIVISqw1CwDz82OINNgpF2oFy92ra12SKrq2cAPP1d7YlOyWH4opkKssKzVaivVzLQ6nY7c3Fzs7e0lUVGBGuUvDXxCVFCKovDan4c5l5BOgJs9/xvUFJ22cleV63VahrUxD1X+dUeUjJgRohyQREWICmr6tnOsPhaLrU7L9483w8PJVu2QrMLgFtWx12s5diWFPVGJaocjhLgDSVSEqIDWHotlyqoTAEzoU58mge7qBmRFqjjZ0q+pucmnogxVFqIik0RFiArm8KUkXpx3EEWBIa2qM7RVdbVDsjqj2tYAYG1kLH8dvKxuMEKI25JERYgK5NL1DEbN3EemwUiH2t6891CYDOEsRKivC480q4ZJgRfnHeSbDaelv4oQVkoSFSEqiORMAyNn7CUhLZu6fi5MHdIUG538i9/KpwMa8Uz7YAC+XHeKVxYcIjvXqHJUQoj/kk8xISqAnFwTz82O4HRcGr6udswY2QIXexm6eTtarYa3e9Xjw34N0Gk1LD5wmSd+3sP19MozJ4kQ5YEkKkKUc4qiMOGvo/xz9hqOtjp+Gd4CfzcHtcMqN4a2CmLGiBa42Nmw53wig37aRZZBalaEsBaSqAhRzs3efZF5e6PRamDqkKY0qOqmdkjlTvva3ix6vg1eznacvJrKtxtPqx2SEOIGSVSEKMf2nE9k8t/HAHi9R91KPT3+vart68IHfRsA8OOWc0ReSVE5IiEESKIiRLl1PRvGzjtErkmhT+MA/u9Gx1Bx93o08KNHmB+5JoU3Fx8m12hSOyQhKj1JVIQoh7IMRn45qSMx3UB9f1c+HdBIhiGXkPceDsPF3obDl5KZ+c95tcMRotKTREWIckZRFN79K5LodA1VHPX8+EQzHGzL9yrA1sTH1Z53etUD4PO1J7l4LUPliISo3CRREaKc+XlbFH8dikGLwjcDGxPo4ah2SBXOwBaBtA72IMtg4u0lR2QyOCFUJImKEOXIhuNX+WjVcQD61jDROthD5YgqJo1Gw5T+jbCz0bL9TAILIy6pHZIQlZYkKkKUEydjU3lh7gEUBQY2r0Z7P/mVX5pqejkxrmttAD5YcZz41GyVIxKicpJERYhy4FpaNk/O2kt6jpHWwR5MfLAu0ne29D3driZhAa4kZxqYvOyY2uEIUSlJoiKElcvONfLs7AguXc8kyNORaUOboZc1fMqEjU7LJwMaodNqWH44hvWRV9UOSYhKRz7thLBiiqLwzpKj7D1/HRd7G34Z3oIqTrZqh1WpNKjqxlPtagLw7tKjpGYZVI5IiMpFEhUhrNiaY1dZGHEJrQa+G3IftXyc1Q6pUhrXpTZBno7EpmTx6eqTaocjRKUiiYoQVirXaOLTNScAeK5jCO1re6scUeXlYKtjSr+GAPy+6wL7LlxXOSIhKg9JVISwUgsjLnEuPp0qjnqe7RCidjiVXptaXgxsHgjAO0sjMcjs+kKUCdUTlcuXL/P444/j6emJg4MDDRs2ZN++fWqHJYSqMnOMfLX+FABjOofiYq9XOSIB8Haveni72HEuIZ1NV2TYlRBlQdVE5fr167Rt2xa9Xs+qVauIjIzkiy++oEqVKmqGJYTqZv5znqsp2VR1d+Dx1tXVDkfc4Oao562edQHYGqslO1eqVYQobTZqnvyTTz4hMDCQGTNmWLbVrFlTxYiEUF9SRg7TNp8B4OVutbGzkXV8rEmfxgF8uvoEsSnZLD8cw6BWNdQOSYgKTdUalb///pvmzZvz6KOP4uPjQ9OmTZk+fbqaIQmhummbz5KSlUtdPxf6Nq2qdjjiP/Q6LU/cqOWa8c8FWQdIiFKmao3KuXPnmDZtGi+//DJvv/02e/fu5YUXXsDW1pbhw4cX2D87O5vs7H+nsU5JSQHAYDBgMMjcBmUpr7yl3EtWTHIWM/85D8DLXWthMuZiMhbcT8pfXf0b+/K/9ac4eTWNrSev0ibEU+2QKg1576urpMq/OI/XKCr+HLC1taV58+b8888/lm0vvPACe/fuZefOnQX2nzRpEpMnTy6wfc6cOTg6ygqyovybe1bLrjgtIS4KY8OMMk2+FVsYpWVbrJb67ib+r570VRGiODIyMhgyZAjJycm4urredl9Va1T8/f2pX79+vm316tVj0aJFhe7/1ltv8fLLL1tup6SkEBgYyAMPPHDHJypKlsFgYN26dXTr1g29XkaklISd566xe1cEAFMGtaJpdfdb7ivlry6DwUD83+vYHqslMklLnRbtCPF2UjusSkHe++oqqfLPaxEpClUTlbZt23LyZP5ZHk+dOkVQUFCh+9vZ2WFnZ1dgu16vlzesSqTsS0Z8ajavLDx6Y2XkQFqGFG1yNyl/9Xg7QJe63qw/Ec9vu6P56MaEcKJsyHtfXfda/sV5rKqdaV966SV27drFRx99xJkzZ5gzZw4//fQTo0ePVjMsIcqUyaTw0vyDxKdmU9vXmUkPhakdkiiikW3NP6oW779EYnqOytEIUTGpmqi0aNGCJUuWMHfuXBo0aMD777/P119/zdChQ9UMS4gy9f3mM2w/k4CDXsd3Q+7DwVaGI5cXLYKq0KCqK1kGE3N2X1A7HCEqJNVnpn3wwQc5cuQIWVlZHD9+nKefflrtkIQoM7vPXePLdeYZaN97OIxQXxeVIxLFodFoeOr+YABm7bxAdm4hQ7SEEPdE9URFiMrqWlo2L8w7gEmB/vdV5dEb68iI8qVXQ398Xe2IT81m5ZEYtcMRosKRREUIFSiKwusLD3M1JZsQbyfef7iB2iGJu2Rro+XxVua+Kn/suqhyNEJUPJKoCKGCRfsvs+FEHLY6LVOH3IeTnaoD8MQ9eqxFIDqthn0XrnPqaqra4QhRoUiiIkQZu5qSxXvLjgEwrlso9fxlDqDyztfVnm71fAGYs1tqVYQoSZKoCFGGFEXh7cVHSMnKpVE1N55pF6x2SKKEDGllXv9n0f5LZOZIp1ohSookKkKUoaUH/23y+eyRxtjo5F+wori/lhfVPRxJzcpl2eEraocjRIUhn5JClJG41Cwm/R0JwItdQ6njJ0ORKxKtVsOgluaRW9L8I0TJkURFiDKgKArvLDlKcqaBBlVdeaa9NPlURI82C0Sv03AwOoljV5LVDkeICkESFSHKwLLDMayLvIpep+GzRxqjlyafCsnbxY4HwvwAqVURoqTIp6UQpSw508B7y8xNPmM6ySifim5oS3On2r8OXiE9O1flaIQo/yRREaKUfbn2JAlp5ondnusYonY4opSFh3gS7OVEWnYufx+STrVC3CtJVIQoRUcvJ/P7LvNide8/3ABbG/mXq+g0Gg2Db9Sq/CELFQpxz+RTU4hSYjIpvLP0KCYFHmocQJtaXmqHJMrIgGbVsLXRcvRyCocvJakdjhDlmiQqQpSSeXujORSdhLOdDe/2rqd2OKIMeTjZ0rOBuVPt3D3RKkcjRPkmiYoQpeBaWjafrD4BwMvdauPjaq9yRKKsDWphbv75++Bl6VQrxD2QREWIUvDJ6hMkZxqo5+/KsPAgtcMRKmgd7EFNLyfSc4wsl5lqhbhrkqgIUcIiLlxnwb5LAHzQN0ymya+kNBoNg1rcmKlWmn+EuGvyCSpECTKZFMvKyI82q0azIA+VIxJqGtCsGnqdhkPRSUReSVE7HCHKJUlUhChBSw9e5tClZJztbHi9R121wxEq83K244H65k618/bKTLVC3A1JVIQoIRk5uXy6+iQAz3cKwdvFTuWIhDXIW6hwyYHLZOYYVY5GiPJHEhUhSsiPW84Rm5JFtSoOjGpbU+1whJVoG+JFoIcDqVm5rDwSo3Y4QpQ7kqgIUQJikjP5cetZAN7qWQ97vU7liIS10Go1lqHKc/dI848QxSWJihAl4NPVJ8kymGhRowq9GvqpHY6wMo82q4ZOq2HfheucvpqqdjhClCuSqAhxjw5GJ7HkwGUAxj9YH41Go3JEwtr4uNrTpa4PIDPVClFckqgIcQ8U5d/hyAPuq0ajau7qBiSsVt5ChYv2XyLLIJ1qhSgqSVSEuAfLDsew/2ISDnodr/eoo3Y4woq1r+1NtSoOJGca+OvgZbXDEaLckERFiLuUZTDy8crjADzbIQRfWc9H3IZOq2F4eA0AZuw4j6Io6gYkRDkhiYoQd2n61nNcSc4iwM2eZ9oHqx2OKAceax6Ig17HidhUdkclqh2OEOWCJCpC3IWrKVl8v9k8HPnNXvVwsJXhyOLO3Bz19L+vKgAzd5xXNxghyglJVIS4C5+uPkmmwch91d3p08hf7XBEOTK8TQ0A1kbGcul6hrrBCFEOSKIiRDEdik5i0X7z6sgT+oTJcGRRLLV9XWhbyxOTAr/vuqB2OEJYPUlUhCgGRVF4b3kkAP2bVqVJoLu6AYlyaUQb8xIL8/dGy/o/QtyBJCpCFMPywzFEXLh+YziyrI4s7k7nuj4EejiQlCFDlYW4E0lUhCiiLIORj1edAOC5jiH4uclwZHF3bh6qPPMfGaosxO1IoiJEEf2yPYrLSZn4u9nzdDsZjizuzaM3DVXedU6GKgtxK8VKVNLT05kwYQINGjTA2dkZFxcXGjVqxHvvvUdGhvReFxVXfGo23286A8AbPerKcGRxz9wc9AxodmOo8j9RKkcjhPWyKeqOOTk5dOjQgaNHj9KzZ0/69OmDoigcP36cDz/8kFWrVrF161b0en1pxiuEKr5cd4r0HCONq7nxUOMAtcMRFcSw8BrM3nWR9cfjiE3OkuZEIQpR5ERl2rRpXLp0iUOHDlGnTv41TU6cOEHHjh354YcfGDt2bIkHKYSaTsSmMH/vRQDefbA+Wq0MRxYlo7avCy1reLDnfCLz90bzYtdQtUMSwuoUueln8eLFjB8/vkCSAlC3bl3eeecdFi5cWKLBCaE2RVH4cMVxTAr0auhHixoeaockKpihrc2rKs/be5Fco0nlaISwPkVOVCIjI+nYseMt7+/UqRORkZElEZMQVmPzqXi2nU7AVqflDRmOLEpBjwZ+eDjZEpOcxaaT8WqHI4TVKXKikpSUhKen5y3v9/T0JDk5uVgnnzRpEhqNJt+lbl35MhDWIddo4sMV5tWRR7StQZCnk8oRiYrIzkbHo82qAfDHbpmpVoj/KnKiYjKZ0OluPdJBq9ViNBZ/hsWwsDBiYmIsl+3btxf7GEKUhrl7ozkTl0YVRz2jO9VSOxxRgQ1uaW7+2XIqnuhEGUEpxM2K3JlWURS6dOmCjU3hD8nNzb27AGxs8PPzu6vHClFaUrMMfL3uFAAvdauNm4OMZhOlp4aXE+1Cvdh2OoG5ey7KrMdC3KTIicrEiRPvuM+AAQOKHcDp06cJCAjA3t6e8PBwpkyZQvXq1Yt9HCFK0g9bznItPYdgLyfLr10hStPQVtXZdjqBBfuiGde1NrY2Mh+nEFDCiUpxtWrVipkzZ1KnTh1iYmKYPHky7dq14+jRo7i4uBTYPzs7m+zsbMvtlJQUAAwGAwaDocTjE7eWV94VsdxjkrP4eZt5Aq5Xu4WCyYjBZF0Lx1Xk8i8PSqP829fywMfFjrjUbFYdvkyvhlLTXBh576urpMq/OI/XKPe4yMSWLVtIT08nPDycKlWq3MuhSEpKIigoiC+//JInn3yywP2TJk1i8uTJBbbPmTMHR0fHezq3EHnmnNGyO15LTReFF8OMaGTaFFFGVl7UsuayllBXE2PCZKiyqLgyMjIYMmQIycnJuLq63nbfIicqn3zyCWlpabz//vuAuc9Kz549Wbt2LQA+Pj5s2LCBsLCwewq+RYsWdO3alSlTphS4r7AalcDAQBISEu74REXJMhgMrFu3jm7dulWo2YhPxqbS5/udKAoseKYlTQPd1Q6pUBW1/MuL0ir/K0mZdPpyGyYFVr/QlhBvGWn2X/LeV1dJlX9KSgpeXl5FSlSK3PQzf/583njjDcvthQsXsnXrVrZt20a9evUYNmwYkydPZsGCBXcdeFpaGmfPnuWJJ54o9H47Ozvs7OwKbNfr9fKGVUlFK/vP1p1BuTG5W8tgb7XDuaOKVv7lTUmXf5C3ns51fVh/PI5FB67wTu/6JXbsikbe++q61/IvzmOL3FsrKiqKRo0aWW6vXLmSRx55hLZt2+Lh4cG7777Lzp07ixXoq6++ypYtWzh//jz//PMP/fr1Q6fTMXjw4GIdR4iSsP10AltOxWOj1fB6dxl1IdTxSLNAAJYfjsFkuqeWeSEqhCInKrm5uflqM3bu3EmbNm0stwMCAkhISCjWyS9dusTgwYOpU6cOjz32GJ6enuzatQtvb+v/JSsqFpNJYcoq8+Ruj7cOooaXVLkLdXSs442znQ0xyVlEXLyudjhCqK7ITT8hISFs3bqV4OBgLl68yKlTp2jfvr3l/kuXLt125trCzJs3r1j7C1Falh68zLErKbjY2TC2s0zuJtRjr9fxQJgvi/dfZvmhK7K+lKj0ilyjMnr0aMaMGcOTTz5Jz549CQ8Pp379f9tPN27cSNOmTUslSCFKU0qWgSmrTgDwbMcQPJ0L9oMSoiz1aRwAwIojMbJQoaj0ipyoPP3003zzzTckJibSvn17Fi1alO/+K1euMGrUqBIPUIjS9uXaU8SnZlPTy4mn2tVUOxwhuL+WF+6OehLSctgdlah2OEKoqshNPwCjRo26ZTLy/fffl0hAQpSlo5eT+W3neQDef7gBdja3Xs9KiLKi12np2cCfuXsusuzQFdrW8lI7JCFUU+RE5fDhw4Vud3Nzo3r16mhkVixRzhhNCu8sOYJJMVe13x8qXwbCevRpbE5UVh2N5b2HG8iU+qLSKnKi0qRJEzQaDf+dH06j0WBvb8+4ceN47733brvCshDWZO6eixy6lIyLnQ3je9dTOxwh8mlV0xNvFzviU7PZfiaeznV91Q5JCFUUOVGJiooqdHtSUhIRERGMHz+eKlWq8Oqrr5ZYcEKUlvjUbD5dbe5A+8oDtfFxtVc5IiHy02k19G7oz8x/zrPsUIwkKqLSKnKiEhQUdMvtjRs3xtXVlcmTJ0uiIsqFKSuPk5KVS1iAK0+E11A7HCEK1adxADP/Oc/aY7FkGYzY66XGWlQ+Jdbo2axZs1vWughhTfZEJbL4wGU0GviwX0N0WulfJazTfdXdqeruQHqOkU0n4tQORwhVlFiiEhsbKzPKCqunKIqlyWdQi+o0sdJFB4UAcx/ABxv7A7Ds8BWVoxFCHSWSqMTHxzN+/Hg6depUEocTotRsORXPvgvXsbPRMq5rqNrhCHFHfRqZJ3/bcDyOtOxclaMRouwVuY9K06ZNCx2CnJyczKVLl6hTpw6zZ88u0eCEKEmKovDF2lMADAsPwlc60IpyICzAlWAvJ84lpLP0wGUeb114f0EhKqoiJyp9+/YtdLurqyt16tShe/fuMjRZWLW1kVc5cjkZR1sdz3YIUTscIYpEo9HwRHgQk5dF8t2mMzzSrJp0qhWVSpETlYkTJ5ZmHEKUKpNJ4csbtSmj2taU9XxEuTK4ZXV+2nqOmOQs5u65yMi2stSDqDxkqkNRKSw/EsPJq6m42NvwdLtgtcMRoljs9TrGdjb3qfpu01kyc4wqRyRE2ZFERVR4uUYTX68z16Y80y4YN0e9yhEJUXyPNq9GdQ9HEtKyLetTCVEZSKIiKrwlBy5zLiGdKo56Rt4vVeaifNLrtLzQxVyr8sOWs6RmGVSOSIiyUaREJSUlpbTjEKJU5OSa+N+G0wA81zEEZ7tiLRguhFXp2ySAYG8nrmcYmLHjvNrhCFEmipSoVKlShbg486yInTt3JikpqTRjEqLEzNl9gUvXM/F2seOJ1jXUDkeIe2Kj0/JS19oATN96jqSMHJUjEqL0FSlRcXZ25tq1awBs3rwZg0GqHIX1S8kyWGpTxnUNxcFWhnSK8q93Q3/q+rmQmp3L9G3n1A5HiFJXpHrwrl270qlTJ+rVqwdAv379sLW1LXTfjRs3llx0QtyDHzaf5XqGgWBvJwY2D1Q7HCFKhFar4eVutXnm9whm7DjP0+2CcXcs/PNYiIqgSInK7NmzmTVrFmfPnmXLli2EhYXh6OhY2rEJcddikjP5Zbt5kcw3e9TFRif9xkXF0a2+L3X9XDgRm8qKIzEMbSWz1YqKq0iJioODA88++ywA+/bt45NPPsHd3b004xLinny59hTZuSZa1vCgW31ftcMRokRpNBr631eVj1aeYOmBy5KoiAqt2D8zN23aZElSFEVBUZSSjkmIe3IiNoWF+y8B8FavuoWuUSVEefdQ46poNLD3/HWiEzPUDkeIUnNX9eG//fYbDRs2xMHBAQcHBxo1asTvv/9e0rEJcVc+XnUCRTF3OmxavYra4QhRKvzc7GkT4gnA34euqByNEKWn2InKl19+yXPPPUevXr1YsGABCxYsoEePHjz77LN89dVXpRGjEEW240wCm0/GY6PV8Fr3OmqHI0Sp6tukKgCL91+S2m1RYRV79qtvv/2WadOmMWzYMMu2hx56iLCwMCZNmsRLL71UogEKUVS5RhMfrTwOwOOtg6jh5aRyREKUrh4N/Hh36VHOxqdz7EoKDaq6qR2SECWu2DUqMTExtGnTpsD2Nm3aEBMTUyJBCXE3ftx6jmNXUnCxt2Fs51pqhyNEqXOx19P1RmfxJQcuqxyNEKWj2IlKrVq1WLBgQYHt8+fPJzQ0tESCEqK4jsek8PV688KDE/uE4elsp3JEQpSNfjeaf/4+dIVco0nlaIQoecVu+pk8eTIDBw5k69attG3bFoAdO3awYcOGQhMYIUpbTq6JVxYcwmBU6FrPlwH3VVU7JCHKTPva3lRx1BOfms0/Z6/Rvra32iEJUaKKXaMyYMAAdu/ejZeXF0uXLmXp0qV4eXmxZ88e+vXrVxoxCnFbUzeeJjImhSqOej7q30CGI4tKxdZGy4ONAgBYelCaf0TFc1dLyTZr1ozZs2eXdCxCFNuh6CS+23wWgPf7NsDHxV7liIQoe32bVuX3XRdYczSWjL65ONrKKuGi4pB5xUW5lWUw8sqfhzCaFB5s5G/5VSlEZXNfdXeqeziSnmNkXeRVtcMRokRJoiLKrS/XneJMXBpezna8/3ADtcMRQjUajYa+TcyJuoz+ERWNJCqiXNp7PtGyxP3H/RtSxUlWjxWVW9+m5k7k204nEJ+arXI0QpQcSVREuZORk8urfx5CUeCRZtUs80gIUZkFezvTJNAdo0nhL+lUKyqQe05UUlJSWLp0KcePHy+JeIS4o49XneDCtQz83eyZ0Ke+2uEIYTUGNKsGwKL9kqiIiqPYicpjjz3G1KlTAcjMzKR58+Y89thjNGrUiEWLFpV4gELcbMeZBH7beQGATwY0wtVer3JEQliPPo38sdVpOR6TQuSVFLXDEaJEFDtR2bp1K+3atQNgyZIlKIpCUlIS33zzDR988EGJByhEntQsA68vPAzA0FbVZWIrIf7D3dGWLvV8AFi0/5LK0QhRMoqdqCQnJ+Ph4QHA6tWrGTBgAI6OjvTu3ZvTp0+XeIBC5Plg+XEuJ2US6OHA273qqR2OEFZpwH3m5p+/Dl6WKfVFhVDsRCUwMJCdO3eSnp7O6tWreeCBBwC4fv069vYy2ZYoHZtOxDF/XzQaDXz+SGOc7GRCKyEK06GON55OtiSk5bD1dLza4Qhxz4qdqIwbN46hQ4dSrVo1AgIC6NixI2BuEmrYsOFdB/Lxxx+j0WgYN27cXR9DVEypWQbeXnIEgJFtatIq2FPliISwXnqdlodvLFS4KEI61Yryr9iJyvPPP8/OnTv59ddf2b59O1qt+RDBwcF33Udl7969/PjjjzRq1OiuHi8qts/XnCQmOYvqHo681r2O2uEIYfX631iYc13kVZIzDCpHI8S9uavhyc2bN6dfv344OztbtvXu3duymnJxpKWlMXToUKZPn06VKlXuJhxRgUVcuM5vu8yjfD7q1xAHW53KEQlh/cICXKnr50KO0cTyI1fUDkeIe1Kkhv6XX365yAf88ssvixXA6NGj6d27N127dpVRQyKfnFwTby46bJnY7f5QL7VDEqJc0Gg0DLivGh+uPM6iiEsMbRWkdkhC3LUiJSoHDhzId3v//v3k5uZSp465Gv7UqVPodDqaNWtWrJPPmzeP/fv3s3fv3iLtn52dTXb2v1NDp6SY5wkwGAwYDFK9WZbyyrs0y33qprOcjkvDw0nP6w/Uktf4JmVR/uLWykP5927gw5RVx9l/MYlTMUnU9HJSO6QSUR7KviIrqfIvzuOLlKhs2rTJ8veXX36Ji4sLs2bNsjTVXL9+nZEjR1rmVymK6OhoXnzxRdatW1fk0UJTpkxh8uTJBbavXbsWR0fHIp9blJx169aVynFjM+C7wzpAQ5+ALHZuXl8q5ynvSqv8RdFYe/nXcdNyPEnL5wu30bt6xRqqbO1lX9Hda/lnZGQUeV+NoihKcQ5etWpV1q5dS1hYWL7tR48e5YEHHuDKlaK1hy5dupR+/fqh0/3b58BoNKLRaNBqtWRnZ+e7DwqvUQkMDCQhIQFXV9fiPA1xjwwGA+vWraNbt27o9SU7O6zJpDDkl71EXEyiY20vfnq8KRqNpkTPUd6VZvmLOysv5b/iSCzjFhymWhUHNr50f4X4PyovZV9RlVT5p6Sk4OXlRXJy8h2/v4s9GUVKSgrx8QXH5sfHx5Oamlrk43Tp0oUjR47k2zZy5Ejq1q3LG2+8USBJAbCzs8POzq7Adr1eL29YlZRG2f+x+wIRF5NwstXxYf9G2NrKysi3Iu99dVl7+XdvEIDj0mNcup7Jsdh0mlavOAMWrL3sK7p7Lf/iPLbYiUq/fv0YOXIkX3zxBS1btgRg9+7dvPbaa/Tv37/Ix3FxcaFBgwb5tjk5OeHp6Vlgu6g8EtKy+WTVCQBeeaAOVd0dVI5IiPLLwVZHt/q+/HXwCssOxVSoREVUHsUenvzDDz/Qs2dPhgwZQlBQEEFBQQwZMoQePXrw/fffl0aMohL5aOVxUrJyCQtwZVi4jFQQ4l71aRQAwPLDVzCaitXSL4RVKHaNiqOjI99//z2fffYZZ8+eBSAkJAQnp3vvUb558+Z7PoYov3adu8bi/ZfRaOCDvg2w0d3VND9CiJu0q+2Fq70NcanZ7D2fSGuZ2VmUM3f9TeDk5ESjRo1o1KhRiSQponLLyTXx7tKjAAxuWV2qqIUoIXY2Ono08ANg2SGZ/E2UP8VOVNLT0xk/fjxt2rShVq1aBAcH57sIcTd+2R7Fmbg0PJ1seaN7XbXDEaJC6dPY3Pyz6mgsBllRWZQzxW76eeqpp9iyZQtPPPEE/v7+FWK4m1DXpesZfLPhNABv9aqHm6P05BeiJIUHe+LpZMu19Bz+OXuNDrW91Q5JiCIrdqKyatUqVqxYcVfr+ghRmEl/R5JpMNKypgcDbiymJoQoOTY6Lb0a+vP7rgv8ffCKJCqiXCl200+VKlXw8PAojVhEJbT88BXWH7+KjVbDB30bSA2dEKXkoSbm5p+1x2LJMhhVjkaIoit2ovL+++8zYcKEYk1/K0RhrqZkWTrQPt8xhNq+LipHJETF1ax6Ffzd7EnNzmXLqYKTdgphrYrd9PPFF19w9uxZfH19qVGjRoHZ5fbv319iwYmKS1EU3lh0mKQMAw2qujKmc6jaIQlRoWm1Gh5s5M/0bVEsO3SF7mF+aockRJEUO1Hp27dvKYQhKpu5e6LZfDIeWxstXz7WBFsbmTNFiNLWp3EA07dFseF4HBk5uTjaFvsrQIgyV+x36cSJE0sjDlGJXLiWzgcrIgF4vXsdafIRoow0rOpGkKcjF65lsC7yKg83kc7rwvrJz1hRpowmhVcWHCIjx0irmh6MaltT7ZCEqDQ0Gg0P3ZhTZWHEJZWjEaJoip2oGI1GPv/8c1q2bImfnx8eHh75LkLczvRt59h34TpOtjo+f7QxWq2M8hGiLD3WPBCNBradTuDCtXS1wxHijoqdqEyePJkvv/ySgQMHkpyczMsvv0z//v3RarVMmjSpFEIUFcX5hHS+XHsKgIl9wgj0cFQ5IiEqn0APR9qFmudRmbc3WuVohLizYicqf/zxB9OnT+eVV17BxsaGwYMH8/PPPzNhwgR27dpVGjGKCkBRFCYtO0aO0US7UC8ebV5N7ZCEqLSGtKwOwJ/7osnJlSn1hXUrdqISGxtLw4YNAXB2diY5ORmABx98kBUrVpRsdKLCWBt5lc0n49HrNEx+KEwmdhNCRV3q+eDjYkdCWg7rIq+qHY4Qt1XsRKVatWrExMQAEBISwtq1awHYu3cvdnZ2JRudqBAyc4y8t8w8yueZ9sEEezurHJEQlZtep2Vgi0AA5uy5oHI0QtxesROVfv36sWHDBgDGjh3L+PHjCQ0NZdiwYYwaNarEAxTl3/ebz3A5KZMAN3tGd6qldjhCCGBgC3On2h1nrnE+QTrVCutV7HlUPv74Y8vfAwcOpHr16uzcuZPQ0FD69OlTosGJ8i8qIZ0ft5wDYEKf+jLBlBBWoloVRzrU9mbzyXjm7r3IWz3rqR2SEIW652+N8PBwwsPDSyIWUcEoisLkGx1o29f2lim7hbAyQ1pWZ/PJeBbuu8Qr3erIDNHCKhX7XTlr1qx8nWZff/113N3dadOmDRcuSFun+NfNHWgn9akvHWiFsDKd6/rg52rPtfQc1hyLVTscIQpV7ETlo48+wsHBAYCdO3cydepUPv30U7y8vHjppZdKPEBRPiVnGpj09zFAOtAKYa1sdFoey+tUu/uiytEIUbhiJyrR0dHUqmXuELl06VIeeeQRnnnmGaZMmcK2bdtKPEBRPr23LJKY5CxqeDoyppOsjCyEtRrYIhCtBnaeu8a5+DS1wxGigGInKs7Ozly7dg2AtWvX0q1bNwDs7e3JzMws2ehEubQu8iqL9l9Cq4EvHmuMg61O7ZCEELdQ1d2BjnV8AJgvM9UKK1TsRKVbt2489dRTPPXUU5w6dYpevXoBcOzYMWrUqFHS8Yly5np6Dm8tPgLA0+2CaRYk6z8JYe0G35ipdmHEJZmpVlidYicq3333HeHh4cTHx7No0SI8PT0BiIiIYPDgwSUeoChfxv91lIS0bEJ9nHmpW221wxFCFEGnOt74utpxLV1mqhXWp9jDk93d3Zk6dWqB7ZMnTy6RgET5tfzwFZYfjkGn1fDlY02w10uTjxDlgY1Oy6PNApm66Qzz9l6kdyN/tUMSwqLYicrWrVtve3/79u3vOhhRfsWnZjN+6VEARneqRcNqbipHJIQojoEtzInKttMJRCdmyOrmwmoUO1Hp2LFjgW03z49hNBrvKSBR/uQaTYybf4DrGQbq+7syRqbJF6LcCfRwpF2oF9tOJzB/bzSvdq+jdkhCAHfRR+X69ev5LnFxcaxevZoWLVpYFigUlcsHK46z48w1HG11fD2oicxuKUQ5ldepdsG+aHKN0qlWWIdi16i4uRWs0u/WrRu2tra8/PLLRERElEhgonz4M+IyM/85D8CXjzWhtq+LugEJIe5a13q+eDrZEpeazcYTcTwgy14IK1BiP319fX05efJkSR1OlANRqTBxWSQAL3WtTY8G8qEmRHlma6PlkWbVAJgnc6oIK1HsGpXDhw/nu60oCjExMXz88cc0adKkpOISVi4mOYtfTuowGBV6NvBjbGfplyJERTCwRSA/bj3H5pNxXEnKJMDdQe2QRCVX7ESlSZMmaDQaFEXJt71169b8+uuvJRaYsF5ZBiOj5x4k1aChrq8znz/aGK1WFhwUoiII9namdbAHu84lsmBfNOO6ynxIQl3FTlSioqLy3dZqtXh7e2Nvb19iQQnrNuGvoxy5nIKTjcL3Q5vgZFfst5EQwooNblndnKjsjWZs51B08kNEqKjY3zBBQUGlEYcoJxbsi2bBPvM6PsNrmwisInMtCFHRdA/zw91Rz5XkLDYcvyqdaoWq7qoz7ZYtW+jTpw+1atWiVq1aPPTQQ7JyciVwPCbFMqnbC51rUcdNucMjhBDlkb1eZxmq/OuOqDvsLUTpKnaiMnv2bLp27YqjoyMvvPACL7zwAg4ODnTp0oU5c+aURozCCqRmGXj+j/1k55roWMeb59rXVDskIUQpGhYehI1Ww65ziRy7kqx2OKISK3ai8uGHH/Lpp58yf/58S6Iyf/58Pv74Y95///3SiFGoTFEU3lh0mKiEdALc7PnqsSbSeVaICs7fzYFeDc1r/vy6/by6wYhKrdiJyrlz5+jTp0+B7Q899FCBjraiYpix4zwrj8Si12n4buh9VHGyVTskIUQZGHW/ueZ02aErxKVmqRyNqKyKnagEBgayYcOGAtvXr19PYGBgiQQlrMeeqEQ+WnkcgHd61aNp9SoqRySEKCtNAt25r7o7OUYTf+y6qHY4opIq9qifV155hRdeeIGDBw/Spk0bAHbs2MHMmTP53//+V+IBCvVEJ2bw7OwIck0KDzbyZ3ibGmqHJIQoY6Pur8n+OQf4Y/cFnusYgr1ep3ZIopIpdo3Kc889x7x58zhy5Ajjxo1j3LhxHD16lPnz5/N///d/xTrWtGnTaNSoEa6urri6uhIeHs6qVauKG5IoBenZuTz92z4S03NoUNWVzx5pnG+VbCFE5dAjzI8AN3sS0nL4+9AVtcMRldBdzdTVr18/+vXrd88nr1atGh9//DGhoaEoisKsWbN4+OGHOXDgAGFhYfd8fHF3TCaFlxcc5ERsKl7Odvz0RHMcbOVXlBCVkY1Oy/A2NZiy6gS/bo/i0WbV5EeLKFN3PaVoTk4OcXFxmEz5lwKvXr16kY/x3065H374IdOmTWPXrl2SqKjo6/WnWHPsKrY6LT8+0UzW+hCikhvUojpfrz/NidhUdp67RpsQL7VDEpVIsZt+Tp8+Tbt27XBwcCAoKIiaNWtSs2ZNatSoQc2adz+3htFoZN68eaSnpxMeHn7XxxH3ZvnhK3yz8QwAH/VvSLMg6TwrRGXn5qi3rKr863YZ3SnKVrFrVEaMGIGNjQ3Lly/H39//nqsAjxw5Qnh4OFlZWTg7O7NkyRLq169f6L7Z2dlkZ2dbbqekpABgMBgwGAz3FIeAo5dTePXPQwA82TaIhxv53rJc87ZLuatDyl9dlbH8H29Zjdm7L7D+eBxHohOp6+eiShyVseytSUmVf3Eer1H+uwzyHTg5OREREUHdunWLHVhhcnJyuHjxIsnJySxcuJCff/6ZLVu2FJqsTJo0icmTJxfYPmfOHBwdZc2Ze5GcA18c0ZGco6Geu4ln6pqQOd2EEDebeUrLgWtamniYGFnHdOcHCHELGRkZDBkyhOTkZFxdXW+7b7ETlRYtWvDVV19x//3331OQt9K1a1dCQkL48ccfC9xXWI1KYGAgCQkJd3yi4tayDEaG/rKXw5dTCPF24s9nWuJir7/tYwwGA+vWraNbt27o9bffV5Q8KX91VdbyP3U1ld5TdwKwYkw4tX3Lvlalspa9tSip8k9JScHLy6tIiUqRmn7ymlgAPvnkE15//XU++ugjGjZsWCDQe00YTCZTvmTkZnZ2dtjZ2RXYrtfr5Q17lxRF4Z2FRzl8OQV3Rz2/jmiBh0vRa6ek7NUl5a+uylb+YdU86NXQj5VHYpm29TxTh9ynWiyVreytzb2Wf3EeW6RExd3dPV9fFEVR6NKlS759FEVBo9FgNBqLfPK33nqLnj17Ur16dVJTU5kzZw6bN29mzZo1RT6GuDffbTrD34euYKPVMG1oM4I8ndQOSQhhxcZ2DmXlkVhWHInhxauphKpQqyIqlyIlKps2bSqVk8fFxTFs2DBiYmJwc3OjUaNGrFmzhm7dupXK+UR+q4/G8PnaUwC893ADwkM8VY5ICGHt6vm70j3MlzXHrvLtxjN8M7ip2iGJCq5IiUqHDh147733ePXVV0u00+ovv/xSYscSxbP9dAIvzD0IwIg2NRjSqujz3wghKrcXuoSy5thVlh2+wgtdQqnl46x2SKICK/I8KpMnTyYtLa00YxFlZO/5RJ7+bR85RhM9wvx4t3c9tUMSQpQjYQFudKvvi6LA1I2n1Q5HVHBFTlSKOThIWKnDl5IYOWMvmQYjHet4883gptjoij3vnxCiknuxSygAfx+6wrl4+RErSk+xvqFkfYfy7URsCsN+3UNadi6tgz344fFm2NpIkiKEKL4GVd3oWs8HkwLfbJBaFVF6ijUzbe3ate+YrCQmJt5TQKJ0nItP4/Gfd5OUYaBpdXd+Ht5ClmsXQtyTcV1rs/54HEsPXuGpdsE0qOqmdkiiAipWojJ58mTc3OSNWN7EJmfxxC97SEjLob6/KzNHtsTZ7q7XoxRCCMBcq9KvaVWWHLjMBysimft0a6l5FyWuWN9WgwYNwsfHp7RiEaUgKSOHYb/u5nJSJsFeTvz2ZEvcHGSSJCFEyXi1ex1WHIlh17lENhyPo2t9X7VDEhVMkTsoSJZc/mTmGHly1j5OXU3D19WOWaNa4uVccGZfIYS4W1XdHXjy/poAfLTqOAajrAEkSpaM+qmgDEYTo+fsJ+LCdVztbfhtVCsCPWThRiFEyXuuYwgeTraci09n3t5otcMRFUyRExWTySTNPuWEyaTwxqLDbDwRh71ey68jWlBHpSXZhRAVn6u9nnFdzcOVv153itQsg8oRiYpExqZWQJ+tPcni/ZfRaTV8P/Q+mtfwUDskIUQFN7hldYK9nLiWnsMPW86qHY6oQCRRqWBm77rAtM3mD4lPBjSic13p2CaEKH16nZY3e9YF4OdtUVxJylQ5IlFRSKJSgayPvMqEv44C8HK32jzSrJrKEQkhKpNu9X1pWdOD7FwTn605qXY4ooKQRKWCOBSdxNi5BzApMKhFIGM711I7JCFEJaPRaCxrhy05cJmD0UnqBiQqBElUKoCL1zJ4cpZ5/Z4Otb15v28DGU4uhFBFo2ru9L+vKgDvL4+UEaPinkmiUs4lpucwYoZ51tmwAFe+G3ofellkUAihote718VBryPiwnWWH45ROxxRzsk3WjmWkZPLyJl7OZeQTlV3B34d0UKmxhdCqM7PzZ5nO4QA8PGqE2QZjCpHJMozSVTKKYPRxHOz93MoOgl3Rz2zRrXA19Ve7bCEEAKAZ9oH4+9mz+WkTH7ZHqV2OKIck0SlHDKZFF5feJgtp+ItE7rV8pEJ3YQQ1sPBVsfrPeoA8P2mM8SlZqkckSivJFEph6asOs6SA+YJ3aYNbcZ91auoHZIQQhTwcOOqNK7mRnqOkS/WnFI7HFFOSaJSzvy45SzTt5mrUT8d0IhOdWVZAyGEddJqNUzoUx+ABRHRRFxIVDkiUR5JolKOzPrnPFNWnQDgrZ51GSATugkhrFyzIA/6Na2KosCzs/cTmyxNQKJ4JFEpJ37fdYGJfx8DzCuVPtM+WOWIhBCiaN7v24A6vi7Ep2bzf7/vk1FAolgkUSkH5uy+yPil5qnx/699MK93ryMTugkhyg1nOxumD2uOu6OeQ5eSeXvxEZkIThSZJCpWbv7ei7y95AgAT91fkzd71pUkRQhR7lT3dOS7Ifeh02pYfOCyDFkWRSaJihWbt+ciby42Jykj29bgnd71JEkRQpRbbWt58U4v81pAH608ztZT8SpHJMoDSVSskKIofLXuFG8uPoKiwIg2NZjwYH1JUoQQ5d7ItjV4pFk1TAqMmbOfM3GpaockrJwkKlbGYDTx+sLD/G/DaQCe7xjCxD6SpAghKgaNRsMHfRtwX3V3UrJyGf7rXq6myEggcWuSqFiR1CwDo2bu5c+IS2g18FG/hrzeQ/qkCCEqFnu9jp+Ht6CmlxOXkzIZ/useUrMMaoclrJQkKlYiLiWLx37cxbbTCTjodfw8vDlDWlVXOywhhCgVHk62zBrZEi9nO07EpvLs7Ahyck1qhyWskCQqViA718jTv+3jeEwKXs62zP+/1nSu66t2WEIIUaqqezoyY0QLHG117DhzjdcXHsJkkmHLIj9JVKzAe8siOXQpGTcHPQufbUOjau5qhySEEGWiYTU3vh96HzZaDUsPXuHj1SdkjhWRjyQqKlsUcYk/dl9Eo4GvBzWhhpeT2iEJIUSZ6ljHh48HNALgp63n+HiVJCviX5KoqOjYlWTLZG4vdgmlUx1ZYFAIUTk90qwakx8KA+DHred4f/lxSVYEIImKapIzDDw3ez/ZuSY61vHmhc6haockhBCqGt6mBh/2awDArzuimPj3MemzIiRRUYPJpPDygoNcTMygWhUHvh7YBK1WhiALIcTQVkF8OqARGg38tvMC7yw9KslKJSeJigqmbjrDhhNx2Npo+eHxZrg72qodkhBCWI3HWgTyxaON0Wpg7p6LvPLnIRm6XIlJolLG1kde5ct1pwD4oG8DGlR1UzkiIYSwPv3vq8ZXA5ug02pYcuAyo2bulUnhKilJVMrQmbg0Xpp/EIDh4UE81jxQ3YCEEMKKPdykKr/emGdl+5kEHv1hp0y3XwlJolJGUrIMPPP7PlKzc2lZ04N3H6yvdkhCCGH1OtT2ZsH/hVtmsH30pz3EZqgdlShLkqiUAZNJ4eX5BzkXn46/mz3fD70PvU6KXgghiqJBVTeWPN+GYG8nYpKz+PqojlVHY2X4ciUh35Zl4H8bTrP+uLnz7I9PNMPL2U7tkIQQolwJ9HBk0bNtuK+6O5lGDS/MP8ywX/dwLj5N7dBEKVM1UZkyZQotWrTAxcUFHx8f+vbty8mTJ9UMqcQtP3yF/204DZhXQ5bp8YUQ4u5UcbLltxHN6F7NhK2Nlm2nE+jx9TY+X3OSzByj2uGJUqJqorJlyxZGjx7Nrl27WLduHQaDgQceeID09HQ1wyox+y9e5+UFhwAY1bYmjzSrpnJEQghRvtnpdfQKNLFyTBs61PYmx2hi6qYzdP1yC1tOxasdnigFNmqefPXq1fluz5w5Ex8fHyIiImjfvr1KUZWM6MQMnp61j5xcE13r+fBO73pqhySEEBVGkKcjM0e2YM2xq7y/PJLLSZkM/3UPjzWvxju96+PmoFc7RFFCVE1U/is5ORkADw+PQu/Pzs4mOzvbcjslJQUAg8GAwWA94+tTMg2MmLGHa+k51Pd34fMBDTAZczFVoJrJvPK2pnKvTKT81SXlr57/ln2XOp6E1wzny/Vn+G3XRRbsu8Tmk/G816ceXerJ+mklraTe+8V5vEaxkm7TJpOJhx56iKSkJLZv317oPpMmTWLy5MkFts+ZMwdHR8fSDrFIjCb44YSWU8la3GwVXm5gxF36zgohRKk7mwLzzuqIyzIvSdLU00TPQBO+DioHJgrIyMhgyJAhJCcn4+rqett9rSZRee6551i1ahXbt2+nWrXC+3IUVqMSGBhIQkLCHZ9oWXn3r0jm77uEo62OuU+1oL6/dcRV0gwGA+vWraNbt27o9VLFWtak/NUl5a+eO5V9lsHINxvP8suO85gU0GigdwM/nu8QTKivswoRl1+5RhMHopNpHuSORmNO/krqvZ+SkoKXl1eREhWraPoZM2YMy5cvZ+vWrbdMUgDs7OywsytYPaHX663iw2LJgUvM33cJjQa+HdyUxtU91Q6p1FlL2VdWUv7qkvJXz63KXq/X886DYTzctBpfrz/N+uNXWX4klhVHY+nVwJ/RnWpRP6Bi/oAsaWuOX2HMnAN0rOPNzJEt8913r+/94jxW1VE/iqIwZswYlixZwsaNG6lZs6aa4dyTc/FpvLPkKAAvdgmlSz1flSMSQojKq0FVN34e3pzlY++nR5gfigIrjsTQ65ttjJixh93nrsmEcbehKArTt54DoEmgu6qxqFqjMnr0aObMmcNff/2Fi4sLsbGxALi5ueHgUH4aFbNzjYyde4CMHCOtanowtnOo2iEJIYTAnLD88EQzTsSmMHXjGVYeiWHzyXg2n4ynaXV3nusQQtd6vmi1GrVDtSp7ohI5dCkZOxstT7QOUjUWVWtUpk2bRnJyMh07dsTf399ymT9/vpphFduUlSc4diUFDydb/jeoKTp5wwshhFWp6+fK1CH3sfGVjgxpVR1bGy0HLibxzO8RdP1yC7/vPE96dq7aYVqN6dvMtSmPNKuGp8qzqatao1IRqt3WHotl5j/nAfji0cb4udmrG5AQQohbquHlxEf9GjKuaygzdpxn9q4LnEtIZ/xfx/hszUkGt6zOsDY1qOpefmr1S9qZuDTWH49Do4Gn2gWrHY51dKYtry4nZfLawsMAPN2uJp3qyph9IYQoD3xc7HmjR11Gd6rFoohLzNgRxflrGfy49RzTt53j/lBvHm4cQPcGfjjbVa6vyl+2m2tTHqjvS00vJ5WjkUTlrimKwpuLDpOcaaBxNTde615X7ZCEEEIUk7OdDcPb1OCJ1kFsOhnHrzui2HHmGltPxbP1VDxvLzlC1/q+9G1SlQ61vbG1qdhr+canZrNo/2UAnmmvfm0KSKJy17aeTmDb6QRsdVr+N6hphX/zCiFERabVauhSz5cu9Xw5n5DOXwev8NfBy5xLSGfF4RhWHI7BzUFPr4Z+9GkcQKuanhWyP+JvO8+Tk2vivuruNAsqfJb4siaJyl0wmhSmrDwOwLDwIGpYQdWYEEKIklHDy4kXu4byQpdaHL2cwtKDl1l26ApxqdnM3RPN3D3R+Lra0auhP13q+tKypkeF+LGakZPL77suANZTmwKSqNyVJQcucyI2FVd7G8Z0rqV2OEIIIUqBRqOhYTU3GlZz4+1e9dgddY2/D15h5ZEYrqZkM2PHeWbsOI+znQ331/Kicz0fOtb2xse1fA6qWBhxiaQMA0GejnSr76d2OBaSqBRTlsHIF2tPAjCmcy3cHW1VjkgIIURp02k1tAnxok2IF5MfDmPLyXjWH7/KxhPxJKRls/pYLKuPmecCq+PrQrtQL+4P9aJVTU8cbHUqR39nuUYTP2+LAuCp+2taVbOWJCrFNGPHeWKSs6jq7sCw8BpqhyOEEKKM2dnoeCDMjwfC/DCZFI5cTmbDiTg2nYjj6JVkTl5N5eTVVH7eHoWtTkuzoCrcH+pF21peNKzqZlVJAIDBaOKl+Qe5mJiBu6OeR5oFqh1SPpKoFENieg7fbzoDwKvda2Ovt/4sWQghROnRajU0DnSncaA7L3erTWJ6DjvOJLD9dALbzyRwOSmTneeusfPcNT5bcxJXexvCQzy5v5Y5canp5WRZ8E8N2blGxsw5wLrIq+h1Gj57pLHV1QBJolIM3248TWp2LvX9XXm4cVW1wxFCCGFlPJxs6dM4gD6NA1AUhaiEdHPiciaBf85eIyUrlzXHrrLm2FUA/N3saVvLi/BgTxpVcyPY27nMalwyc4z83+wItp6Kx85Gyw9PNKNTHeubD0wSlSK6cC2d2Td6Q7/dq56sCyGEEOK2NBoNwd7OBHs780R4DXKNJo5eSWH76Xh2nLlGxIXrxCRnsTDiEgsjLgHgoNdRP8CVhlXdCAtwpUFVN2r5OKPXleyoovTsXJ6ctZdd5xJx0Ov4ZXhz2tTyKtFzlBRJVIpo+rZzGIwK7Wt7c3+odb6YQgghrJeNTkuTQHeaBLozpnMomTlG9l1IZPvpBCIuXOfYlRQyDUYiLlwn4sJ1y+PsbLTU9XelQYArdf1dqe3jTG1fF6o4FW8wR06uiYgL19l6Op7VR2OJSkjH2c6GmSNb0LyGdcyZUhhJVIogO9fIskMxADxjBeseCCGEKP8cbHW0C/WmXag3YJ6jKyohjaOXUzhyOZmjl5OJvJJCanYuh6KTOBSdlO/xXs52hPo44+9mj4eTLZ7Odng62+JqrycjJ5fUrFxSswykZuVyNj6NnWevkZ5jtDze3VHPrJEtaRzoXobPuvgkUSmCTSfiSc404OtqR3iIp9rhCCGEqIB0Wg21fFyo5eNC36bmfpAmk8LFxAyOXknmyOVkTsWmcjoujUvXM0lIyyYhLbtY5/BytqVdqDfta3vRqY5PuZhiQxKVIlhywNx22LdJVasbViaEEKLi0mo11PByooaXEw82CrBsT8/O5UxcGmfj04hPzeZaeg7X0nK4lp5NSqYBJzsbXOxtcLHT42Jvg4+rHW1CvKjv71ru+lhKonIHSRk5bDwRB0C/+2SkjxBCCPU52dlYhkVXdOV/cYJStvxwDAajQj1/V+r6uaodjhBCCFGpSKJyB0sOmJe77t9UalOEEEKIsiaJym1cvJZBxIXraDXwcJOAOz9ACCGEECVKEpXbyKtNaVvLq9yuhimEEEKUZ5Ko3IKiKJbRPv2k2UcIIYRQhYz6uYUD0Umcv5aBg15H9zA/tcMRQlgrYy4Ys8FoAI0GNFrzhRt/a21AqzPfJ4QoNklUbmHJfnOzT48GfjjZSTEJUeEZcyH1CqTFQUYiZFyDzBvX6Qk3ruMhPQGbzER6Z6WjO5gLiqmIJ9CYkxaNBhTlxrYb1xod6PTmhEarBxs7cPAAJy9w8jZfHKrcuF934zg6sHOBKjXAoyY4+4FWKslFxSPfwIXIyTWx7PAVQJp9hCh3jLmQlWROONKu/nudeR1MNxKLvEtWMiRFQ9JFSLkMivGOhwfQcDcfngqYDLe470atzM1SLhfv8Db24B4EHsE3LjXBM8T8t1t1SWJEuSWJSiE2n4wjKcOAj4sdba10NUkhKg1FgZx0c/KRed2cdKRcuXG5bL7OuGauBclMguzkuz+XVg8ufuDoYa7RcPQ0/+3odaN2w1zDYbB1Y9O2nXTq2h29vZO5BkSrBxRzvDcnQ4oRTHmX3H/PdXNTkMloTmJMRnMTUm6WuTYnPeFGLU68+bmZjDeOl2v+O/M6XI8yJ1u5WZBw0nz5L1sXCGhy49LUfKlSU5qjRLkgicothPo407GOt0yZL0Rpykm3NKeQGmuu2bj5khpjTlBu/oIvKkdPcPY1N5s4+5oTDkt/Ea256cTW0Vzb4H7j4uxbtJoHg4FMuzPmpEavL35sJc2YC8nR5qQlMQoSz910iYKcVDi/zXzJ4+ABVZtBteZQtTlUa2ZuXhLCykiiUogHwvzoVt+XHGNR256FEBZ5NSB5CUh6nLnWI/mSuQYk+bL5Oj0eDBlFP65WDw7u5sTDtSq4Bvx77eR9oxakivkL2N4NdJXo401nY27q8agJIf+5z5hrrmW5cuDfS+wRc43NmXXmSx6fMKjRFoLaQlAbcPYp06chRGEq0X9y8Wg0GuxsdGqHIYR1yc2+qdnlirnzaWrsjb9jzTUgaXGQm1n0Y+rszF+ITt7/1my4Vzf3t3D1v5F8VAG9ozRV3A2dDfiGmS9NHzdvy82Bq0fgUgRc2guX95lrX+KOmS97fjLv5xkKga0gsAVUawnedaWviyhzkqgIIe5s1w+w/StIiy36Y2zswcnH3K8jr/bDreqN62r/Jie2zpKAlDUbW3OzT9Vm0OoZ87a0OLjwD1zYYb6+ehSunTZfDs4272PnBtVbQXAnCOlkTlzktROlTBIVIcStKQps+hC2fvbvNhv7fxMPF39zrYfLTRdnb3OCYuskX2LlibMPhPU1X8DcOfnSXojeDdF74PJ+c0fl02vNFzC/3sEdIaQLhHQGJ0+VghcVmSQqQojCmUyw5m3YPc18u/O70GyUuS+IJCAVn6MH1O5uvoC5r8vVoxC1Bc5ugos7zU19h+aaL2jMNTS1upovVe8zd1wW4h5JoiKEKMhkhGUvwIEbVf69PoeWT6sbk1CXzubfIc5tXwRDljlZObvRfLl61NzX5fI+2PKxeUh36ANQp4e5qcjeVe1nIMopSVSEEP9SFLh+HjZMhmNLzMN4H/4OmgxROzJhbfT25n4qIZ2A980dqs9sMI8iOrsZMhLg0BzzRauH6q3NtSx+jcwXzxCpcRFFIomKEJWZ0WDug3BxF1zaZ+6TkJFgvk+rhwE//9tnQYjbcQ2A+54wX4wGc23LqTVwchUkni04j4ve8cZopAbg18B87RtmXhZAiJtIoiJEZZN5HU6vh1OrzNf/nclVqwf/xtD5HXMHSSGKS6eHmu3Nl+4fQsJpOL/dPH9L7GG4esw8h86lvebLzbzr3aip6Wyey8XWSZ3nIKyGJCpCVHSKAvEnbozWWGceenrzmjaOnlCjHQS2hGotzNXyenv14hUVj1eo+ZLHZIRrZ8yJy9WjEHvUfJ0aA/HHzZdd34PO1jyPS8325qSlajPka6vykVdciIooI9GckJzdaE5Oki/mv9+7nrmTY+2e5inUpa+AKEtaHXjXMV8aPvLv9vQEc/PQ2Y3mfi7JF/M3Gels0fk3pZ7BG82RNPAOBY8QGYlWwUmiIkRFkHnd3M8kahuc32r+hYry7/029uZak9AHILSbeap1IayNkxeE9TNfFMU8W+65Teak+/wOSItFe2k3tQH+Xv7v4+zcwDPYPAGddx1zIu5dxzy7scykW+5JoiJEeaMokHSBaok70K7cAJf2mKvK/8urjrnKPPQBqHG/eQE+IcoLjcY8MsgzBFo8dWNEWhS557ZxaecSqjsb0F4/DymXzP2s8tYxupnWBpz9bkxK6AcuAeDia95mufYzrw8lCY3VkkRFCGuXFmeeFfTKfsu1PuMazQAu3LSfRwjUbGeuOanRzvxBLERFodGARzCKSyCHLrtTtVcvtHo9GDLNQ+oTTpsXX4w7AfEnIeEUGLPNiUzKpTscW2fuq+XkbZ5Z2dHLPO+LnQvYupivLRdnsHM1L/1g62SurbSxM1/r9NIEVQokURHCmuQtFhe9998REUkXCuymaPVct6+OW8Pu6Gq0gcDW5g9YISobvQP41DNfbmYy3lgoM9a8eGZKzI1FNK+a16zKu864Zu5cnh5nvsTdSzAacwdgnd5cm6Oz/fe2zta8xpLuRlLjWMWcEDl5mZMkBw9zEmTrfOP6RmLk4G7evxInQKomKlu3buWzzz4jIiKCmJgYlixZQt++fdUMSYiyYTKafwXGHb9xiTRfXzsNptz/7Kwxt7cH3GeeMCvgPnI9a7Nt7UZ6de2FTq9X4xkIYd20OvMimG5VwVz/WDijwZyspMVBery5Q29GAmSn3rik3PR3mvk658btnAxzrY2FYr6db1sJ0NmCvTvYu5kTGycvc+1P3qKfbtXMK467BVbIGYBVTVTS09Np3Lgxo0aNon///mqGIkTpyMkwD8O8dtpcNR1/0ny5dubWH2YOHuZhwtVamEfkVG1W8MPHYCj92IWoDHT6G/1X/O7u8YoCxhzIzTIvK2DMAZPBvDaSMefGxXAjgckx15oaMswd4POSovQE8+2cNHMylJP+bzKkmMyPy6vxuXb69vHYu0GVGua5kKo2N3+GeNct1yP7VE1UevbsSc+ePdUMQYh7ZzTA9Qvm2Tevnb1xfQYSzty+bdzGHrxqg0998K1vvvapb57hsxJX8wpRrmg0N/qo2JmThJJkMpmTl6wkyEqGzCRz7U96/L+XtDhIjoakaMhMNO8Xc8h82f+b+Ti2zlA9HLpMAP9GJRtjGShXfVSys7PJzv73V2hKSgoABoMBg/zCLFN55V0pyt2UC2lX0SRHQ/IlNMnRaK6fh6QLaJIuQsolNIrplg9XHKqgeNQCz1ooXrVRvOqgeNU2V9MW9isn979NPwVVqvK3QlL+6ql0Za9zACcHcPK/8745aebPqGtn0FzZj+ZKBJqYg2hy0uDMOpSzGzC1eBpT+zfveqmCkir/4jxeoyiKcufdSp9Go7ljH5VJkyYxefLkAtvnzJmDo6MMvRTFpJjQG9Oxy03BzpCKXW4SDobr2Ockmq8N13HIScTecB0tt05EAHK1tqTb+pJu70eanS/pdr6k2fmTZu9Pjo2sXSKEUIliwjXrEqGxy6iWtBuATH0VjlQdSox7C9VqbzMyMhgyZAjJycm4ut6+X025SlQKq1EJDAwkISHhjk9UlCyDwcC6devo1q0bemvozKmYbrTrpkN2CprMRPPsrJnXb/ydgOZGVakmLd7c1ptxDc3NU8nf7vBaPbhWRXGrCm6BKO5BKO5BUKUGilt1cPYt0394qyv/SkbKXz1S9ndPc24TutWvo7keBYCpdi+M/X82d9YtopIq/5SUFLy8vIqUqJSrph87Ozvs7OwKbNfr9eq/YY0GSLlsbidMj/u3d3jeJTfz345UN3ewMhludLQymIfIaXQ3hrXZmK+1NuZtGq15QiKNztxckLct76IrZChc3sXGruDfN2/LO49W++/58s6Rd07NjcmQ8r6Mc43YG66jz7qG3mR3436NOWHIdzEWfM43dzYzGcxNK7nZ5vkQDJnmsjJkmTucGTJvXN/4Oy8ZMWQUvL5b9u43etB7myeGcg0wTwzl6g+u5sRE4+wDWh3W1nPEKt77lZiUv3qk7O9CnQcguD1s/wq2f4n21Eq0q1+Hh6cW+4fWvZZ/cR5brhIVq2A0mIeRxhw0z4IYdwKSLprH59+mn0JFowe6AxxVOZD/0mjNkzE5ephHzzhUMf+dl4g4+5iH9DnfGNrn6Gme20AIISoDvT10egsCW8Afj8LB2eBdG9q+qHZkt6RqopKWlsaZM2cst6Oiojh48CAeHh5Ur15dxcj+IzcHDs2FA79DzOFbDyvV2YF7oHla5v/OZKh3vFHTYVdIrYcNaG9MEKQYb9S05JovRsO/NRMmo3konGL8t8bCZLxxn+nf4W9Gg7mG4uZhcbk5+YfHWbbl3Hh8rvlYedd5x8w7L4r53DeuFRQUkwkNChr+23qouVEDdKMmRmd30/PV35gMSf/v89bpb/SadzD/E+Vd653MkznpHczlp3e4MRuko/m2rZP5Om+SpLxZImXEjBBC3F6trtDjY1j1OqybCJ6hULeX2lEVStVEZd++fXTq1Mly++WXXwZg+PDhzJw5U6WobpKbDQdmm6vJkqP/3W7nBgGNwb8J+DUyj1l3r27+xV5J1ovINRhYuXIlvXr1MlfhKYo5cdJoJVEQQojyoOUzEH8C9v0Ki56CJ9eAX0O1oypA1USlY8eOWElf3vwMWebak+1fmfudgLmzZJuxUKcXVKlZaRKSItNozH1ZhBBClA8aDfT81Dz/U9QWmDMInt5odeuEybdtYbZ/BStfNScpLv7Q4xN48ZA5UfEMkSRFCCFExaDTw2OzwLOWeYLKBcNuNPdbD/nGLUyLJ80vWq/P4YWD0PpZc/8IIYQQoqJxqAJDFpgXQozeBf98o3ZE+UiiUhhnHxizD1o+be7UKYQQQlRkniHQ82Pz3xs/hFjrGdIpicqtSIdQIYQQlUmToeZ+mCYDLPk/84ASKyCJihBCCCHMP9D7/M88v9TVo7D5Y7UjAiRREUIIIUQeZx948Cvz3zu+hug9qoYDkqgIIYQQ4mb1H4ZGA81zYy35P/MyJSqSREUIIYQQ+fX81LzmWeI588y1KpJERQghhBD5ObhD3+8AjXk5FBUnZ5VFCYUQQghRUEhn81QdXrVUDUNqVIQQQghROJWTFJBERQghhBBWTBIVIYQQQlgtSVSEEEIIYbUkURFCCCGE1ZJERQghhBBWSxIVIYQQQlgtSVSEEEIIYbUkURFCCCGE1ZJERQghhBBWSxIVIYQQQlgtSVSEEEIIYbUkURFCCCGE1ZJERQghhBBWy0btAO6FoigApKSkqBxJ5WMwGMjIyCAlJQW9Xq92OJWOlL+6pPzVI2WvrpIq/7zv7bzv8dsp14lKamoqAIGBgSpHIoQQQojiSk1Nxc3N7bb7aJSipDNWymQyceXKFVxcXNBoNGqHU6mkpKQQGBhIdHQ0rq6uaodT6Uj5q0vKXz1S9uoqqfJXFIXU1FQCAgLQam/fC6Vc16hotVqqVaumdhiVmqurq3xYqEjKX11S/uqRsldXSZT/nWpS8khnWiGEEEJYLUlUhBBCCGG1JFERd8XOzo6JEydiZ2endiiVkpS/uqT81SNlry41yr9cd6YVQgghRMUmNSpCCCGEsFqSqAghhBDCakmiIoQQQgirJYmKEEIIIayWJCqiyD7++GM0Gg3jxo2zbMvKymL06NF4enri7OzMgAEDuHr1qnpBVjCXL1/m8ccfx9PTEwcHBxo2bMi+ffss9yuKwoQJE/D398fBwYGuXbty+vRpFSOuOIxGI+PHj6dmzZo4ODgQEhLC+++/n29tEin/krN161b69OlDQEAAGo2GpUuX5ru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+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -177,7 +177,7 @@
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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hxx9/tHjt49pwZHRMkgULFtCrVy/Gjx9vsf7OnTu4ubml2T40NDTddel9UKfy8vIC4K+//spQTcnTdO7cmUGDBvHVV1/x1ltvPbZ3iJeXFzqdjh07dpgTioelrkuN/XHn9rSxezJbhhlRqVIlFi1ahKIoHDt2jLlz5/L5559jb2/P//73v3RfU7NmTTw8PFi5ciUTJkx46nvA09OTffv2oSiKxbZhYWGkpKSY/24Z5e7ujk6ne2w5ZqXUv1lISEiaXlo3b97MdOyg/h2LFSvG4sWLLcrj0bY1mYkxJSWF27dvWyQ8iqIQGhpqbpCdXRo1akSrVq1YsWIFYWFheHt7Y2dnl+75PO4Hx+PeQ926dWPo0KHMnTuX//u//2P+/Pl06tQpTS1Qevr27cvAgQM5ffo0ly5dIiQkxKKdT2RkJKtXr2bMmDEW7/XUNlVCG1KzI7JE//79MRqNTJo0ibVr1/L666/j4OBgfl6n06X5wj527Bh79ux5ruOmt981a9Zw48aNdLdfuHAhiqKYH1+5coXdu3dbDLz3qNatW2Ntbc3FixepWbNmurfMsLe3Z/To0XTo0IEBAwY8drv27dujKAo3btxI95iVKlUCoG7dutjZ2fH7779bvH737t0ZuoSQ2TLMDJ1OR5UqVZg6dSpubm4cPnz4sdsaDAZGjhzJmTNn+OKLL9LdJiwsjF27dgHQvHlzYmJiWLFihcU2qY1xmzdvnqlYHR0dqV27NsuWLbOoCYmOjmbVqlVPff2jNVxP0qxZMwCLBsYABw4c4PTp05mOHdSytrGxsfiCDw0NfebeWKkxPBrj0qVLiY2NfaYY03Pr1q10u5cbjUbOnz+Pg4ODOekOCgoiLCzMonF+UlIS69evz9Qx3d3d6dSpE7/99hurV68mNDT0qZewUnXr1g07Ozvmzp3L3LlzKVy4MK1atTI/r9PpUBQlzf/UzJkzzY34Rc6Tmh2RJWrWrEnlypWZNm0aiqKkGVunffv2fPHFF4wZM4bGjRtz9uxZPv/8c4oVK0ZKSsozH7d9+/bMnTuXsmXLUrlyZQ4dOsSkSZMeO6ZJWFgYL7/8Mm+99RaRkZGMGTMGOzs7Pv7448ceIygoiM8//5xPP/2US5cu0aZNG9zd3bl16xb79+/H0dGRcePGZSruoUOHmru4Pk6DBg14++236du3LwcPHqRRo0Y4OjoSEhLCzp07qVSpEgMGDMDd3Z3hw4fz5Zdf8uabb/Lqq69y7do1xo4dm6HLL5ktw6dZvXo1M2bMoFOnThQvXhxFUVi2bBkRERG0bNnyia/96KOPOH36NGPGjGH//v10797dPKjg9u3b+eWXXxg3bhwNGjSgV69e/PDDD/Tu3ZvLly9TqVIldu7cyfjx42nXrh0tWrTIdOxffPEFbdq0oWXLlgwbNgyj0cjXX3+No6PjU3+VlyhRAnt7e37//XfKlSuHk5MT/v7++Pv7p9m2TJkyvP3223z//ffo9Xratm3L5cuXGTVqFAEBAXz44YeZjr19+/YsW7aMgQMH8sorr3Dt2jW++OIL/Pz8OH/+fKb317JlS1q3bs3IkSOJioqiQYMGHDt2jDFjxlCtWrV0u28/i/nz5/Pzzz/TvXt3atWqhaurK9evX2fmzJmcPHmS0aNHY2NjA6hjVY0ePZrXX3+djz76iISEBL777rtnSiL69evH4sWLee+99yhSpEiG3y9ubm68/PLLzJ07l4iICIYPH27RPd7FxYVGjRoxadIkvLy8CAoKYtu2bcyaNeuZa0pFFtCsabTId7799lsFUMqXL5/mucTERGX48OFK4cKFFTs7O6V69erKihUr0vSuSO0BMWnSpDT7SK93xL1795T+/fsr3t7eioODg/LCCy8oO3bsUBo3bmzRCym118P8+fOV999/XylUqJBia2urNGzYUDl48KDFcdLr2aEoirJixQqladOmiouLi2Jra6sEBgYqr7zyivLvv/8+sVwe7o31JI/2xko1e/ZspU6dOoqjo6Nib2+vlChRQunVq5dF3CaTSZkwYYISEBCg2NjYKJUrV1ZWrVqVphyyogwfPY9H93nmzBmlW7duSokSJRR7e3vF1dVVqV27tjJ37twnnv/DVq5cqbz44otKoUKFFGtra8Xd3V1p2rSp8tNPPymJiYnm7e7evau8++67ip+fn2Jtba0EBgYqH3/8sZKQkGCxP0AZNGhQmuOk1yvp77//VipXrqzY2NgoRYsWVb766qt03xPpvXbhwoVK2bJlFYPBoADKmDFjFEVJ/z1lNBqVr7/+WildurRiMBgULy8vpWfPnsq1a9cstmvcuLFSoUKFNLGn1zPpq6++UoKCghRbW1ulXLlyyq+//prh2NMTHx+vjBw5UgkMDFQMBoPi5+enDBgwQLl3716a/b344otpXv/oeyg9p06dUoYNG6bUrFnT4u/duHFjZf78+Wm2X7t2rVK1alXF3t5eKV68uDJ9+vTH9sZK72+eymg0KgEBAQqgfPrpp2mef1LPxQ0bNph7jqXX4/T69etKly5dFHd3d8XZ2Vlp06aNcuLEiTTlLr2xco5OUR6q0xdCCCGEyGekzY4QQggh8jVJdoQQQgiRr0myI4QQQoh8TZIdIYQQQuRrkuwIIYQQIl+TZEcIIYQQ+ZoMKgiYTCZu3ryJs7NzhqcpEEIIIYS2FEUhOjoaf39/i8EdHyXJDupcNAEBAVqHIYQQQohncO3atSeO+i7JDuDs7AyoheXi4qJxNAVHcnIyGzZsoFWrVhgMBq3DKXCk/LUl5a8tKX9tZVX5R0VFERAQYP4efxxJdngwM66Li4skOzkoOTkZBwcHXFxc5MNGA1L+2pLy15aUv7ayuvyf1gRFGigLIYQQIl+TZEcIIYQQ+ZokO0IIIYTI16TNjhBC5FNGo5Hk5GStw8iVkpOTsba2JiEhAaPRqHU4BU5Gy99gMGBlZfXcx5NkRwgh8hlFUQgNDSUiIkLrUHItRVHw9fXl2rVrMr6aBjJT/m5ubvj6+j7X30mSHSGEyGdSEx1vb28cHBzkyzwdJpOJmJgYnJycnjgYncgeGSl/RVGIi4sjLCwMAD8/v2c+niQ7QgiRjxiNRnOi4+npqXU4uZbJZCIpKQk7OztJdjSQ0fK3t7cHICwsDG9v72e+pCV/YSGEyEdS2+g4ODhoHIkQWSP1vfw87c8k2RFCiHxILl2J/CIr3suS7AghhBAiX5NkRwghhBD5miQ7QgghcoU+ffqg0+nQ6XQYDAaKFy/O8OHDiY2N1To0TfXp04dOnTpl2XYFkfTGEkLkTckJkJIA9m5aRyKyUJs2bZgzZw7Jycns2LGDN998k9jYWH788cdM70tRFIxGI9bW8lVX0EnNjhAi+ygKJMZAVAjcPgfXD6m3e1cgKe7JrzWZIPIGXN4FR36Hzf8Hy96GWa1hcln4Px+YVAJOrsiRUxE5w9bWFl9fXwICAujevTs9evRgxYoVgJq8TJw4keLFi2Nvb0+VKlX466+/zK/dunUrOp2O9evXU7NmTWxtbdmxYwdNmjRh8ODBDBkyBHd3d3x8fPjll1+IjY2lX79+ODs7U6JECf755x/zvoxGI/3796dYsWLY29tTpkwZvv32W4tYU2tSvvnmG/z8/PD09GTQoEEWvYaSkpIYMWIEhQsXxtHRkTp16rB161bz83PnzsXNzY3169dTrlw5nJycaNOmDSEhIQCMHTuWefPmsXLlSnOt18Ovf5ImTZrw/vvvM2LECDw8PPD19WXs2LEW20RERPD222/j4+ODnZ0dFStWZPXq1ebnly5dSoUKFbC1tSUoKIjJkydbvD4oKIgvv/ySXr164eTkRGBgICtXruT27dt07NgRJycnKlWqxMGDBy1et3v3btq1a4ejoyMBAQG8//772VqDJ+muECLrKQr8Oxb2zgBj0uO3s3ECRy+wtgfF9OBmSoboW2BMfPJxTCmw/B1wKQwBtbL0FPITRVGIT875KRHsDVbP3ZPG3t7enDx89tlnLFu2jB9//JFSpUqxfft2evbsSaFChWjcuLH5NSNGjOCbb76hePHiuLm5ATBv3jxGjBjB/v37Wbx4MYMGDWLp0qW88sorfPrpp0ydOpU33niDq1ev4uDggMlkokiRIixZsgQvLy92797N22+/jZ+fH127djUfa8uWLfj5+bFlyxYuXLjAa6+9RtWqVXnrrbcA6Nu3L5cvX2bRokX4+/uzfPly2rRpw/HjxylVqhQAcXFxfPPNN8yfPx+9Xk/Pnj0ZPnw4v//+O8OHD+f06dNERUUxZ84cADw8PDJcfvPmzWPo0KHs27ePPXv20KdPHxo0aEDLli0xmUy0bduW6OhoFixYQIkSJTh16pR5LJtDhw7RtWtXxo4dy2uvvcbu3bsZOHAgnp6e9OnTx3yMqVOnMn78eEaNGmUuxwYNGtCvXz8mTZrEyJEj6dWrFydPnkSn03H8+HHatm3LJ598wpw5c7h79y7vvfce7733nvkcs5pOURQlW/ach0RFReHq6kpkZCQuLi5ah1NgJCcns3btWtq1a4fBYNA6nAIn28pfUWDjaNj93YN1Oj3YOoOti/p8bNiTk6BUemtwDQD3QHALBPeg+7dAcC0Kfw+Gc/+Agxe8+S94FMu688hm2VX+CQkJBAcHU6xYMezs7ACIS0qh/Oj1WXaMjDr1eWscbDL+m7pPnz5ERESYa3L2799Pu3btaN68ObNnz8bLy4vNmzdTr14982vefPNN4uLi+OOPP9i6dStNmzZlxYoVdOzY0bxNkyZNMBqN7NixA1BrbVxdXWnfvj1//PEHer2e0NBQ/Pz82LNnD3Xr1k03vkGDBnHr1i1zbVKfPn3YunUrFy9eNCcIXbt2Ra/Xs2jRIi5evEipUqW4fv06/v7+5v20aNGC2rVrM378eObOnUvfvn25cOECJUqUAGDGjBl8/vnnhIaGplsuGS2/R88boHbt2jRr1oyvvvqKDRs20LZtW06fPk3p0qXT7K9Hjx7cvn2bDRs2mNeNGDGCNWvWcPLkSUCt2WnYsCHz588HMJfjqFGj+PzzzwHYu3cv9erVIyQkBF9fX3r16oWdnR0TJ07ExcUFvV7Pzp07ady4MbGxseb3bar03tOpMvr9LTU7Qoiste3rB4nOi5OhSjcwOMDDv/AVBRKjIPYOxNxPfHR69aa3Uu+dfNQaG6snfEx1mQlz2kLoMfijK/TfAPbu2Xt+IlutXr0aJycnUlJSSE5OpmPHjnz//fecOnWKhIQEWrZsabF9UlIS1apVs1hXs2bNNPutXLmyednKygpPT0/Kly9vXufj4wNgnpoA4KeffmLmzJlcuXKF+Ph4kpKSqFq1qsV+K1SoYDGqr5+fH8ePHwfg8OHDKIqSJpFITEy0GN3awcHBnOik7uPhOJ7Hw+f96L6PHj1KkSJF0k10AE6fPm2RNAI0aNCAadOmYTQazef98DFSy7FSpUpp1oWFheHr68uhQ4e4cOECf/zxh3kbRVEwmUwEBwdTrly5Zz3dx5JkRwiRdXZ9B1snqMttvoJab6a/nU4Hdq7qzbNE+ttkhK0TdF8CM1vAnXOw+A3ouQysbZ59n/mQvcGKU5+31uS4mdW0aVN+/PFHDAYD/v7+5lqv4OBgANasWUPhwoUtXmNra2vx2NHRMc1+H609S+3x9fBjUKcxAFiyZAkffvghkydPpl69ejg7OzNp0iT27dv31P2m7sNkMmFlZcWhQ4fSTHPg5OT0xH1k1UWXJ8WXOhXD4yiKkuYyZHpxpVeOTypbk8nE22+/Td++fdPMjVW0aNGnntOzkGRHCJE19v8KG0epy81GQd0BOXNcFz/osURtuHx5B6x4V61RkhoeM51Ol6nLSVpydHSkZMmSadaXL18eW1tbrl69atE+J7vs2LGD+vXrM3DgQPO6ixcvZmof1apVw2g0EhYWRsOGDZ85FhsbG4zGrG9zVblyZa5fv865c+fSrd0pX748O3futFi3e/duSpcu/cxzVAFUr16dU6dOUbx4cfNlrOwmvbGEEM/vv0Wwdri63HAYNBqes8f3qQBd54LOCk4shakVYcNnai8wkS84OzszfPhwPvzwQ+bNm8fFixc5cuQIP/zwA/Pmzcvy45UsWZKDBw+yfv16zp07x6hRozhw4ECm9lG6dGl69OhBr169WLZsGcHBwRw4cICvv/6atWvXZng/QUFBHDt2jLNnz3Lnzp3nmiPqYY0bN6ZRo0Z06dKFjRs3EhwczD///MO6desAGDZsGJs2beKLL77g3LlzzJs3j+nTpzN8+PP9f48cOZI9e/YwfPhwjh49yvnz5/n7778ZPHhwVpxWuiTZEUI8n/P/wspB6nKdAWqtjhZKtoBui8C7AiTFwO7v4dvK8Pf7EB6sTUwiS33xxReMHj2aCRMmUK5cOVq3bs2qVasoVizrG6a/++67dO7cmddee406depw9+5di1qejJozZw69evVi2LBhlClThpdeeol9+/YREBCQ4X289dZblClThpo1a1KoUCF27dqV6TgeZ+nSpdSqVYtu3bpRvnx5RowYYa5Fql69OkuWLGHRokVUrFiR0aNH8/nnn1v0xHoWlStXZsuWLVy6dInGjRtTrVo1Ro0ahZ+fXxacUfqkNxbSG0sr0htLW1lS/jcOwdwOkBwLlbrCyz9DDlRJP5GiwPkNsHMqXN2jrrNxVmt+SrbQNLSH5WRvLJGWyWQiKioqxy6jCEuZKf+s6I0lf2EhxLO5exF+76omOsWbQMcftE90QG38XLo19FsHfddBQF1IilZjPThb6+iEEBrIBZ9MQog8JyYMFnSGuDvgVwVeW5A7e0AF1oPeq6BKd1CMsPpDtS3P/V4hQoiCQZIdIUTmJETB76/AvcvqQH/d/1QHDMytrG2g0wxo+qn6ePf38Gfvp09XIYTINyTZEUJkXGIM/P4qhPwHDp7wxnJw9tE6qqfT6aDxCOj8K1jZwOm/YWZzuHlU68iEEDlAkh0hRMYkxcEfr8G1vWDrCj2XPt+AgFqo3BV6rVSnlwg7pSY8W78GY9Z05RVC5E6S7Aghni45HhZ1gys71Z5NbywD/2pPf11uFFgfBu2Dci+pE4luHa+OwBx2WuvIhBDZRJIdIcSTpSTC4p5waSsYHNUanSJp5x7KUxy9oOtv0GUW2LlByFH4uREcmKV2XRdC5CuS7AghHs9kgj/7woV/wdoeevwJRetoHVXW0Omg0iswcC+UaqVORrpmKKz6QE3whBD5hiQ7QojHO78ezq4BK1vovgiCGmgdUdZz8VMnE20xFtDB4Xkwt71MNSFEPiLJjhAifYoCO6aoy3XfVQcOzK90OnjhQ+jxlzoT+/X98EsTuJa5uZBE3jB37lw8PDy0DkPkIEl2hBDpu7pH/dK3soW6mZ8TKE8q1QLe2gKFykFMKMxtp879JXJEnz590Ol0vPvuu2meGzhwIDqd7rnnZcoKW7duRafTERERoXUoIoMk2RFCpC+1Vqdqd3D21TaWnORZAt7cCGVeVNvxLOkFNw5rHVWBERAQwKJFi4iPjzevS0hIYOHChRQtWvS5959VM4Y/C6PRiCmXjt6tZbnkBEl2hBBphR6HCxtBp4cG72sdTc6zdYZX56qX7pJj4Y+uEH5J66gKhOrVq1O0aFGWLVtmXrds2TICAgKoVs1yuIN169bxwgsv4ObmhqenJ+3bt+fixYvm5y9fvoxOp2PJkiU0adIEOzs7FixYkOaYd+/epXbt2rz00kskJCSgKAoTJ06kePHi2NvbU6VKFf766y/zPps2bQqAu7v7E2ub5s6di5ubG6tXr6Z8+fLY2tpy5coVkpKSGDFiBIULF8bR0ZE6deqwdetW8+uuXLlChw4dcHd3x9HRkQoVKrB27Vrz89u2baN27drY2tri5+fH//73P1JSUszPBwUFMW3aNItYqlatytixY82PdTodP/30Ex07dsTR0ZEvv/wSgL///puaNWtiZ2eHl5cXnTt3Nr/meePWkiQ7Qoi0dk5V7yu8DB7FtY1FK9Y20HU++FaC2NuwoAvE3tE6qmejKJAUm/O3Z+zG37dvX+bMmWN+PHv2bPr165dmu9jYWIYOHcqBAwfYtGkTer2el19+OU3tyciRI3n//fc5ffo0rVu3tnju+vXrNGzYkLJly7Js2TLs7Oz47LPPmDNnDj/++CMnT57kww8/pGfPnmzbto2AgACWLl0KwNmzZwkJCeHbb7997LnExcUxYcIEZs6cycmTJ/H29qZv377s2rWLRYsWcezYMV599VXatGnD+fPnARg0aBCJiYls376d48eP8/XXX+Pk5ATAjRs3aNeuHbVq1eK///7jxx9/ZNasWeZkJTPGjBlDx44dOX78OP369WPNmjV07tyZF198kSNHjrBp0yZq1nwwzMTzxK01a60DEELkMuGX4ORydbnBEE1D0ZydC/RYCrNaqOXyR1d1YlEbR60jy5zkOBjvn/PH/eTmM5XVG2+8wccff2yumUn9gn24FgGgS5cuFo9nzZqFt7c3p06domLFiub1Q4YMsaihSHXhwgW6dOlCx44d+fbbb9HpdMTGxjJlyhQ2b95MvXr1AChevDg7d+7k559/pnHjxubGzd7e3ri5uT3xXJKTk5kxYwZVqlQB4OLFiyxcuJDr16/j76/+TYYPH866deuYM2cO48eP5+rVq3Tp0oVKlSqZj59qxowZBAQEMH36dHQ6HWXLluXmzZuMHDmS0aNHo9dnvA6je/fuFklkt27deP311xk3bpx5XVbFrTVJdoQQlnZ9B4oJSrYEv8paR6M9Zx/ouQxmtYQbh9Rxh7otBL2V1pHlW15eXrz44ovMmzcPRVF48cUX8fLySrPdxYsXGTVqFHv37uXOnTvmGp2rV69aJDsP106kio+Pp23btnTr1o3vvvvOvP7UqVMkJCTQsmVLi+2TkpLSXEbLCBsbGypXfvB/dPjwYRRFoXTp0hbbJSYm4unpCcD777/PgAED2LBhAy1atKBLly7mfZw+fZp69eqh0+nMr23QoAExMTFcv349U+2aHi2Xo0eP8tZbb6W77fPGrTVJdoQQD0SHwtHf1eUXPtQ2ltzEq5Q6Fs+8DurYQ5u/uD8uTx5hcFBrWbQ47jPq168f7733HgA//PBDutt06NCBgIAAfv31V/z9/TGZTFSsWJGkpCSL7Rwd09Yu2dra0rhxY9auXcuIESMoUqQIgDlhWrNmDYULF07zmsyyt7e3SExMJhNWVlYcOnQIKyvLhDn1ks+bb75J69atWbNmDRs2bGDChAlMnjyZwYMHoyiKxf4AlPuXC1PX6/V687pU6TVAfrRc7O3tH3sezxu31qTNjhDigb0z1B5IRWqrc0iJBwJqQ6cZ6vLOqXBqpbbxZIZOp15OyunbI1/KmdGmTRuSkpJISkpK084G1EbFp0+f5rPPPqN58+aUK1eOe/fuZXj/er2en3/+merVq9OsWTNu3lSTwdSGxFevXqVkyZIWt4CAAECtrQG1d1VmVatWDaPRSFhYWJr9+/o+6PUYEBDAu+++y7Jlyxg2bBi//vqrOb7du3dbJDO7d+/G2dnZnJwVKlSIkJAHg2JGRUURHBz81NgqV67Mpk2bsiVurUmyI4RQxd5R54YCtVbnOb6o8q2KXaCeWtvA8gEyeWg2srKy4vTp05w+fTpNTQKoPaE8PT355ZdfuHDhAps3b2bo0KGZPsaCBQuoUqUKzZo1IzQ0FGdnZ4YPH86HH37IvHnzuHjxIkeOHOGHH35g3rx5AAQGBqLT6Vi9ejW3b98mJiYmw8csXbo0PXr0oFevXixbtozg4GAOHDjA119/be65NGTIENavX09wcDCHDx9m8+bNlCtXDlDHG7p27RqDBw/mzJkzrFy5kjFjxjB06FBze51mzZoxf/58duzYwYkTJ+jdu3e6ZfioMWPGsHDhQsaMGcPp06c5fvw4EydOzJK4tSbJjhBCtWMKJMWAXxUo3UbraHKvFuOgWCO1S/qiHhAfoXVE+ZaLiwsuLi7pPqfX61m0aBGHDh2iYsWKfPjhh0yaNCnTx7C2tmbhwoVUqFCBZs2aERYWxhdffMHo0aOZMGEC5cqVo3Xr1qxatYpixYoBULhwYcaNG8f//vc/fHx8zJfbMmrOnDn06tWLYcOGUaZMGV566SX27dtnrjkyGo0MGjSIcuXK0aZNG8qUKcOMGTPMx167di379++nSpUqvPvuu/Tv35/PPvvMvP+PP/6YRo0a0b59e9q1a0enTp0oUaLEU+Nq0qQJf/75J3///TdVq1alWbNm7Nu3L0vi1ppOefTCXgEUFRWFq6srkZGRj/3HElkvOTmZtWvX0q5dOwwGg9bhFDgW5R93C76rDsZEdVbzki20Di93i72jTicReU1NDF9fCJnoBQPZ9/5PSEggODiYYsWKYWdnl2X7zW9MJhNRUVG4uLhkqgeTyBqZKf8nvacz+v0tf2EhBGz7Wk10AhtAieZaR5P7OXrBa/PVqTTOrYPtE7WOSAjxBJLsCFHQ3b0AR+73wGo+RtrqZJR/NWh/f/DFrRPg7Dpt4xFCPJYkO0IUcFbbJoBiVC/HFK2jdTh5S7UeUOtNdXnZ23D34pO3F0JoQpIdIQow17jL6E/f70LdbJS2weRVrSdAQB1IjITFPSEx4z1zhBA5Q5IdIQqwciHq5IZUfAV8Kz55Y5E+axt4dR44+UDYKfh78DPPCSWEyB6S7AhRQOmu7sEn6hiK3hqafqJ1OHmbix90/Q301nByGexJf8RfIYQ2JNkRoiAypqDfqI7LYarSAzyfPgaHeIqidaHNV+ryxtEQvF3beIQQZpLsCFEQ7fsJfeh/JFs5YGr4kdbR5B+13oQq3dQG36s/BGOK1hEJIchFyc6ECRPQ6XQMGTLEvE5RFMaOHYu/vz/29vY0adKEkydPWrwuMTGRwYMH4+XlhaOjIy+99BLXr1/P4eiFyEPCg2HzlwCc8H8dnH2f8gKRYTodtJsE9h5ql/4Tf2kdkRCCXJLsHDhwgF9++SXNVPATJ05kypQpTJ8+nQMHDuDr60vLli2Jjo42bzNkyBCWL1/OokWL2LlzJzExMbRv3/6ZJmgTIt9TFLXGISUeU2ADrno21jqi/MfWGerfn+V520Sp3RHPTafTsWLFCq3DyNM0T3ZiYmLo0aMHv/76K+7u7ub1iqIwbdo0Pv30Uzp37kzFihWZN28ecXFx/PHHHwBERkYya9YsJk+eTIsWLahWrRoLFizg+PHj/Pvvv1qdkhC517HFcGkLWNlibDdFBhDMLrXfVmt3wi/C8T+1jiZPCQ0NZfDgwRQvXhxbW1sCAgLo0KHDY2fjzk/Gjh1L1apV06wPCQmhbdu2OR9QPqJ5sjNo0CBefPFFWrSwnIsnODiY0NBQWrVqZV5na2tL48aN2b17NwCHDh0iOTnZYht/f38qVqxo3kYIcV/MbVj3P3W5yUjwkEbJ2cbWCRq8ry5vl9qdjLp8+TI1atRg8+bNTJw4kePHj7Nu3TqaNm3KoEGDtA5PM76+vtja2modRp5mreXBFy1axOHDhzlw4ECa50JDQwHw8fGxWO/j48OVK1fM29jY2FjUCKVuk/r69CQmJpKYmGh+HBUVBagT8yUnJz/byYhMSy1rKfOcYfXPSPTx91C8K5BSa4CUf3ar1gfr3d+jC79EytGFKJVft3g6u8o/OTkZRVEwmUyYTKYs3Xd2GzBgADqdjr179+Lo6GheX65cOfr06YPJZOLq1au8//77bN68Gb1eT+vWrfnuu+/M3xXjxo1j5cqVfPjhh4wZM4Z79+7Rpk0bfvnlF5ydnQH466+/+OKLL7hw4QIODg5Uq1aN5cuX4+joSLNmzahSpQpTp041H//ll1/Gzc2NOXPmAFC8eHH69+/PuXPnWL58OZ6enkybNo369evz1ltvsXnzZooVK8asWbOoWbMmAHPnzmXo0KHMnj2b//3vf1y9epUXXniBWbNmERAQwNy5cxk3bhygXrYCmDVrFn369MHKyoqlS5fSqVMnAI4fP86HH37Inj17cHBwoHPnzkyePBknJycA+vbtS0REBC+88AJTpkwhKSmJ1157jalTp+aaSZdT5yBPfa8+iclkQlEUkpOTsbKysnguo/8/miU7165d44MPPmDDhg1PnJlX90g1u6IoadY96mnbTJgwwfymetiGDRtwcHB4SuQiq23cuFHrEPK9QlHHqX9xKQo6tru9SsT6B2Uu5Z99Srq1oELcYhLWf87ma44oOqs022R1+VtbW+Pr60tMTAxJSUmA+pmYYEzI0uNkhJ2V3VM/r1Pdu3eP9evX89lnn2E0Gs0/QlPp9XoiIyPp2LEjDg4OrF69mpSUFIYPH86rr77K6tWrAfXH7MWLF1m6dCl//PEHERER9OvXj88//5xRo0YRGhpKjx49GDduHO3btyc6Opo9e/YQGRmJ0WgkJSWFpKQki+OnpKSQnJxsXmcymZg6dSqjRo1iyJAhzJgxg969e1O7dm169uzJ6NGjGTt2LL169WLPnj3odDoSEhKIi4vjyy+/ZPr06djY2DB8+HC6du3K+vXradu2Le+99x7//vuvuX2Oi4uL+Zjx8fFERUURFxdH27ZtqVmzJps2beLOnTu8//77vPvuu8yYMQNQE4AtW7bg6enJypUruXTpEv3796dMmTL07t37uf6mWe3hNriPk5SURHx8PNu3byclxbKWNC4uLkPH0SzZOXToEGFhYdSoUcO8zmg0sn37dqZPn87Zs2cBtfbGz8/PvE1YWJg5g/f19SUpKYl79+5Z1O6EhYVRv379xx77448/ZujQoebHUVFRBAQE0KpVqydOES+yVnJyMhs3bqRly5a55tdGfmX1+0wATDXfpH7r9wAp/xyR1Bjlh004xYXRrkgMSpVu5qeyq/wTEhK4du0aTk5O5h+ScclxNFrUKMuOkVF7Xt+DgyFjPyDPnDmDoihUqVLlsZ/DGzdu5OTJk1y8eJGAgAAAFixYQKVKlTh79iy1atXC1tYWk8nE/PnzzTU5b7zxBjt27MDFxYULFy6QkpLC66+/joeHB87OztSrV898DGtra2xsbCxisLa2xmAwmNfp9XratWvHBx98AMAXX3zB7NmzqVevHr169QLgk08+oUGDBsTHx+Pr64udnR3Jycn88MMP1KmjzkH322+/UaFCBc6cOUPt2rXx8PDA1taWUqVKpTl3e3t7XFxcWLx4MQkJCfz+++/m2i+9Xk/Hjh2ZPHkyPj4+GAwGPDw8+Pnnn7GysqJmzZosXbqU3bt3M3jw4Az9PbKboihER0fj7Oz81IQ4ISEBe3t7GjVqlKZy5NGk+HE0S3aaN2/O8ePHLdb17duXsmXLMnLkSIoXL46vry8bN26kWrVqgJrdbdu2ja+//hqAGjVqYDAY2LhxI127dgXUhlwnTpxg4sSJjz22ra1tutc/DQaDfOhrQMo9m925AJe3AzqsGgzG6pGylvLPRgY3aPABbByF9a7JUK0bWGVv+RuNRnQ6HXq9Hr1ebZaZep/THo7haVK/8KysrB77mrNnzxIQEEBgYKB5XcWKFXFzc+Ps2bPUqVMHnU5HUFAQrq6u5m38/f0JCwtDr9dTrVo1mjdvTtWqVWnWrBlt27ala9euFj+YU8vv4cePrqtSpYr5ceoP8sqVK6dZd+fOHfz9/dHr9VhbW1O7dm3zNuXLlzfHXrduXXMZpHf+qWV59uxZqlSpYk7kABo2bIjJZOL8+fP4+fmh0+moUKGCxfvK39+f48ePa/ZeeFTqpatHyzU9er0enU6X7v9KRv93NEt2nJ2dqVjRci4eR0dHPD09zeuHDBnC+PHjKVWqFKVKlWL8+PE4ODjQvXt3AFxdXenfvz/Dhg3D09MTDw8Phg8fTqVKldI0eBaiwDqktjOgVEtwD3zytiLr1eoPu7+De5fh8LwHs6TnIHtre/Z136fJcTOqVKlS6HQ6Tp8+bW6b8qjHNVF4dP2jX4A6nc785WplZcXGjRvZuXMnq1ev5ocffmDUqFHs27ePYsWKodfrze1JUqXXLuThY6QeO711j7ZHSS/+jF7qgyc308hoGRREuSPFe4wRI0YwZMgQBg4cSM2aNblx4wYbNmywyGinTp1Kp06d6Nq1Kw0aNMDBwYFVq1alacQkRIGUHA9HFqjLNftrG0tBZeMIDYeryxtGqzVtOUyn0+FgcMjxW2a+xD08PGjdujU//PADsbGxaZ6PiIigfPnyXL16lWvXrpnXnzp1isjISMqVK5ep8mjQoAEff/wxhw4dwsbGhuXLlwNQqFAhQkJCzNsajUZOnDiR4X0/SUpKCgcPHjQ/Pnv2LBEREZQtWxYAGxubp44RV758eY4ePWpRRrt27UKv11O6dOksiTM/ylXJztatW5k2bZr5sU6nY+zYsYSEhJCQkMC2bdvS1AbZ2dnx/fffc/fuXeLi4li1apX5Wq4QBd7J5ZAQAa5F1ZodoY3ab0NQQ0iOhaX9ISVJ64hypRkzZmA0GqlduzZLly7l/PnznD59mu+++4569erRokULKleuTI8ePTh8+DD79++nV69eNG7c2Nzr6Wn27dvH+PHjOXjwINeuXWPZsmXcvn3bnCw1a9aMNWvWsGbNGs6cOcPAgQOJiIjIkvMzGAwMHjyYffv2cfjwYfr27UvdunWpXbs2AEFBQQQHB3P06FHu3Llj0Ws4VY8ePbCzs6N3796cOHGCLVu2MHjwYN544400vZfFA7kq2RFCZLEDs9T7Gr1BL7WdmtHr4eWfwc4NQo7Clv/TOqJcqVixYhw+fJimTZsybNgwKlasSMuWLdm0aRM//vijeSRhd3d3GjVqRIsWLShevDiLFy/O8DFcXFzYvn077du3p1atWowePZrJkyebB+3r168fvXv3NidRxYoVo2nTpllyfg4ODowcOZLu3btTr1497O3tWbRokfn5Ll260KZNG5o2bUqhQoVYuHBhuvtYv3494eHh1KpVi1deeYXmzZszffr0LIkxv9Ipj16cLICioqJwdXUlMjJSemPloOTkZNauXUu7du2kgWx2CPkPfm4EegMMPQVO3hZPS/lr4NTfsOQNQEdKj2WsORWd5eWfkJBAcHAwxYoVe+KwHgWdyWQiKioKFxeXHGm0O3fuXIYMGZJltUR5XWbK/0nv6Yx+f0vNjhD51cHZ6n25DmkSHaGR8i9B9d6AgtXfAzGkPH2MESHE85NkR4j8KCEKjt2fk6mWNEzOVdpMAM9S6KJDqHp1tjo5qxAiW0myI0R+dGyx2hi2UFkIbKB1NOJhNo7QZSaK3oB/5CF0//2udUQiB/Tp00cuYWlIkh0h8htFedAwuWY/mdk8N/KviqnJxwBYbfwUwoM1DkiI/E2SHSHym8s74fZpMDhA5de0jkY8hqnOIO44lkGXFAsrBoDpyeOrZJb0PRH5RVa8lyXZESK/2Xl/tuYqr4O9m6ahiCfQW3Ek8G0UG0e4ugd2f58lu03t2ZXRCRKFyO1S38vP02tRs+kihBDZ4MZhuLgJdFZQ/32toxFPEWdbCGPL8Viv+UAde6dkC/Ct+PQXPoGVlRVubm6EhYUB6rgsmRnJuKAwmUwkJSWRkJCQa+aLKkgyUv6KohAXF0dYWBhubm7PNTOCJDtC5Cc7p6j3lV4Bj2LaxiIyRKnSHS6sh7NrYdnb8PYWsE47UXFm+Pr6ApgTHpGWoijEx8djb28vyaAGMlP+bm5u5vf0s5JkR4j8IuwMnF6lLr8wVNtYRMbpdNDhO7i2H8JOqjU8LT9/zl3q8PPzw9vbO91JLIU6qOb27dtp1KiRDKqpgYyWv8FgyJK5LiXZESK/SG2rU7Y9eJfVNhaROU6F4KXvYFF32PUdVO0Bhco8926trKxkUuTHsLKyIiUlBTs7O0l2NJDT5S8XKoXID+5dhuP3BxFsOEzTUMQzKvsilHkRUGDHFK2jESJfkWRHiPxg13egGKFEMyhcXetoxLNqNFy9P/6njL0jRBaSZEeIvC46FI4sUJelVidvK1xd7ZGlGGHXNK2jESLfkGRHiLxuz3QwJkJAXZkaIj9oeL925+gfEHlD21iEyCck2REiL4uPgAP3ZzdvOEymhsgPAutB4AtgTMqygQaFKOgk2REiL/tv0f0JP8tBqZZaRyOySqP7lyMPzYWY25qGIkR+IMmOEHmVosDB+7U6tfpLrU5+UrwpFK4BKfGw9wetoxEiz5NkR4i86souuHMWDI4y4Wd+o9NBo4/U5f2/Qly4tvEIkcdJsiNEXnVglnpf+VWwc9E2FpH1SrcBn4qQFAP7f9E6GiHyNEl2hMiLYsIeTA1Rs7+2sYjsodM9GEpg30+QJLOYC/GsJNkRIi86Mh9MyVCkFvhV1joakV3KdwS3QIi/B8eXaB2NEHmWJDtC5DUmIxycqy7X7KdpKCKb6a2g9tvq8t6f1EbpQohMk2RHiLzmwr8QeRXs3KDCy1pHI7JbtZ5qI/TbpyF4u9bRCJEnSbIjRF6T2jC5Wk8w2Gsbi8h+9m5QtZu6vO8nTUMRIq+SZEeIvCTiKpzfoC7X6KttLCLn1HlXvT/7D4Rf0jYWIfIgSXaEyEsOzQUUKNYYvEpqHY3IKV6l1AlCUdRxd4QQmSLJjhB5RXI8HJqnLteS7uYFTp0B6v2RBZAYrW0sQuQxkuwIkVccWQBxd8CtKJRpp3U0IqeVaAaeJSExCo4u1DoaIfIUSXaEyAuMybDrO3W5/vtgZdA2HpHz9PoHbXf2/QQmk7bxCJGHSLIjRF5wcrna3dzBS+2FJQqmKt3A1hXCL6pDEAghMkSSHSFyO5MJdk5Vl+sOkO7mBZmtE1R/Q13e8Y0MMihEBkmyI0Rud34DhJ0CG2eo9abW0Qit1R0IBge4tg+O/6V1NELkCZLsCJHbpdbq1OqnDjAnCjbXwg8mCN04ChJjtI1HiDxAkh0hcrMru+HaXrCyUX/RCwFQ7z1wD4LoEPVylhDiiSTZESI3S63VqdodnH21jUXkHgY7aPOVurznB7h7Udt4hMjlJNkRIrcKPa6219Hp1e7mQjysdBt1VGVjEqz7WOtohMjVJNkRIrdKHVenfCfwLKFpKCIX0unU2h29Ac6vh3PrtY5IiFxLkh0hcqOYMHVsHYD672kbi8i9vEqpwxEArPsfpCRqG48QuZQkO0LkRofngSkZCtdQb0I8TqOPwMlHnQ394GytoxEiV5JkR4jcxpgCB+eoy7Xf1jYWkfvZuUDjkery/l9lGgkh0iHJjhC5zbl/IOoGOHiq7XWEeJrKr6mDToZfhOBtWkcjRK4jyY4Quc3+X9T76r3ULsZCPI2tE1R5XV0+OEvbWITIhSTZESI3uX0Wgrer3c1r9tM6GpGX1Oqv3p9ZC1E3tY1FiFxGkh0hcpMDM9X70m3Arai2sYi8xbscFK0PihEO/6Z1NELkKpLsCJFbJEbD0YXqskz4KZ5Fam3goXlqQ3chBCDJjhC5x7HFkBQNniWheFOtoxF5UfmXwMELom/CuXVaRyNEriHJjhC5gaLA/vuXsGq9CXr51xTPwNoWqvVUl6WhshBm8okqRG5weSfcPg0GB6jSTetoRF5Wsy+gg4ubZYJQIe6TZEeI3OC/Rep9pVfB3k3TUEQe5x6kThAKcGiOpqEIkVtIsiOE1lKS4MwqdblyV21jEflDajf0I79DcoK2sQiRC0iyI4TWLm2FhEh1fqOi9bSORuQHpVqBSxGID4dji7SORgjNSbIjhNZOLlPvy3cEvZW2sYj8QW8F9QaqyzsmgzFZ23iE0JgkO0JoKSURzqxRlyu8rG0sIn+p0RccC0HEVXVYAyEKMEl2hNDSxc2QGAXOfhBQV+toRH5i4wD131eXt38jgwyKAk2SHSG0dHK5el++k4ytI7Jerf7g4An3guH4n1pHI4Rm5NNVCK0kJ6iTNoJcwhLZw8YR6g9Wl7dPApNR23iE0MgzJTt37tzh4MGDHDp0iLt372Z1TEIUDBf+VaeHcCkMRWppHY3Ir2q9BfYeEH4RTizVOhohNJGpZOfkyZM0atQIHx8f6tSpQ+3atfH29qZZs2acPXs2u2IUIn+SS1giJ9g6Qb1B6rLU7ogCKsOfsKGhoTRu3Jjbt28zZcoU1q5dy5o1a5g0aRIhISE0bNiQsLCw7IxViPwjOR7O/qMuyyUskd1qvw12bnDn3IMkW4gCJMPJztSpUwkMDOTIkSN88MEHtG7dmjZt2jB06FAOHz5MQEAAU6dOzc5Yhcg/zm+E5FhwDYAiNbWORuR3di6P1O6YtI1HiByW4WRn48aNjBw5Ejs7uzTP2dvb89FHH7F+/fosDU6IfMt8Casj6HTaxiIKhjrvgK0r3D4DZ9doHY0QOSrDyc6lS5eoXr36Y5+vWbMmly5dypKghMjXkuLg3Dp1uWJnbWMRBYedK9R5W13eMRkURdt4hMhBGU52oqOjcXFxeezzzs7OxMTEZElQQuRrx5dAchy4FQX/x/+AECLL1RkABge4eQQubdE6GiFyTKa6gERHRxMVFfXYmyK/FIR4spQkdTRbgDrvyiUskbMcPaFGH3V5xxRNQxEiJ1lndENFUShduvQTn9fJB7cQT3ZkPkReAydfqNlP62hEQVTvPdj/K1zeAVf3QdE6WkckRLbLcM3Oli1b2Lx582Nvqc9nxo8//kjlypVxcXHBxcWFevXq8c8//5ifVxSFsWPH4u/vj729PU2aNOHkyZMW+0hMTGTw4MF4eXnh6OjISy+9xPXr1zMVhxA5IjlBbSsB0HAYGOy1jUcUTK6FoWo3dXmn1O6IgiHDNTuNGzfO8oMXKVKEr776ipIlSwIwb948OnbsyJEjR6hQoQITJ05kypQpzJ07l9KlS/Pll1/SsmVLzp49i7OzMwBDhgxh1apVLFq0CE9PT4YNG0b79u05dOgQVlZWWR6zEM/s8G8QdUMdMbl6L62jEQVZgyFwZIHaUD70OPhW0joiIbJVhmt2TCYTKSmWs+beunWLcePGMWLECHbu3Jnpg3fo0IF27dpRunRpSpcuzf/93//h5OTE3r17URSFadOm8emnn9K5c2cqVqzIvHnziIuL448//gAgMjKSWbNmMXnyZFq0aEG1atVYsGABx48f599//810PEJkm+T4R2p10g7hIESO8SzxYDDLnTI+msj/Mlyz079/fwwGA7/88gugNlauVasWCQkJ+Pn5MXXqVFauXEm7du2eKRCj0ciff/5JbGws9erVIzg4mNDQUFq1amXextbWlsaNG7N7927eeecdDh06RHJyssU2/v7+VKxYkd27d9O6det0j5WYmEhiYqL5cVRUFADJyckkJyc/U/wi81LLuiCUuX7/LKxiQlFcipBS6XXIBedckMo/N9K8/Ou+j+HEUpSTy0lpOAI8SmgTh0Y0L/8CLqvKP6Ovz3Cys2vXLqZPn25+/Ntvv5GSksL58+dxdXVl5MiRTJo0KdPJzvHjx6lXrx4JCQk4OTmxfPlyypcvz+7duwHw8fGx2N7Hx4crV64A6hQWNjY2uLu7p9kmNDT0scecMGEC48aNS7N+w4YNODg4ZCp+8fw2btyodQjZysqUSIuTE7EC/nNtyZX1uavWMb+Xf26nZfnXcamKb9RRbi4ZwdGi/TWLQ0vy/tfW85Z/XFxchrbLcLJz48YNSpUqZX68adMmunTpgqurKwC9e/dmzpw5mQwTypQpw9GjR4mIiGDp0qX07t2bbdu2mZ9/tIdXRnp9PW2bjz/+mKFDh5ofR0VFERAQQKtWrZ44lpDIWsnJyWzcuJGWLVtiMBi0Difb6Pf+gFVKJIpbIBV6jKeCVe4414JS/rlVbih/3XUvmNeOohF78O/1Czh4ahKHFnJD+RdkWVX+qVdmnibDyY6dnR3x8fHmx3v37mXSpEkWzz/LoII2NjbmBso1a9bkwIEDfPvtt4wcORJQa2/8/PzM24eFhZlre3x9fUlKSuLevXsWtTthYWHUr1//sce0tbXF1tY2zXqDwSBveg3k63JPjIE93wOgazwCg13uqznM1+WfB2ha/kH1wa8KupD/MJxYAg3e1yYODcn7X1vPW/4ZfW2GGyhXqVKF+fPnA7Bjxw5u3bpFs2bNzM9fvHgRf3//TIaZlqIoJCYmUqxYMXx9fS2quJKSkti2bZs5kalRowYGg8Fim5CQEE6cOPHEZEeIHLP/Z4i7Ax7FofLrWkcjhCWdDmrev3x1cLZMECryrQzX7IwaNYp27dqxZMkSQkJC6NOnj0WNy/Lly2nQoEGmDv7JJ5/Qtm1bAgICiI6OZtGiRWzdupV169ah0+kYMmQI48ePp1SpUpQqVYrx48fj4OBA9+7dAXB1daV///4MGzYMT09PPDw8GD58OJUqVaJFixaZikWILBcfAbu+VZebfAxWGf53EyLnVHoFNnwG94IheCuUaPbUlwiR12T407dp06YcPHiQf//9F19fX1599VWL56tWrUrt2rUzdfBbt27xxhtvEBISgqurK5UrV2bdunW0bNkSgBEjRhAfH8/AgQO5d+8ederUYcOGDeYxdgCmTp2KtbU1Xbt2JT4+nubNmzN37lwZY0dob890SIiEQuWgYhetoxEifTaOUOV12P8LHJglyY7IlzL1U7NChQpUqFAh3efefvvtTB981qxZT3xep9MxduxYxo4d+9ht7Ozs+P777/n+++8zfXwhsk3sHdj7o7rc7FPQS/ItcrGa/dRk5+w/EHUTXJ6/SYIQuUmGk53ffvst3fWurq6UKVOGsmXLZllQQuR5O6dCUgz4VYWy7bWORogn8y4HgQ3gyi51pO8m/9M6IiGyVIaTnQ8++CDd9TExMZhMJtq1a8cff/xhcYlJiAIpKgQOzFSXm42Smc1F3lCzn5rsHJoHDYdLGzORr2S4N9a9e/fSvSUmJrJ3716uXr2a7kB9QhQ42ydBSgIE1IWSzbWORoiMKdcBHLwg+qY6Z5YQ+UiGk53H7kCvp1atWkyePJlVq1ZlRUxC5F33LquXAQCaS62OyEOsbaFaT3X54JPbUwqR1zx3spOqZMmSXL9+Pat2J0TetG0SmJKheBMIekHraITInJp9AR1c3Ax3L2odjRBZJsuSnYsXL1KkSJGs2p0QeU9cOBxfoi43/UzbWIR4Fu5BUPL+GGUHZ2saihBZ6bmTHUVROHz4MMOGDaNDhw5ZEZMQedPxv8CYBL6VIKCW1tEI8WxqvaneH/4NEjI275AQuV2Gm9u7u7unO7lmTEwMRqORNm3aPHE8HCHyvaML1PuqPbWNQ4jnUaoVeJWBO2fh0NwCOV+WyH8ynOxMmzYt3fUuLi6ULVuWcuXKZVVMQuQ9oScg5D/QG6DSq0/fXojcSq9XE5yVg2DvDKjzjtp4WYg8LMPJTu/evbMzDiHytqO/q/dl2oKjp7axCPG8Kr0Km7+E6BA4/ueDXlpC5FFZ1kBZiAIrJQmOLVaXq72hbSxCZAVrW6g7QF3e9Z3Mhi7yPEl2hHhe59ZB3F1w8pVJFEX+UaMv2LqobXfOr9c6GiGeiyQ7Qjyv1EtYVV6XIfZF/mHnok4hAbBzmqahCPG8MpTsHDt2DJNUYwqRVvQtOL9RXZZ2DSK/qfMuWNnAtb1wda/W0QjxzDKU7FSrVo07d+4AULx4ce7evZutQQmRZxxbBIoRitQGr1JaRyNE1nLxg8qvqcu7vtM2FiGeQ4aSHTc3N4KDgwG4fPmy1PIIAaAocOT+JaxqPbSNRYjs0uADQAdn18Dtc1pHI8QzyVADgy5dutC4cWP8/PzQ6XTUrFkTKyurdLe9dOlSlgYoRK5145DaeNPaHip01joaIbKHVyko005Ndg7OhrZfaR2REJmWoWTnl19+oXPnzly4cIH333+ft956C2dn5+yOTYjcLXV28/IvqY05hcivqvdSk52Ty6H1/4E+/R+7QuRWGe460qZNGwAOHTrEBx98IMmOKNji76mDrQFUlwE3RT5XohnYuUJMKFzdA0EvaB2REJmS6a7nc+bMMSc6169f58aNG1kelBC53pEFkBwHPhUhsL7W0QiRvaxtoNz9iZ5PLNU2FiGeQaaTHZPJxOeff46rqyuBgYEULVoUNzc3vvjiC2m4LAoGkxH2/6Iu13kH0pkgV4h8p2IX9f7USjAmaxuLEJmU6RHQPv30U2bNmsVXX31FgwYNUBSFXbt2MXbsWBISEvi///u/7IhTiNzj3DqIuAr27jLppyg4ghqBgxfE3YHgbVCyhdYRCZFhmU525s2bx8yZM3nppZfM66pUqULhwoUZOHCgJDsi/9v3k3pfow8Y7DUNRYgcY2UNFTrBgZlwYpkkOyJPyfRlrPDwcMqWLZtmfdmyZQkPD8+SoITItW6dguDtoLOCmv21jkaInJV6Kev0KkhJ1DYWITIh08lOlSpVmD59epr106dPp0qVKlkSlBC5VmpbnXLtwS1A21iEyGkBdcHZHxKj4MK/WkcjRIZl+jLWxIkTefHFF/n333+pV68eOp2O3bt3c+3aNdauXZsdMQqRO8Tfg/8Wqcu139E2FiG0oNdDxc6wZ7raK6vsi1pHJESGZLpmp3Hjxpw7d46XX36ZiIgIwsPD6dy5M2fPnqVhw4bZEaMQucPh+ZASDz6VpLu5KLgq3h8t/Ow/kBSrbSxCZFCma3YA/P39pSGyKFhMRtj/q7os3c1FQeZfHdyD4N5ltWdiajseIXKxTNfsCFEgnf0HIq+CvQdUekXraITQjk73IME5sUzbWITIIEl2hMiI/T+r9zV6S3dzIVKTnfMbICFS21iEyABJdoR4mrAz97ub66W7uRAA3uWhUFkwJkntjsgTJNkR4mlSu5uXaSfdzYUA9VJWtZ7q8uF52sYiRAZkOtmJj48nLi7O/PjKlStMmzaNDRs2ZGlgQuQKCZEPupvXke7mQphV6Q5WNnDzCNw8qnU0QjxRppOdjh078ttvvwEQERFBnTp1mDx5Mh07duTHH3/M8gCF0NTRPyA5FgqVgyAZWkEIM0fPBzOhS+2OyOUynewcPnzYPJ7OX3/9hY+PD1euXOG3337ju+++y/IAhdCMyfSgu3ntt6S7uRCPqtFHvT/2JyTGaBqKEE+S6WQnLi4OZ2dnADZs2EDnzp3R6/XUrVuXK1euZHmAQmjm4mYIvwi2LlD5Na2jESL3CWoIHsUhKRpOLtc6GiEeK9PJTsmSJVmxYgXXrl1j/fr1tGrVCoCwsDBcXFyyPEAhNJPaMLlqD7B10jYWIXIjnQ6q91aXD83VNBQhniTTyc7o0aMZPnw4QUFB1KlTh3r16gFqLU+1atWyPEAhNBF+SR1DBNRLWEKI9FXtAXoD3DgIoce1jkaIdGU62XnllVe4evUqBw8eZN26deb1zZs3Z+rUqVkanBCa2T8TUKBkC/AsoXU0QuReToUeTAh6SBoqi9zpmcbZ8fX1pVq1auj1D15eu3ZtypYtm2WBCaGZpFg4skBdltnNhXg6c0PlxZAU98RNhdBChiYC7dy5c4Z3uGyZjKYp8rgTSyExUp3ssGQLraMRIvcr1vjB5KAnl0O1HlpHJISFDNXsuLq6mm8uLi5s2rSJgwcPmp8/dOgQmzZtwtXVNdsCFSLHpDa0rNEX9DLIuBBPpddLQ2WRq2WoZmfOnDnm5ZEjR9K1a1d++uknrKysADAajQwcOFB6Y4m8L/Q43DikNrisKr9Ohciwqj1gy//B9f1w/RAUqaF1REKYZfpn6+zZsxk+fLg50QGwsrJi6NChzJ49O0uDEyLHpTawLPui2vBSCJExzj5Qqau6vHOKtrEI8YhMJzspKSmcPn06zfrTp09jMpmyJCghNJEUpzawhAcNLoUQGffCEEAHZ1ZD2BmtoxHCLEOXsR7Wt29f+vXrx4ULF6hbty4Ae/fu5auvvqJv375ZHqAQOebkckiMArdAtcGlECJzCpWBcu3h9CrYORU6/6x1REIAz5DsfPPNN/j6+jJ16lRCQkIA8PPzY8SIEQwbNizLAxQix5gbJveWhslCPKsXhqrJzvE/oenHai8tITSW6U90vV7PiBEjuHHjBhEREURERHDjxg1GjBhh0Y5HiDzl1im1YaXeGqr21DoaIfKuwtWhRDNQjLBLJocWucNz/Xx1cXGRHlgifzh8v2FymbZqQ0shxLN7Yah6f2QBRN/SNhYheIZk59atW7zxxhv4+/tjbW2NlZWVxU2IPCc5Hv5bqC5Lw2Qhnl/QC1CkNhgTYe8PWkcjRObb7PTp04erV68yatQo/Pz80Ol02RGXEDnn1EpIiATXolC8mdbRCJH36XTQcBgsfA0OzIIXPgR7d62jEgVYppOdnTt3smPHDqpWrZoN4QihAXPD5F7SMFmIrFK6NfhUhFsnYP+v0HiE1hGJAizTn+wBAQEoipIdsQiR824chqt7QGclDZOFyEo6nVqjA7DvJ0hO0DYeUaBlOtmZNm0a//vf/7h8+XI2hCNEDts+Sb2v9Cq4+GkbixD5TflO4BoAcXfhxF9aRyMKsEwnO6+99hpbt26lRIkSODs74+HhYXETIs8IPQ5n1wI6aDRc62iEyH+srKHWm+ryvp9ArgoIjWS6zc60adOyIQwhNJBaq1OxM3iV0jYWIfKr6r1g61fqj4ureyCwvtYRiQIo08lO7969syMOIXJW2Gm1FxZAQ6nVESLbOHhA5a7qWFZ7f5RkR2jiubqexMfHExUVZXETIk/Y/o16X64D+JTXNhYh8rs676r3Z1ZDxDVtYxEFUqaTndjYWN577z28vb1xcnLC3d3d4iZErnfnPJxcpi43+kjbWIQoCHzKQ7FGoJjgwEytoxEFUKaTnREjRrB582ZmzJiBra0tM2fOZNy4cfj7+/Pbb79lR4xCZK0dk9UP3dJtwa+K1tEIUTDUGaDeH5oLSXGahiIKnkwnO6tWrWLGjBm88sorWFtb07BhQz777DPGjx/P77//nh0xCpF1woPh2BJ1ubHU6giRY0q3BrdASIiA40u0jkYUMJlOdsLDwylWrBigTgQaHh4OwAsvvMD27duzNjohstrOKepszCWaQ+EaWkcjRMGht4Lab6vLe6UbushZmU52ihcvbh5QsHz58ixZomboq1atws3NLStjEyJrRd+C/xapy9JWR4icV60nGBzh9mkIlh/HIudkOtnp27cv//33HwAff/yxue3Ohx9+yEcfyReIyMUOzARjkjobc2A9raMRouCxd4Oq3dTl3d9rGoooWDI9zs6HH35oXm7atClnzpzh4MGDlChRgipVpLGnyKWS4x/0Aqk3SNtYhCjI6g6Eg3Pgwka4th8CamsdkSgAnnuK56JFi9KsWTNJdETu9t8iiA8Ht6JQtr3W0QhRcHmWeFC7s2W8trGIAiPTyc7XX3/N4sWLzY+7du2Kp6cnhQsXNl/eyqgJEyZQq1YtnJ2d8fb2plOnTpw9e9ZiG0VRGDt2LP7+/tjb29OkSRNOnjxpsU1iYiKDBw/Gy8sLR0dHXnrpJa5fv57ZUxP5lckEe2eoy3UGqPP1CCG00+gj0FvDpS1wZbfW0YgCINPJzs8//0xAQAAAGzduZOPGjfzzzz+0bds20212tm3bxqBBg9i7dy8bN24kJSWFVq1aERsba95m4sSJTJkyhenTp3PgwAF8fX1p2bIl0dHR5m2GDBnC8uXLWbRoETt37iQmJob27dtjNBoze3oiP7rwL9w5BzbOagNJIYS23IOg2hvqstTuiByQ6Z+4ISEh5mRn9erVdO3alVatWhEUFESdOnUyta9169ZZPJ4zZw7e3t4cOnSIRo0aoSgK06ZN49NPP6Vz584AzJs3Dx8fH/744w/eeecdIiMjmTVrFvPnz6dFixYALFiwgICAAP79919at26d2VMU+c2e6ep9jd5g56JtLEIIVcNhcPR3uLxD7ZlVrJHWEYl8LNPJjru7O9euXSMgIIB169bx5ZdfAurlpuetSYmMjATAw8MDgODgYEJDQ2nVqpV5G1tbWxo3bszu3bt55513OHToEMnJyRbb+Pv7U7FiRXbv3p1uspOYmEhiYqL5ceqcXsnJySQnJz/XOYiMSy3rbC3zWycxBG9D0elJqdEf5O9rliPlLx6rwJe/oy/6qm9gdWgWps3/h/GNuqDT5djhC3z5ayyryj+jr890stO5c2e6d+9OqVKluHv3Lm3btgXg6NGjlCxZMrO7M1MUhaFDh/LCCy9QsWJFAEJDQwHw8fGx2NbHx4crV66Yt7GxsUkzL5ePj4/59Y+aMGEC48aNS7N+w4YNODg4PPM5iGezcePGbNt3tSu/UhS46VqTg7uOA8ez7Vh5VXaWv3i6glz+dsmVaaEzYHVtL3sXT+S2S6Ucj6Egl39u8LzlHxeXsalHMp3sTJ06laCgIK5du8bEiRNxcnIC1MtbAwcOzOzuzN577z2OHTvGzp070zyneyTbVxQlzbpHPWmbjz/+mKFDh5ofR0VFERAQQKtWrXBxkcscOSU5OZmNGzfSsmVLDAZD1h8gOhTr//YC4NPpC9rJiMkWsr38xRNJ+d/ncAr2/0zd+M0YXxuRY7U7Uv7ayqryT70y8zSZTnYMBgPDhw9Ps37IkCGZ3ZXZ4MGD+fvvv9m+fTtFihQxr/f19QXU2hs/Pz/z+rCwMHNtj6+vL0lJSdy7d8+idicsLIz69eunezxbW1tsbW3TrDcYDPKm10C2lfuROWBKhoA6WAfVzfr95xPyvtdWgS//hsPg8G/obx5Cf3krlG711JdkpQJf/hp73vLP6Gsznew8bWbzXr16ZXhfiqIwePBgli9fztatW81zbqUqVqwYvr6+bNy4kWrVqgGQlJTEtm3b+PrrrwGoUaMGBoOBjRs30rVrV0CtZTpx4gQTJ07MzKmJ/CT2Luz7RV2uP1jbWIQQj+fsA7XfVEdU3vENlGqZo213RMGQ6WTngw8+sHicnJxMXFwcNjY2ODg4ZCrZGTRoEH/88QcrV67E2dnZ3MbG1dUVe3t7dDodQ4YMYfz48ZQqVYpSpUoxfvx4HBwc6N69u3nb/v37M2zYMDw9PfHw8GD48OFUqlTJ3DtLFEC7v4WkaPCtDGVe1DoaIcST1HtP/XFybZ867k5QA60jEvlMppOde/fupVl3/vx5BgwYkOlxdn788UcAmjRpYrF+zpw59OnTB4ARI0YQHx/PwIEDuXfvHnXq1GHDhg04Ozubt586dSrW1tZ07dqV+Ph4mjdvzty5c7GyssrcyYn8IfrWg1qdZp+B/rkHChdCZCdnX6jaHQ7NgZ1TJNkRWS5LhpItVaoUX331FT179uTMmTMZfp2iKE/dRqfTMXbsWMaOHfvYbezs7Pj+++/5/nuZWE6gflimxEORWlAqZ6//CyGeUYP34fA8dRDQkP/AT6YgElkny37yWllZcfPmzazanRDPJvI6HJytLjf7TK79C5FXeBSHCurgseycqm0sIt/JdM3O33//bfFYURRCQkKYPn06DRpI1aPQ2PZJYEyCoIZQrLHW0QghMuOFD+HEX3BqJdy9qE4aKkQWyHSy06lTJ4vHOp2OQoUK0axZMyZPnpxVcQmReeGX4MgCdbnpp1KrI0Re41sRSrWG8+th1zR4SZomiKyR6WTHZDJlRxxCPL+tX4MpBUq2gMB6WkcjhHgWDYeqyc7RhdDkY3Dx1zoikQ88V5sdRVEy1MhYiGx3+ywcW6wuN/1U21iEEM+uaF0oWl8dEHTPD1pHI/KJZ0p2fvvtNypVqoS9vT329vZUrlyZ+fPnZ3VsQmSMosC6/wEKlG0PhatrHZEQ4nk0vD+dz8E5EBeubSwiX8h0sjNlyhQGDBhAu3btWLJkCYsXL6ZNmza8++67TJ0qLeiFBk6thIubwcoGWn6udTRCiOdVsoU6IGhyrNrpQIjnlOk2O99//z0//vijxUjJHTt2pEKFCowdO5YPP/wwSwMU4okSY2D9J+pygyHSe0OI/ECng5bjYP7LsP8XqNkfvEpqHZXIwzJdsxMSEpLuBJv169cnJCQkS4ISIsO2T4SoG+AW+KDqWwiR95Vopg4KakqBjaO1jkbkcZlOdkqWLMmSJUvSrF+8eDGlSpXKkqCEyJDbZx80YGw7EQz22sYjhMharb4EnRWcXQPB27WORuRhmb6MNW7cOF577TW2b99OgwYN0Ol07Ny5k02bNqWbBAmRLRQF1gxTf/WVbgtl2mgdkRAiqxUqAzX7wYFf1cvVb28Dvcx5KDIv0zU7Xbp0Yd++fXh5ebFixQqWLVuGl5cX+/fv5+WXX86OGIVI68RSuLwDrO2g7VdaRyOEyC5N/ge2rhB6HP5bqHU0Io96polAa9SowYIFC7I6FiEyJjEa1t8fS6fhcHAP0jQcIUQ2cvSCRsNh4yjY9AWU7wS2TlpHJfKYZ0p2TCYTFy5cICwsLM2Iyo0aNcqSwIR4rL0/QkyoOnFg/cFaRyOEyG513oGDs+DeZdj9HTT9ROuIRB6T6WRn7969dO/enStXrqQZPVmn02E0GrMsOCHSiL8Hu6ery00/BYOdtvEIIbKfta06htaSXrDrWyjdRgYPFZmS6TY77777LjVr1uTEiROEh4dz79498y08XEa6FNls93RIjATv8lChs9bRCCFySrmX1ElCUxJg4esQeV3riEQekumanfPnz/PXX39RsqQM8CRyWOxd2PeTutz0E9A/19RuQoi8RKeDLjNhdmsIOwV/vA79/gFbZ60jE3lApr8t6tSpw4ULF7IjFiGebNc0SIoBvyrqHFhCiILFzgW6LwZHb7h1HP7qDyZpOiGeLkM1O8eOHTMvDx48mGHDhhEaGkqlSpUwGAwW21auXDlrIxQCIPoW7P9VXW76qforTwhR8LgVhW6LYG47OL8eNnwGbSZoHZXI5TKU7FStWhWdTmfRILlfv37m5dTnpIGyyDY7p0BKPBSuqQ4hL4QouIrUgJd/hj97w94Z6px4td7UOiqRi2Uo2QkODs7uOIR4vMgbcHC2utzsM6nVEUJAhU4QPho2fa6Ou1WqlVrrI0Q6MpTsBAYG0q9fP7799lucnaUxmMhhO74BYxIENoDiTbSORgiRW7wwFC5uUUdTX/8pvDZf64hELpXhBsrz5s0jPj4+O2MRIq3wS3D4N3VZ2uoIIR6m00Hbr0Gnh9N/w6WtWkckcqkMJzuPDiAoRI7Y9IU62WeJ5hDUQOtohBC5jU+FB+11/hkJxmRt4xG5Uqa6nuvkV7XISTcOwcllgA5ajtM6GiFEbtX0E7D3gNtn4MBMraMRuVCmBhUsXbr0UxMeGUVZZAlFgY1j1OXKr4FvJW3jEULkXvbu0Hw0rB4CWyZAxVfAqZDWUYlcJFPJzrhx43B1dc2uWIR44MImtdGhlY1M+ieEeLrqveDQHAj5DzaNg47TtY5I5CKZSnZef/11vL29sysWIVQmI/x7v1an9tvgHqhtPEKI3E9vBW0nwexWcGQB1OwLhWtoHZXIJTLcZkfa64gcc2wJ3DoBtq7QcJjW0Qgh8oqidaDy64ACK9+DZOlBLFTSG0vkLskJsOX/1OUXhoCDh6bhCCHymFZfgGMhdbLQDaO0jkbkEhlOdkwmk1zCEtnvwK8QeQ2c/aHuAK2jEULkNU7e0OkndfnAr3BmrbbxiFwh07OeC5Ft4sJh+zfqctNPwGCvbTxCiLypVAuo9566vHIgRN3UNh6hOUl2RO6x/RtIiADv8lC1u9bRCCHysuajwbcyxN+DZW+rHR9EgSXJjsgdwi/B/l/U5VZfqD0rhBDiWVnbwitzwOCoDmOxa5rWEQkNSbIjcod/x4IpWZ0WomQLraMRQuQHXiWh3UR1efP/wZU92sYjNCPJjtCc7vp+OLVSncyv1RdahyOEyE+q9oCKXUAxwqLucPei1hEJDUiyI7SlKOj/Ha0uV+upTuonhBBZRaeDl74H/+oQHw4LukDsHa2jEjlMkh2hKf+I/ehvHFSvqzf9VOtwhBD5kY0jdF8MboFwLxj+eA2S47SOSuQgSXaEdlISKX9zibrc4ANw9tU2HiFE/uXkDT2XqpOG3jiI1coBoJi0jkrkEEl2hGb0B37BMek2ipMv1H9P63CEEPmdVyl4fSFY2aI/u4aKN/4AmR2gQJBkR2gj/BL67WovCWOTT9VqZiGEyG6B9eDlHwEocXsD+m0TJOEpACTZETnPZIK/30eXEs9tp/IolV/XOiIhREFSsQvGluocfFa7psDGUZLw5HOS7Iicd2gOXN6BYnDgaNH+am8JIYTIQaba73CsyBvqg93fw7r/ScKTj0myI3JWxDXYOAYAU9PPiLMtpHFAQoiCKrhQS1LaTlYf7PsJ1gxTa55FviPJjsg5igKrh0BSNATUxVTzTa0jEkIUcEr13tDxB0AHB2fBqvcl4cmHJNkROee/hXDhX7CyhY7T1RGThRBCa9V6wss/q59JR+bD34Ml4cln5NtG5IzoUPWaOEDTj9UuoEIIkVtUeQ06/6omPEcXSMKTz1hrHYAoINYOh4RI8KsK9QZrHY0QQqRV6RX1ftlbasKDok41obfSNCzx/CTZEdnv1N9wehXordXLV1bythNC5FKVXlF7iC59E47+rq6ThCfPk8tYInvF31NrdQAaDAHfSpqGI4QQT1WxC3SZCTorNeFZ/g4kJ2gdlXgOkuyI7LVhFMTcAs9S0OgjraMRQoiMeTjhOf4nzGsP0be0jko8I0l2RPa5tFXt2QBqNbDBTtNwhBAiUyp2VicPtXOF6wfg16Zw86jWUYlnIMmOyB5JcbDqA3W51pvqfDRCCJHXlGgKb21Ra6ejbsDsNnByudZRiUySZEdkjy3/B/cug0thaD5G62iEEOLZeZaAN/+Fki0gJR7+7ANbxkvX9DxEkh2R9a4fhL0z1OX2U8HORdt4hBDiedm7QfclUO899fG2r2FRd3VIDZHrSbIjslZSrDpGhWKCSl2hdGutIxJCiKyht4LW/wedflJHgj/3D/zaHG6f0zoy8RSS7IisteEzCL+kXr5qN1HraIQQIutV7Qb91qmfc3fPw6/N4Ow/WkclnkCSHZF1zm2Ag7PV5U4zwN5d23iEECK7FK4Ob2+FovXVyY0Xvg7/jITEGK0jE+mQZEdkjdi7sHKQulx3IBRvomk4QgiR7Zy8oddKqPWW+njfT/BjPbi4Wdu4RBqS7Ijnpyiw6n2IDYNCZaH5aK0jEkKInGFtAy9+o47H4xoAEVdh/svqj7/4CK2jE/dJsiOe338L4cxq0Bug8y9gsNc6IiGEyFklW8DAPVD7bfXxkQXwQ204OAeMKdrGJiTZEc/pzgVYO0Jdbvox+FXRNh4hhNCKrTO0mwR916mDEMbcgtVDYEZddTJkRdE6wgJLkh3x7JJiYckbauO8wAbqRJ9CCFHQBdaDAbugzVdg76H22FrcE2a1hCu7tY6uQJJkRzwbRYHVH0LYKXD0hldmq2NQCCGEAGtbqDsAPvgPGo0Ag4M6v9actuoIzBFXtY4w55xeBYvfUK8EaESSHfFsDs6GY4vVGYFfnQvOvlpHJIQQuY+dCzT7FN4/AjX6gk6vzq01vZY65URSnNYRZi9jMvw7Fk7/DccWaRaGJDsi824cgnX/U5dbjIGgBtrGI4QQuZ2zL3SYBu9sh6CGkJKgTjkxvSbs/xVi72gdYfY4Mh/uXgAHT6j/vmZhSLIjMicuHJb0BmMSlG2v6ZtXCCHyHN9K0HsVdP0NXIuqM6mvHQ7flIb5neHI7/mny3pSLGz9Sl1uPFLTeRI1TXa2b99Ohw4d8Pf3R6fTsWLFCovnFUVh7Nix+Pv7Y29vT5MmTTh58qTFNomJiQwePBgvLy8cHR156aWXuH79eg6eRQFiMqrzXkVeA4/i6ijJOp3WUQkhRN6i00H5jvDefmg9AfyqgmKEi5tg5UD4ppTaJjImTOtIn8+eH9Qeae5B6iU8DWma7MTGxlKlShWmT5+e7vMTJ05kypQpTJ8+nQMHDuDr60vLli2Jjo42bzNkyBCWL1/OokWL2LlzJzExMbRv3x6j0ZhTp1FwbP4CLvwL1vbqrxI7V60jEkKIvMtgD/UGwjvbYPBhaPqZOjCrMUltF/ldNdg2Ua0hyWti78Cub9XlZqPUwRc1pGmy07ZtW7788ks6d+6c5jlFUZg2bRqffvopnTt3pmLFisybN4+4uDj++OMPACIjI5k1axaTJ0+mRYsWVKtWjQULFnD8+HH+/fffnD6d/O3kctg5VV3uOF2tihVCCJE1PEtA449g0D7oswb8q0NSDGz5P/i+Bhz+DVKStI4y47ZNVOP3qwoV0n7H5zRrrQN4nODgYEJDQ2nVqpV5na2tLY0bN2b37t288847HDp0iOTkZItt/P39qVixIrt376Z169bp7jsxMZHExETz46ioKACSk5NJTk7OpjPKw8JOYb1iIDrAWHcQprIdIQvKKbWspcy1IeWvLSl/beXq8i9cB/qsQ3dqBVZbvkQXeRX+Hoyy6QtMVd/AVL2XOuN6bnUvGOuDs9EBKU1HoxiN8MjVlqwq/4y+PtcmO6GhoQD4+PhYrPfx8eHKlSvmbWxsbHB3d0+zTerr0zNhwgTGjRuXZv2GDRtwcHB43tDzFUNKDI3PjsWQHEeYcwX2JtREWbs2S4+xcePGLN2fyBwpf21J+Wsrd5e/HfqgMRS78y8lb/2DXWwYVrsmo981hVDXalz2bMod53KY9NpeInpUjeAZFDElc8u5EntPx8Dpx39nPG/5x8VlrOt+rk12UukeaQCrKEqadY962jYff/wxQ4cONT+OiooiICCAVq1a4eKiXWvxXMdkxGrx6+iTwlDcAnHvu5y2Dh5Ztvvk5GQ2btxIy5YtMRgMWbZfkTFS/tqS8tdW3ir/jmBMJuXcP+gPzUZ/ZSd+kYfxizyMYm2HUrQeSvGmmIo1Vdv8aNhxRHd5O9ZH9qKgw6Prd7R7TJOHrCr/1CszT5Nrkx1fX3WQutDQUPz8/Mzrw8LCzLU9vr6+JCUlce/ePYvanbCwMOrXr//Yfdva2mJra5tmvcFgyANv+hyiKLB+NFzaAtb26F7/HYOrz9Nf9wyk3LUl5a8tKX9t5ZnyNxigchf1dvus2oD51N/oom+iu7QFLm3BCsDODbzLg3fZ+/flwL8a2Dhmb3yKAvt+hg2fAqCr/BqGgOpPfdnzln9GX5trx9kpVqwYvr6+FlVcSUlJbNu2zZzI1KhRA4PBYLFNSEgIJ06ceGKyIzJg5xTYO0Nd7vSDNEgWQojcolAZaPs1DD0FA/dB6/HqrOvW9pAQAVd3q8nQ2uEw90X4uhj88Rocmpc93dkTY+CvfrBuJJhSoHwneHFy1h/nOWhasxMTE8OFCw/myggODubo0aN4eHhQtGhRhgwZwvjx4ylVqhSlSpVi/PjxODg40L17dwBcXV3p378/w4YNw9PTEw8PD4YPH06lSpVo0aKFVqeV9x2cA5s+V5dbT4CKXbSNRwghRFo63f0anLJQbxCkJKq1PrfPqPMWhp2G0OPqwIXn1qm3VToIqA2Fa6iXvLzLqffPOuDf7bP35706C3praPmFOidYLhuDTdNk5+DBgzRt2tT8OLUdTe/evZk7dy4jRowgPj6egQMHcu/ePerUqcOGDRtwdnY2v2bq1KlYW1vTtWtX4uPjad68OXPnzsXKSialfCanVsKa++2ZGg5Tx4AQQgiR+1nbgl9l9ZZKUdSk58waOLMaQo7CtX3q7WEuRcCvCvhXVbuL+1cFJ++0x0iMgev71dnbL++CGwfVcYGc/dR5EovWzbbTex6aJjtNmjRBUZTHPq/T6Rg7dixjx4597DZ2dnZ8//33fP/999kQYQFzaSssfRMUE9Toow4EJYTI1RRFIdGYSHxKPAkpCcSnxBOfEk9cShxGxYidlR0OBgfsre2xt7bH1dYVgz4PtFERWUOnA5/y6q3xRxB5Ay5uflDzc/sMRIdA1HX1dnbNg9fauoKVQa2x0VupEz9H31QvVT2sWGPoMjP95CiXyLUNlEUOu3kEFvVQM/RyL8GLU3JdNaQQeVWyMZk78XdIMCaQZEwi0ZhIXGIcF5IvsCdkD1ZWViioP/xikmIIiQ0hJDaE0NhQQmNDiU+JB8CkmDApJhQUc2KTYEzApJgyHIu1zpoizkUo7lqc4m7FKe5aHFfbB6Oh69Ch0+nwsveisFNhnG2cn7A3kee4Fobqb1iui78Ht05ByH9qzc/No3DnHCRGPmYfARDYAALrq/eeJXL994UkOwLuXoQFr6ijXRZrpGboerkMKER6TIqJW7G3CI4MJjgqmODIYOJT4rHWW2Ols0Kv06PX6bkbf5fQ2FBCYkO4E3/HnMw8au6WuVkWm43eBnuDPXZWdthb22OttzbX9KTeUpQULkdd5nLUZTZf2/zUfTrbOFPYqTCFnQpT1LkogS6BFHVR7wvZF3rqUCAiD7B3h6AG6i1VYgxE3a/FUYzqvTEFnH3Arah2sT4jSXYKupgwWNAF4u6Ab2V47Xf1uq8QBVR8SjwRCRFEJEZwJ/4ON2JucD36OtdjrnMt+hrXoq+Za1oyw1pvjYO1A7ZWtthY2WDQG0iIScDFxQW9Xo8ONWmwt7bH19EXP0c//Bz98HX0xdHgaE6iAPQ6PbZWtjhYO2BnrSY2dtZ2WOuf/JFuUkyExYVxKfISwZHBXIq4RHBUMLHJlnMvGU1GbsffJjwhnOikaM6En+FM+Jk0+3MyOFG5UGWqelelmnc1KnlVwtGQzV2cRc6wdYJCpbWOIstIslOQJUbD76/CvWBwC4Qefz17i3whcjlFUYhMjCQ0LtRc4xISG0JIzINLRhGJESQaE5+6L2udNUVdilLMtRhBLkG42LpgUkykmFIwKkaMJiMedh5qsuKkJi7utu4WtSDJycmsXbuWdu3a5dg4L3qdHl9HX3wdfanv//ThOeKS47gZc1NN+GKuczXqKleir3A16io3Y24SkxzD7pu72X1zt3n/pd1LU8GzApW8KlHRqyIl3Eo8NQkTIrvJO7CgSkmCJb3U67MOnvDGcrV6Uog8KtmYTHBUMNejr3Mr7ha3Ym8RFhemLt9/nGBMyNC+rPXWuNu6427nThGnIhRxVm+FnQoT4BxAEeciBaKRr4PBgZLuJSnpXjLNc8nGZC5GXuRI2BGOhB3hv7D/uBl701wLtPT8UgDsrOwo7V6aEm4lKOlWUr25l8TbIfc2ZhX5jyQ7BZHJBH8PVlvkGxygx59qAzMh8gBFUcxfqufCz3Eh4gIXIi5wNeoqKUrKU1/vYeeBj4MPfo5++Dv54+voq947+OJh74GbrRsO1g7SFuUpDFYGynqUpaxHWbqV7QZAaGwoJ+6c4Pid45y8c5KTd08SkxzDsTvHOHbnmMXri7sWp2VgS1oFtaKUWykpb5GtJNkpaBQF/vkIji1SuxN2na8OLiWExhKNiYTEhHAz5iYhsSHEpcSRZEwi2ZRMkjGJ2ORYzkec50z4GaKTotPdh5PBiUCXQHwdffFx8MHbwRsfRx98HHzwdfTF28EbWytpk5ZdUi+RtQhUB3U1KSYuR13m/L3zXIy4aJGYXoq8xM/HfubnYz8T5BJEq6BWNC/anHIe5STxEVlOkp2CRFFgw2dwYCagg04/QikZaVrkDJNi4l7CPUJiQ7gadZWr0VfNDX6vR1/ndvztDO/LWm9NSbeSlHYvbXGJxMfBR74ocxG9Tq92cXctbrE+Oimarde2suHKBnbf2M3lqMv8cuwXfjn2C/6O/jQr2oxmRZtRzbuatPcRWULeRQXJ5i9hz3R1+aXvoHJXbeMR+UZMUgyHww5zJ/4OkYmRRCRGEJkYSXhCOHfi7xAWF8bd+LtPvcxkb21PYafC+Dv542RwwqA3YLAyYKO3wdbaluKuxSnrUZYSriUwWOX/NjP5lbONMx1KdKBDiQ7EJMWw7fo2/r3yL7tu7uJm7E0WnF7AgtMLcLV1pZ5fPer716e+f318HKVdoXg2kuwUFNsmwY5v1OV230D1XtrGI/K8uOQ4tl3fxvrL69lxfQdJpqSnvkaHOlhdgHMARV2KUtS5KAEuARRxUhv/utm6Sc1MAeNk48SLxV/kxeIvEp8Sz56be9h8dTNbr28lMjGSdZfXse7yOgBKupWkjl8dKntVpnKhyhR2KizvF5EhkuwUBLu+gy1fqsutvoTab2kbj8iTopKiOH33NCfvnuS/sP/YfXO3Re+m1AHn3GzdcLV1xc3WDXc7d7zsvfB28KaQfSE87D0KRC8m8Wzsre3Nl7BSTCkcv3OcXTd2sfvmbk7cOWFu8/M7vwNqY/PKXpWp5VuLpgFNCXAJ0PgMRG4lyU5+t3Ma/DtGXW72GdQfrGk4Iu8wmozsC93HP8H/cPjWYa5GX02zTYBzAG2C2tA6qDWl3UvLr2yRZaz11lTzrkY172q8V+09IhMj2ROyhyO3jnD8znFOh58mPCGcrde3svX6ViYdnERJt5I0DWhK04CmVPCqYB6EUQhJdvKzbZMe1Og0/h80+kjbeESupygKp8NPs/rSatYFr0vTaLiwU2HKe5anvGd56vvXl54zIse42rrSJqgNbYLaAGrvvTPhZzgadpQd13dw8NZBc83Pr8d/xdnGmere1anpU5MaPjUo61lWahULMEl28iNFga0TYNvX6uNmn0miIx4rPCGcw9cPsy9kH3tD9nIj5ob5udQvmKYBTangWQE3OzftAhXiIbZWtlQpVIUqharQu0JvIhMj2XFjB1uubmHXzV1EJ0Wz7fo2tl3fBoCDtQO1fGtR378+DQo3wM/OT+MzEDlJkp38RlFg0+ewc4r6uMU4eGGIpiGJ3OdK1BVWnl/JqqhVfLbsM4vnbK1saRLQhPbF29PAv4H0ehJ5gqutK+2Lt6d98fakmFI4G36Wg7cOcvDWQQ7fOkxUUpRF8lPYsTCFkwvjcN2B+kXqy5xe+ZwkO/mJyaSOo7P3B/Vx6/FQb5C2MYlcIzwhnHXB61hzaU2a0WzLuJehjl8d6vrVpYZPDRwMDhpFKcTzs9ZbU8GrAhW8KtC7Qm9Miomz4WfN83gdDjvMjdgb3OAG+7fvx1pvTXXv6jQo3IDavrUp6VYSO2s7rU9DZCFJdvILY7I6BcR/C9XHbSdBnbe1jUloLiIhgs3XNrPh8gb2hewzj3NjpbOirm9d/CL9GNBmAN7OMk+RyL/0Oj3lPMtRzrMc/Sv1Jy45jj039rBozyKu26gz2u8P3c/+0P3m7QNdAs2DVlbyqkRV76rYW9trfCbiWUmykx8kxcFffeHcOtBZwUvfQ7UeWkclNHI9+jp7Qvbw75V/2ReyD6NiND9XwbMC7Yu3p02xNrhau7J27Vrc7dw1jFaInOdgcKBR4UbEOMTQrl07QuJD2HFjB7tu7OLEnRPcS7xHcGQwwZHBrL+8HlBri1K7udfyrUV5z/I42zhrfCYioyTZyevi78Efr8O1vWBtB6/OhTJttY5K5KDbcbfZG7KX/aH7ORB6wKKBMaiXqFoFtaJVYCuCXIPM65OTk3M4UiFyp6IuRenh0oMe5XqgKAp34u9w7t45zt07x+nw0xy+dZhbcbc4HHaYw2GH+fnYz4A6F1gpt1KUcldv5T3KE+gSiJXeSuMzEo+SZCcviwqBBV0g7CTYukL3xRBYT+uoRDZLMaXw3+3/2HF9Bztv7OTsvbMWz1vrrKlUqBINCzekVVArAl0CNYpUiLxHp9NRyKEQhRwK0aBwA0AdkuF6tHqp68CtAxy6dYjQ2FDzbceNHebXO1g7UNajLOU9y1PavTQ+Dj54OXhRyL6QjBCuIUl28qq7F2H+yxBxBZx8oOcy8K2odVQim4TGhrLrxi523dzF3pt7iU5+MOu3Dh3lPMtRx68OtX1rU927ujQwFiIL6XQ6AlwCCHAJoEvpLgBEJkZyIeIC5++d5/y985y9d5az4WeJS4kz1wA9ylpvTWGnwua2QGXcy1DGowy+jr4yAGI2k2QnLwo9DvM7Q2wYuBeDN5aDRzGtoxJZKMmYxKFbh9h5Yye7buziYuRFi+fdbN2o71+fFwq/QIPCDfCw83ji/kwmhaiEZO7EJHI7Ook7MYnciozj0DUdl7dewtbGGmu9Dmu9DisrPXqdmkTpdaDX6TAqCkkpJpKNJhLv37vaG/BztcPHxQ5fVzsKOdlibSUf2KJgcLV1pYZPDWr41DCvSzGlEBwZzKm7pzh59ySXIy9zO/42d+LvEJEYQYophStRV7gSdYWNVzaaX6fX6XG1ccXNzg13W3fc7dwp4lSEQNdAAp0DCXQJxNvBW2qFnoMkO3nNlT3wx2uQGAk+laDnUnCWmYDzg9Tq8B3Xd7A3ZC/xKfHm5/Q6PZW8KtGgcAMa+Deggqc6FH54bBI37yZwJOoWYdGJhEUlEhadwO3oRMJjkwiPSyIiLpmIuCRMSnpHteKf6xeyJH69DtwdbPBwVG+eTjYEuDvQu34Q/m7Si0Xkf9Z6a3P7nY4lO1o8l2RM4k78HS5HXeZcuNoe6Oy9s1yKvESKKYV7iffUhtEEp7tvOys73O3URMjd1h03Oze87Lzwd/KniLM6ka6/kz/21vYoioJRMZJiSiHFlIK9tX2Bb0ckyU5ecm4DLOkFKfFQtB50WwT2blpHJZ5RojGRw7cOmy9PXYiwTDq87L1o4P8CFdxr40p5bobruHAhhu374giJ3E5IZAKJKaZMHdPZzppCTrZ4Odvi6WAg8nYIRQICMCo6jCYTySYFo1FBQcGkqG0VjCYFK70eG2sdNlZ6DFZ6rK30RMQlERqVwK3IBG5FJ2I0KdyNTeJurOXs57/tucLg5iXp/0IxbK0L9geuKLhsrGzwd/LH38mf+v71zeuTTclEJkYSnhBOREIE9xLvcTf+Lteir5lrgW7E3CDBmEBIbAghsSFPPI5BbyDZZNn5wEpnhae9J9723hRyKIS3gzee9p542nniYeeBh50HhewLUdi5cL69nCbJTl5xcA6sHQ6mFCjVWu11ZSPtMvKS+JR4Ttw5wdGwoxwOO8yhW4csa2/QE+BYFm/rqujiyhJ6y5NlJ+JYkGQEzjx2v4WcbfF1scPb2RZvF1sKOdtRyNkWL0cb3BxscHc04O5gg5uDwSLZSE5OZu3aG7RrVwGD4flGSVYTnfu1STFqwhMem8TqYzc5cPkeE9ed5a+D1xnzUgUaly70XMcSIj8x6A142XvhZe/12G2SjcmExoaqtT8Jag1QREIEYfFh3Ii+wY0Y9RaTHJMm0QEwKkbC4sIIiwuDu4+Pxd7anlLupSjjXsbcyLqsR1ms9Xk/Vcj7Z5DfpSTBPyPg0Bz1caWu0GkGyBD+uZ6iKPwXdpylZ9dyOOwg12MvYMJouVGKC8kxpUiJKUNKbEkiTQ8nsGojZIOVjiBPR0r5OFGykBPFCjni72qPv5s9Pi522Fhr/0vMSq/D29kOb2fLUWd71QtkxdEbjF97hkt3Yuk9ez9VA9xwtlM/enQ63f32QWrbILVJwv11ugfrdDqdxTb6+495aNliWx0W7Y5S1+l46Ln7r3vw3OOPpwP0erW9hP6R/adun3oeqcuY1z04n4e3NxmNHLutI/m/EAzWVumc48Nl8dA+Hor74f2Tzvmm7uPhMntwjve31z8S48PlYvF3Ucsg3X08UmY8VMaW5fQgjgfHQdqiPIXByqA2kCbgsdsoikJUUhRxyXEYrAxY66wxWBmw0lkRmRjJ7fjbhMWFcTvuNmHxYYQnhHM3/i7hCeGEJ4RzK/YW8SnxHLt9jGO3H4yw7mxwppZvLer616WuX12CXILy5N9Lkp3cLCZMvWx1dQ+gg+aj4IWhkAffaPlditHEjYh4Lt6OYfe1o+wN28z1xH0YrSx/RpmSXTDGB2KMC8QYVxxToh/qV6R6iSnI05GS3k6U9HaiRCH1PtDTAUMebfir0+l4uVoRWpTzYdq/55m7+zJHr0VoHVYuYsWCC8e1DiJXeDQhMydMjyRTFsllamLJQ0npQwmxxX4tEi31mLExVvxwcbc5kX04qbRIyp4Q28P7h0cS5EeS5ycl8A/v+9EEWa9Xz/FxsT0piX9QhgbQFUGvK4IOHR468NSBzl6HzgEUdxNRKTe5m3yZO0nqLTTxDNHJ0Wy+tpnN1zYD4GkTQH3P1yjv0ggrvZV5/+gsy/fhv0HqOVYq7EqgpzZzkEmyk1vdOAyLe0LUDbB1gS4zoXRrraMSQHySkaPXIjh4OZzD129yPvIUd5LPo7O7it7uOnrrWHVDK1BMBoirQCF9NYo6lCeoUBH83Ozxu997ycPJBg8H9XJTbqihyS7OdgZGtS9Pz7qBHLsegUlRUBQwKWBSFFB4qJ2Quk4BuH9vMqn3qc+Bep+6vYK6P+WRdantjpRH9q8oqfuzPGbqfh+OQXncPnhoO/UUzOdiesIxU58zmkzcvn0bTy8vQGex/cPl8fD+FSXtMZVH4rWI4aEyu3966cRmGb+5LNJZl97xUvf7vBQFjOadZdFOn0pHaHxMDh0rryh0/1YLMKG3u4G14wWsHC9iZX+Zu0nXWBXyDSsu/0bSneakRFUCMvbZ9X8vV5RkRzzk1N+w7C1ISQDPUtBtIXiV0jqqAinFaOLSnVhO3Yzi2PV77Ll+mktRJ9DZXUFvfxUr29vgCg9fVNRjQ5B9LRr6N6dj6eaU8HL///buPDqq8n4D+HPnTjIz2ZNJyEIIDRCykpBFqrIJIlalWG0VkO0o9idYy6olFq2VCigKx6MUkNbWtljB00aPC3qIFakYTTAhkkBKognJZBkCWScLyUzm/f0xZGAIKEpmbmZ4PufknNxt/N5vPOHJe5fX/pfjtS421BexoZxdGui/Z2ofbr8966rvmVLapQLT5cKguEzAEuKicAZbyMXF6+zB7aIweInAemGghb0e2zqzxYLPv8jHhAkTIMvqAWFRQMBqvTjUXfAZF4TFC/+bFwZIh8BsD7KO53nZz8DAXl0cOq+mt+JydV7QB1ttwyEwARBAr7UL9SIXdeJDQNMI3fA3oIs6iBjcB3+ReK7ui/44sJ4/93B/5SZXZdgZavJfAT5YC0AAcTNtIzraQKWruiZY+qwoP9WBr2pbcbS2FaX1TShv/R+EphKyTxVkXTUkn7MD7gsP0w5Hin4cJkSNR1pYKuJD4uEteytzEkQKkC64jCPDPYK92WxGU5nAjaP1bh82XWsSTL2PYnfZbvzj2D9gMtfjG9WL2Dp1K6bFTFO6uMti2BkqrFbgo6eAvJdsy1kP2GYul/kjcoYeSx8qTnXgeEM7yhraUVrXhpK6NvTgDNR+x6H2L4PsWw1vf4vDcd4qLRKCkzEhKh3pw9IxLnQcJ9IkomuKv7c/lqUtw/zE+Xg672nsr96P1QdX44WpL+DmmJuVLu+S+C/pUGDpAd5eBpT+27Z88+94I/Igq23pQn5lM/KrmvCVoQ3fnO6AxSoAyQyVxgi13wmoo4/DT1vvcFyAdxCui8hEVngWMsIzMDZ4rEc8hklEdLUCvAPw3JTnIH8q44OTH+DRTx61BZ6RQy/w8Le20rpbgL0LgZOfAio1cOcfgbS5Slfl1rp6LShrMOFYfRuKDa3Ir2xGXWs3gD6odLWQdTVQh9dD59MAeDUC0vkX86kkFTKGZWB6zHRMjJqI2MBYt3zMkojIFdQqNTZO3ghJkrCvah8ePfgoNk/djFtG3qJ0aQ4YdpTUXAW8fg/QVAF4+wNz/gGMHrrXPIeijh4Ljp27BFVa14bS+nZUnu44d0OiFSqNEbLvN/AZ8Q3UvichpLMDPiNYE4y0YWmYPmI6po6Y+p3zTBER0XlqlRobJ22ESlLhvcr38NjBx7D1pq2YHjNd6dLsGHaUUpMP7JkHdDUBAdHA/DeB8GSlqxrSmjt7cby+Hccb2nC8vh0ldW2oPNN5/tFXuROyzgB1aA10fgZAY4D1gnAjYBt2zQzPRKI+EYkhiUgISUC4TzhHb4iIroKskvHMxGcgQcK7le8i+9Ns7L59N8YGj1W6NAAMO8oo/Tfw1jKgrweIHA/ctxfwj1C6qiGl12JFaX0bDlc14/DJFpTWtcHYfsGojKobsrYOXsF18A1ogKyrQw9O2zf3X5jyUfsgMzwTP478MSZETEB8SLzHzv1CRKQkWSVj/cT1aOxuRH5DPpZ/vBx77tiDIG2Q0qUx7LjcZy8BuU/avo+/3fZouTffO9LVa0FRdSvyq5pQUNWMYkPrBZNcCkheLVAHVCM4pBYqXTW6UGs/1nLuCwB+FPAjpIalIi0sDWlhaRgTNOaan+2XiMhV1Co1XpjyAua9Pw+1HbV49OCj2HnLTsUf7GDYcaVPngM+2Wj7/vqHgZnPANfoP8Rt3WYUVJ7Gu9UqvLYrHyV17banowD032sTqDcgOKQOZ+Wv0dnXDAC48I6b4X7DkaxPRpI+CcmhyUgMSUSghu8kIiJSUpA2CC9Nfwnz981HvjEfW77cgrUT1ipaE8OOKwgBfPwM8OkLtuWbfwdMXqNsTS7U3duHyjMdKD9lQmF1C7482YITp0zn7rVRQZLrodIZoA9ugH9gPTpQiR5rJ6w4N0Fvn+2vhSR9EtLDbO+3GT9sPPQ6vaLnRURElxYXHIeNkzZi1SersLvMdu/OXXF3KVYPw46zCWG7bJX3sm155gbgxkeUrcmJOnosOFzVjC+qmnDCaMLXjR2oa+2+YP6cPqi0RqiDahAQVAfhVQmzbBu16QXQdO7KlY/aB+OHjUfGsAxkhGcgJTQFOrVOiVMiIqIfYMbIGViWtgw7vtqBP3zxB4wOGo3UsFRFamHYcSYhgA+zgfydtuXbngd+/H/K1jTITpt67DcSf17ZhKO1bejrvxwld0L2PgV1YCN0vqeh9W1Er1yDPvQAsIWbfv332owLHYfUsFS+vI+IyAMsTVuKE80n8LHhY+Q35DPseBwhgPfXAF++alue9SKQdb+iJV2trl4LCqqaUVTdgtJ62xQLjaaec1sFVN6nIQdUIjC4BrKuCmdFi/1YAaD73Pf+Xv5IDUtFij4FZyvP4v7b7ofel5ekiIg8jUpSYePkjfi8/nPMGDlDsToYdpxFkoCgEQAk4M5tQPoCpSv63nosfSipbcNnXzfhs2/O4EhNC8x9AkAfVN5noNIYoQkzwj/gDIR3NXrRDgAwAzCfG9yJ8o3C6KDRGBM0BqODRiMlNAWxgbFQSSrbrM+GfQjwDlDsHImIyLl8vXwVDToAw45zTVoFxN0KhCcpXcl36rMKVDSacNTQdm7W7zb8z9huCzeqbsg+1ZBDqhAUUA2rdy0Ezk+Q2T+2o5E1SAtLQ1Z4FrIispCkT4KvFx+rJyIiZTHsONsQDTodPRYU17Tano6qbkZxTStMPRZIcgdUGqPta5gRAT4NEN71sF2IAvrOHe+j9sGY4DGIC4pDXHAcEkMSkRKaAm/ZW7FzIiIiuhSGnWvAqfaz+MrQirIGE8oa2lFmbEd1UyckdRtkbR1UujrIw+rgp62HpDY5HNv/ENXIgJHIDLfN/j0+bDyG+w/nm4iJiMgtMOx4oPrWbnxR2YT8ymbkVzXhZFMXJHU7VNpayNpayD518A2phUrdOeBYCRKi/aPtIzZjg8di/LDxGOYzTIEzISIiunoMO25MCIFT7T0oPTfr97H6NpTWtcPY0WYLNToDVFoDfMcYoPJqH3C8WlJjdNBoJOmTkKhPRJI+CXFBcfDx8lHgbIiIiJyDYcdNCCFQ29JtDzQldW0orW9F89kzUGkbIGsaoNI2QBXaAL+oM5Ak4XC8SlJhVOAopISmIFmfjGR9MsaGjIVG1ih0RkRERK7BsDPECCHQaOpBWUM7yk+ZUH6qAxWnbG8i7rSYIOtsIzay1gBVZC381B2X/JwI3wjbC/pCU5ESmoIkfRJHbIiI6JrEsKOgtm4zvjndga8bO1BuNKHM2I6yBhOaO7ug0pyCSmuErDllezIqxgj/S1yKkiUZsYGxGBs8FmODxyI+JB7xwfEI8wlT4IyIiIiGHoYdJ+u/r+brxg583WjCN6c7bd+f7sBpUw9sc0U12J6K0tZBDquDX7QRkqrvkp83MmAkkvXJGBc6DimhKUgISYBWrXXtSREREbkRhh0nWv/ucbz5pQEdPedfwCepWyD71EDW1cAnpBayrh6QzAOO9ff2R3xwPOKC4zAmaAzGBo/F6KDR8Pf2d+UpEBERuT2GHSdSSQJdog6a4JPwDzJAaKvQi6YB+wV4ByBZn4wkfZL9yahov2hIkqRA1URERJ6FYceJDF474Tv6EwDnp1SQJRkJIQkOM3zH+Mcw2BARETkJw44TpUcko+j0F0gLS0NGeAYywjOQGprKp6KIiIhciGHHiRYmLcSSlCXwkr2ULoWIiOiaxbDjRJzxm4iISHmcyZGIiIg8GsMOEREReTSGHSIiIvJoDDtERETk0Rh2iIiIyKMx7BAREZFHY9ghIiIij8awQ0RERB7NY8LO9u3bERsbC61Wi8zMTHz66adKl0RERERDgEeEnb1792LlypVYt24djhw5gsmTJ+O2225DTU2N0qURERGRwjwi7GzduhVLlizBgw8+iMTERLz44osYMWIEduzYoXRpREREpDC3nxurt7cXhYWFyM7Odlg/c+ZM5OXlXfKYnp4e9PT02Jfb29sBAGazGWaz2XnFkoP+XrPnymD/lcX+K4v9V9Zg9f9Kj3f7sHPmzBn09fUhPDzcYX14eDiMRuMlj9m0aROefvrpAev3798PHx8fp9RJl5ebm6t0Cdc09l9Z7L+y2H9lXW3/u7q6rmg/tw87/SRJclgWQgxY1+/xxx/H6tWr7cttbW2IiYnBDTfcAH9/f6fWSeeZzWYcOHAA06ZNg5eXl9LlXHPYf2Wx/8pi/5U1WP03mUwAbP/mfxu3DzuhoaGQZXnAKE5jY+OA0Z5+Go0GGo3Gvtx/GSs2NtZ5hRIREZFTmEwmBAYGXna724cdb29vZGZmIjc3F3fddZd9fW5uLu68884r+oyoqCgYDAb4+/tfdjSIBl97eztGjBgBg8GAgIAApcu55rD/ymL/lcX+K2uw+i+EgMlkQlRU1Lfu5/ZhBwBWr16NhQsXIisrCzfccAN27dqFmpoaLF269IqOV6lUiI6OdnKVdDkBAQH8ZaMg9l9Z7L+y2H9lDUb/v21Ep59HhJ05c+agqakJ69evR0NDA1JSUrBv3z6MHDlS6dKIiIhIYR4RdgDg4YcfxsMPP6x0GURERDTEeMRLBck9aTQaPPXUUw43i5PrsP/KYv+Vxf4ry9X9l8R3Pa9FRERE5MY4skNEREQejWGHiIiIPBrDDhEREXk0hh0iIiLyaAw75FKbNm2CJElYuXKlfZ0QAr///e8RFRUFnU6Hm266CceOHVOuSA9TV1eHBQsWQK/Xw8fHB+PHj0dhYaF9O/vvPBaLBU888QRiY2Oh0+kwatQorF+/Hlar1b4P+z94/vvf/+KnP/0poqKiIEkS3n77bYftV9Lrnp4e/PrXv0ZoaCh8fX0xe/Zs1NbWuvAs3Ne39d9sNmPt2rUYN24cfH19ERUVhUWLFqG+vt7hM5zVf4YdcpnDhw9j165dSE1NdVi/efNmbN26Fdu2bcPhw4cRERGBW265xT7BG/1wLS0tmDhxIry8vPDBBx/g+PHj2LJlC4KCguz7sP/O89xzz2Hnzp3Ytm0bysrKsHnzZjz//PN4+eWX7fuw/4Ons7MTaWlp2LZt2yW3X0mvV65cibfeegt79uzBoUOH0NHRgVmzZqGvr89Vp+G2vq3/XV1dKCoqwpNPPomioiLk5OSgvLwcs2fPdtjPaf0XRC5gMplEXFycyM3NFVOnThUrVqwQQghhtVpFRESEePbZZ+37nj17VgQGBoqdO3cqVK3nWLt2rZg0adJlt7P/znXHHXeIBx54wGHd3XffLRYsWCCEYP+dCYB466237MtX0uvW1lbh5eUl9uzZY9+nrq5OqFQq8eGHH7qsdk9wcf8vpaCgQAAQ1dXVQgjn9p8jO+QSv/rVr3DHHXdgxowZDuurqqpgNBoxc+ZM+zqNRoOpU6ciLy/P1WV6nHfeeQdZWVm45557MGzYMKSnp+NPf/qTfTv771yTJk3Cf/7zH5SXlwMAvvrqKxw6dAi33347APbfla6k14WFhTCbzQ77REVFISUlhT8PJ2hra4MkSfaRZmf232Omi6Cha8+ePSgqKsLhw4cHbDMajQCA8PBwh/Xh4eGorq52SX2erLKyEjt27MDq1avx29/+FgUFBVi+fDk0Gg0WLVrE/jvZ2rVr0dbWhoSEBMiyjL6+PmzYsAHz5s0DwP//XelKem00GuHt7Y3g4OAB+/QfT4Pj7NmzyM7Oxn333WefCNSZ/WfYIacyGAxYsWIF9u/fD61We9n9JElyWBZCDFhH35/VakVWVhY2btwIAEhPT8exY8ewY8cOLFq0yL4f++8ce/fuxe7du/HPf/4TycnJKC4uxsqVKxEVFYXFixfb92P/XeeH9Jo/j8FlNpsxd+5cWK1WbN++/Tv3H4z+8zIWOVVhYSEaGxuRmZkJtVoNtVqNgwcP4qWXXoJarbb/lXVxam9sbBzwFxh9f5GRkUhKSnJYl5iYiJqaGgBAREQEAPbfWR577DFkZ2dj7ty5GDduHBYuXIhVq1Zh06ZNANh/V7qSXkdERKC3txctLS2X3Yeujtlsxr333ouqqirk5ubaR3UA5/afYYec6uabb0ZJSQmKi4vtX1lZWZg/fz6Ki4sxatQoREREIDc3135Mb28vDh48iBtvvFHByj3DxIkTceLECYd15eXlGDlyJAAgNjaW/Xeirq4uqFSOv2ZlWbY/es7+u86V9DozMxNeXl4O+zQ0NKC0tJQ/j0HQH3QqKirw0UcfQa/XO2x3av+v6vZmoh/gwqexhBDi2WefFYGBgSInJ0eUlJSIefPmicjISNHe3q5ckR6ioKBAqNVqsWHDBlFRUSFef/114ePjI3bv3m3fh/13nsWLF4vhw4eL9957T1RVVYmcnBwRGhoqfvOb39j3Yf8Hj8lkEkeOHBFHjhwRAMTWrVvFkSNH7E/7XEmvly5dKqKjo8VHH30kioqKxPTp00VaWpqwWCxKnZbb+Lb+m81mMXv2bBEdHS2Ki4tFQ0OD/aunp8f+Gc7qP8MOudzFYcdqtYqnnnpKRERECI1GI6ZMmSJKSkqUK9DDvPvuuyIlJUVoNBqRkJAgdu3a5bCd/Xee9vZ2sWLFChETEyO0Wq0YNWqUWLduncMvd/Z/8Bw4cEAAGPC1ePFiIcSV9bq7u1s88sgjIiQkROh0OjFr1ixRU1OjwNm4n2/rf1VV1SW3ARAHDhywf4az+i8JIcTVjQ0RERERDV28Z4eIiIg8GsMOEREReTSGHSIiIvJoDDtERETk0Rh2iIiIyKMx7BAREZFHY9ghIiIij8awQ0RERB6NYYeI3FZeXh5kWcZPfvITpUshoiGMb1AmIrf14IMPws/PD3/+859x/PhxxMTEKF0SEQ1BHNkhIrfU2dmJN998E8uWLcOsWbPw2muvOWx/5513EBcXB51Oh2nTpuFvf/sbJElCa2urfZ+8vDxMmTIFOp0OI0aMwPLly9HZ2enaEyEip2PYISK3tHfvXsTHxyM+Ph4LFizAX//6V/QPVJ88eRK/+MUv8LOf/QzFxcV46KGHsG7dOofjS0pKcOutt+Luu+/G0aNHsXfvXhw6dAiPPPKIEqdDRE7Ey1hE5JYmTpyIe++9FytWrIDFYkFkZCTeeOMNzJgxA9nZ2Xj//fdRUlJi3/+JJ57Ahg0b0NLSgqCgICxatAg6nQ6vvPKKfZ9Dhw5h6tSp6OzshFarVeK0iMgJOLJDRG7nxIkTKCgowNy5cwEAarUac+bMwV/+8hf79uuuu87hmAkTJjgsFxYW4rXXXoOfn5/969Zbb4XVakVVVZVrToSIXEKtdAFERN/Xq6++CovFguHDh9vXCSHg5eWFlpYWCCEgSZLDMRcPYlutVjz00ENYvnz5gM/njc5EnoVhh4jcisViwd///nds2bIFM2fOdNj285//HK+//joSEhKwb98+h21ffvmlw3JGRgaOHTuGMWPGOL1mIlIW79khIrfy9ttvY86cOWhsbERgYKDDtnXr1mHfvn3IyclBfHw8Vq1ahSVLlqC4uBhr1qxBbW0tWltbERgYiKNHj+L666/H/fffj1/+8pfw9fVFWVkZcnNz8fLLLyt0dkTkDLxnh4jcyquvvooZM2YMCDqAbWSnuLgYLS0t+Ne//oWcnBykpqZix44d9qexNBoNACA1NRUHDx5ERUUFJk+ejPT0dDz55JOIjIx06fkQkfNxZIeIrgkbNmzAzp07YTAYlC6FiFyM9+wQkUfavn07rrvuOuj1enz22Wd4/vnn+Q4domsUww4ReaSKigo888wzaG5uRkxMDNasWYPHH39c6bKISAG8jEVEREQejTcoExERkUdj2CEiIiKPxrBDREREHo1hh4iIiDwaww4RERF5NIYdIiIi8mgMO0REROTRGHaIiIjIozHsEBERkUf7fzymJ3l/gysrAAAAAElFTkSuQmCC",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
@@ -205,7 +205,7 @@
             "outputs": [
                 {
                     "data": {
-                        "image/png": 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",
+                        "image/png": 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",
                         "text/plain": [
                             "<Figure size 640x480 with 1 Axes>"
                         ]
diff --git a/code/notebooks/WealthPortfolio.ipynb b/code/notebooks/WealthPortfolio.ipynb
index 3015fb9..ab3db7a 100644
--- a/code/notebooks/WealthPortfolio.ipynb
+++ b/code/notebooks/WealthPortfolio.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,16 +28,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(5.927650549987919, 0.4337387151345948, 9.395681818099618)"
+       "(3.394330573661952, 1.0, 0.537543812530427, 0.0)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -46,13 +46,13 @@
     "portfolio_agent = WealthPortfolioLifeCycleConsumerType(**init_calibration)\n",
     "portfolio_agent.CRRA = float(res[\"CRRA\"])\n",
     "portfolio_agent.WealthShare = float(res[\"WealthShare\"])\n",
-    "portfolio_agent.WealthShift = float(res[\"WealthShift\"])\n",
-    "portfolio_agent.CRRA, portfolio_agent.WealthShare, portfolio_agent.WealthShift"
+    "# portfolio_agent.WealthShift = float(res[\"WealthShift\"])\n",
+    "portfolio_agent.CRRA, portfolio_agent.DiscFac, portfolio_agent.WealthShare, portfolio_agent.WealthShift"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,12 +61,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -81,12 +81,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -97,12 +97,15 @@
    ],
    "source": [
     "plot_funcs([sol.ShareFuncAdj for sol in portfolio_agent.solution[:-1:5]], 0, 100)\n",
-    "# add Morton-Samuelson"
+    "# add Morton-Samuelson\n",
+    "# plot all data\n",
+    "# Title: Portfolio Share Converges to Merton-Samuelson at top and bottom\n",
+    "# add reference"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -177,13 +180,13 @@
        "(25.0, 95.0)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -196,17 +199,21 @@
     "plt.figure()\n",
     "plt.plot(AgeMeans.Age, AgeMeans.nrmM, label=\"Market resources\")\n",
     "plt.plot(AgeMeans.Age, AgeMeans.nrmC, label=\"Consumption\")\n",
-    "plt.plot(moments_values[0], moments_values[1], label=\"scf\")\n",
+    "plt.plot(moments_values[0], moments_values[1], label=\"SCF\")\n",
     "plt.legend()\n",
     "plt.xlabel(\"Age\")\n",
-    "plt.title(\"TRP Variable Medians\")\n",
+    "plt.title(\"TRP Wealth Medians vs. SCF data\")\n",
     "plt.grid()\n",
-    "plt.xlim([25, 95])"
+    "plt.xlim([25, 95])\n",
+    "\n",
+    "# show these figures for other models too\n",
+    "\n",
+    "# try all on same graph; make TRP prediction thicker"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -215,13 +222,13 @@
        "(25.0, 95.0)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -246,7 +253,11 @@
     "plt.title(\"TRP Portfolio Share Median Conditional on Survival\")\n",
     "plt.ylim(0, 1)\n",
     "plt.grid()\n",
-    "plt.xlim(25, 95)"
+    "plt.xlim(25, 95)\n",
+    "\n",
+    "# same graph for other two\n",
+    "\n",
+    "# also try all on same graph"
    ]
   },
   {
diff --git a/code/notebooks/parse_tables.ipynb b/code/notebooks/parse_tables.ipynb
index 0b11769..ec8970c 100644
--- a/code/notebooks/parse_tables.ipynb
+++ b/code/notebooks/parse_tables.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,18 +39,7 @@
     "    labor = 0\n",
     "    stock = 0\n",
     "\n",
-    "    if \"Sub\" in file_name:\n",
-    "        if \"(Labor)\" in file_name:\n",
-    "            labor = 1\n",
-    "            file_name = file_name.replace(\"(Labor)\", \"\")\n",
-    "\n",
-    "        if \"(Stock)\" in file_name:\n",
-    "            stock = 1\n",
-    "            file_name = file_name.replace(\"(Stock)\", \"\")\n",
-    "\n",
-    "    file_name = file_name.replace(\"Sub\", \"\").replace(\"Market\", \"\")\n",
     "    file_parameters[\"Name\"] = file_name\n",
-    "    file_parameters[\"Stock\"] = stock\n",
     "\n",
     "    # Iterate over each parameter we want to keep\n",
     "    for param in params_to_keep:\n",
@@ -64,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -88,135 +77,54 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Stock</th>\n",
+       "      <th>Name</th>\n",
        "      <th>criterion</th>\n",
        "      <th>CRRA</th>\n",
-       "      <th>BeqShift</th>\n",
-       "      <th>BeqFac</th>\n",
        "      <th>WealthShare</th>\n",
-       "      <th>WealthShift</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Name</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
+       "      <th>BeqFac</th>\n",
+       "      <th>BeqShift</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Portfolio</th>\n",
-       "      <td>✔️</td>\n",
-       "      <td>18.328</td>\n",
-       "      <td>3.476</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Portfolio</th>\n",
-       "      <td></td>\n",
+       "      <th>0</th>\n",
+       "      <td>Portfolio</td>\n",
        "      <td>0.895</td>\n",
        "      <td>6.374</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WarmGlowPortfolio</th>\n",
-       "      <td>✔️</td>\n",
-       "      <td>49.567</td>\n",
-       "      <td>2.947</td>\n",
-       "      <td>52.462</td>\n",
-       "      <td>45.677</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>WarmGlowPortfolio</th>\n",
-       "      <td></td>\n",
+       "      <th>1</th>\n",
+       "      <td>WarmGlowPortfolio</td>\n",
        "      <td>7.719</td>\n",
        "      <td>4.706</td>\n",
-       "      <td>16.964</td>\n",
-       "      <td>46.463</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WealthPortfolio</th>\n",
-       "      <td>✔️</td>\n",
-       "      <td>0.154</td>\n",
-       "      <td>6.848</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td>0.389</td>\n",
-       "      <td>41.345</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WealthPortfolio</th>\n",
-       "      <td></td>\n",
-       "      <td>0.058</td>\n",
-       "      <td>5.928</td>\n",
-       "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>0.434</td>\n",
-       "      <td>9.396</td>\n",
+       "      <td>46.465</td>\n",
+       "      <td>16.966</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>WealthPortfolioShareOnly</th>\n",
-       "      <td>✔️</td>\n",
-       "      <td>0.446</td>\n",
-       "      <td>2.000</td>\n",
+       "      <th>2</th>\n",
+       "      <td>WealthPortfolio</td>\n",
+       "      <td>0.347</td>\n",
+       "      <td>3.386</td>\n",
+       "      <td>0.536</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>0.574</td>\n",
-       "      <td></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WealthPortfolioShareOnly</th>\n",
-       "      <td></td>\n",
-       "      <td>0.346</td>\n",
-       "      <td>3.421</td>\n",
-       "      <td></td>\n",
-       "      <td></td>\n",
-       "      <td>0.538</td>\n",
-       "      <td></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                         Stock  criterion   CRRA BeqShift  BeqFac WealthShare  \\\n",
-       "Name                                                                            \n",
-       "Portfolio                   ✔️     18.328  3.476                                \n",
-       "Portfolio                           0.895  6.374                                \n",
-       "WarmGlowPortfolio           ✔️     49.567  2.947   52.462  45.677               \n",
-       "WarmGlowPortfolio                   7.719  4.706   16.964  46.463               \n",
-       "WealthPortfolio             ✔️      0.154  6.848                        0.389   \n",
-       "WealthPortfolio                     0.058  5.928                        0.434   \n",
-       "WealthPortfolioShareOnly    ✔️      0.446  2.000                        0.574   \n",
-       "WealthPortfolioShareOnly            0.346  3.421                        0.538   \n",
-       "\n",
-       "                         WealthShift  \n",
-       "Name                                  \n",
-       "Portfolio                             \n",
-       "Portfolio                             \n",
-       "WarmGlowPortfolio                     \n",
-       "WarmGlowPortfolio                     \n",
-       "WealthPortfolio               41.345  \n",
-       "WealthPortfolio                9.396  \n",
-       "WealthPortfolioShareOnly              \n",
-       "WealthPortfolioShareOnly              "
+       "                Name  criterion   CRRA WealthShare  BeqFac BeqShift\n",
+       "0          Portfolio      0.895  6.374                             \n",
+       "1  WarmGlowPortfolio      7.719  4.706              46.465   16.966\n",
+       "2    WealthPortfolio      0.347  3.386       0.536                 "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -224,20 +132,48 @@
    "source": [
     "def format_df(df):\n",
     "    for col in df.columns:\n",
+    "        if col == \"Name\":\n",
+    "            continue\n",
     "        # Check if column is of float type\n",
     "        if col in params_to_keep:\n",
     "            df[col] = df[col].astype(float).round(3).fillna(\"\")\n",
-    "            pass\n",
     "        # Check if column contains only 0 and 1\n",
     "        else:\n",
     "            df[col] = df[col].map({0: \"\", 1: \"✔️\"})\n",
     "    return df\n",
     "\n",
     "\n",
-    "df = pd.DataFrame(parameters).set_index(\"Name\")\n",
-    "format_df(df).sort_index()"
+    "# Define the order of columns\n",
+    "column_order = [\"Name\", \"criterion\", \"CRRA\",\n",
+    "                \"WealthShare\", \"BeqFac\", \"BeqShift\"]\n",
+    "\n",
+    "df = pd.DataFrame(parameters)\n",
+    "formatted_df = format_df(df)[column_order].sort_index()\n",
+    "formatted_df\n",
+    "\n",
+    "\n",
+    "# Life cycle portfolio choice\n",
+    "# Bequest portfolio Choice\n",
+    "# TRP Life cycle portfolio choice\n",
+    "# leave out wealth shift"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "formatted_df.to_latex(\"../../content/tables/parameters.tex\", index=False)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/code/run_all.py b/code/run_all.py
index b9d270e..cb47c8b 100644
--- a/code/run_all.py
+++ b/code/run_all.py
@@ -2,8 +2,8 @@
 from estimark.options import low_resource
 
 agent_names = [
-    # "Portfolio",
-    # "WarmGlowPortfolio",
+    "Portfolio",
+    "WarmGlowPortfolio",
     "WealthPortfolio",
 ]
 
@@ -11,18 +11,14 @@
 # Ask the user which replication to run, and run it:
 def run_replication():
     for agent_name in agent_names:
-        for sub_stock in [0, 1]:
-            temp_agent_name = agent_name
-            if sub_stock:
-                temp_agent_name += "Sub(Stock)Market"
 
-            replication_specs = low_resource.copy()
-            replication_specs["agent_name"] = temp_agent_name
-            replication_specs["save_dir"] = "content/tables/TRP2"
+        replication_specs = low_resource.copy()
+        replication_specs["agent_name"] = agent_name
+        replication_specs["save_dir"] = "content/tables/TRP"
 
-            print("Model: ", replication_specs["agent_name"])
+        print("Model: ", replication_specs["agent_name"])
 
-            estimate(**replication_specs)
+        estimate(**replication_specs)
 
     print("All replications complete.")
 
diff --git a/content/tables/TRP/PortfolioSub(Stock)Market_estimate_results.csv b/content/tables/TRP/PortfolioSub(Stock)Market_estimate_results.csv
deleted file mode 100644
index 7cb0580..0000000
--- a/content/tables/TRP/PortfolioSub(Stock)Market_estimate_results.csv
+++ /dev/null
@@ -1,3409 +0,0 @@
-CRRA,3.4761873563115224
-time_to_estimate,51.52127408981323
-params,{'CRRA': 3.4761873563115224}
-criterion,18.32824621546995
-start_criterion,0.769823566069069
-start_params,{'CRRA': 3.506741858886719}
-algorithm,multistart_tranquilo_ls
-direction,minimize
-n_free,1
-message,Absolute criterion change smaller than tolerance.
-success,
-n_criterion_evaluations,
-n_derivative_evaluations,
-n_iterations,
-history,"{'params': [{'CRRA': 3.506741858886719}, {'CRRA': 3.156067672998047}, {'CRRA': 3.857416044775391}, {'CRRA': 3.156067672998047}, {'CRRA': 3.156067672998047}, {'CRRA': 3.857416044775391}, {'CRRA': 3.156067672998047}, {'CRRA': 3.156067672998047}, {'CRRA': 3.857416044775391}, {'CRRA': 3.156067672998047}, {'CRRA': 3.857416044775391}, {'CRRA': 3.156067672998047}, {'CRRA': 3.857416044775391}, {'CRRA': 3.7329319926014666}, {'CRRA': 3.682078951831055}, {'CRRA': 3.594410405358887}, {'CRRA': 3.462907585650635}, {'CRRA': 3.4515731415346496}, {'CRRA': 3.4738661539596563}, {'CRRA': 3.4750860112745987}, {'CRRA': 3.477088976799838}, {'CRRA': 3.4825280018010147}, {'CRRA': 3.4880475451088593}, {'CRRA': 3.4716096926453277}, {'CRRA': 3.4743493347225827}, {'CRRA': 3.478458797838466}, {'CRRA': 3.477773887319152}, {'CRRA': 3.476746521540181}, {'CRRA': 3.477431432059495}, {'CRRA': 3.477088976799838}, {'CRRA': 3.476575293910353}, {'CRRA': 3.476232838650696}, {'CRRA': 3.47691774917001}, {'CRRA': 3.475890383391039}, {'CRRA': 3.4760616110208677}, {'CRRA': 3.4761472248357816}, {'CRRA': 3.4759759972059534}, {'CRRA': 3.476232838650696}, {'CRRA': 3.4761900317432386}, {'CRRA': 3.476275645558153}, {'CRRA': 3.4762328386506955}, {'CRRA': 3.47616862828951}, {'CRRA': 3.4761793300163744}, {'CRRA': 3.476195382606671}, {'CRRA': 3.4761873563115224}], 'criterion': [18.331685735235567, 18.85141327641509, 18.781224297805664, 18.85141327641509, 18.85141327641509, 18.781224297805664, 18.85141327641509, 18.85141327641509, 18.781224297805664, 18.85141327641509, 18.781224297805664, 18.85141327641509, 18.781224297805664, 18.539734759687963, 18.46460352957699, 18.37422823352925, 18.330919932338045, 18.33182660211879, 18.329610094874507, 18.329174632156235, 18.328624498210143, 18.329787669175076, 18.331617937354878, 18.331570600810426, 18.32922028717803, 18.329203520243695, 18.328991724557877, 18.328505154732994, 18.328780865247694, 18.328624498210143, 18.32841995457031, 18.328263453054372, 18.328565607712658, 18.328444597254837, 18.32828299329787, 18.32825711336178, 18.328360295077562, 18.328263453054372, 18.328247015084084, 18.328279900728017, 18.328263453054824, 18.328251299268185, 18.32824839383649, 18.328249068850873, 18.328246215469946], 'runtime': [0.0, 1.3381362649961375, 1.5250998559931759, 1.732069171994226, 1.9417264210060239, 2.1487989710003603, 2.3627909340139013, 2.5827782599953935, 2.7903999169939198, 2.99710913101444, 3.1823381860158406, 3.377834867016645, 3.689269199996488, 5.000087497988716, 6.068661086988868, 7.157922853017226, 8.22855682199588, 9.313979091006331, 10.36488386199926, 11.445925898995483, 12.514805935003096, 13.584923074988183, 14.740701767994324, 15.813816025009146, 16.90381552799954, 17.979513366997708, 19.0562784789945, 20.10668119598995, 21.163388766988646, 22.221325504011475, 23.296941690990934, 24.48212526901625, 25.575156384002184, 26.674849720991915, 27.75130777800223, 28.828439616016112, 29.908796656003688, 30.981264631001977, 32.05493979199673, 33.13654309499543, 34.29326524899807, 35.35521415100084, 36.430194757005665, 37.48134312700131, 38.537288337014616], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]}"
-convergence_report,
-multistart_info,"{'start_parameters': [{'CRRA': 3.506741858886719}], 'local_optima': [Minimize with 1 free parameters terminated.
-
-The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance.
-
-Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
-
-                             one_step     five_steps 
-relative_criterion_change  4.363e-08*    1.413e-05   
-relative_params_change     7.696e-07*    0.0001609   
-absolute_criterion_change  7.996e-07*    0.0002589   
-absolute_params_change     2.675e-06*    0.0005592   
-
-(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 3.506741858886719}, {'CRRA': 4.25}, {'CRRA': 5.375}, {'CRRA': 6.5}, {'CRRA': 8.75}, {'CRRA': 11.0}, {'CRRA': 13.25}, {'CRRA': 14.375}, {'CRRA': 15.5}, {'CRRA': 17.75}], 'exploration_results': array([ 18.33168574,  20.0564337 ,  26.36368616,  34.93078814,
-        54.49645909,  74.0243029 ,  91.99444646, 100.52865702,
-       108.81279178, 124.35225438])}"
-algorithm_output,"{'states': [State(trustregion=Region(center=array([3.50674186]), radius=0.35067418588867194, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=18.331685735235567, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.35067418588867194, shift=array([3.50674186])), vector_model=VectorModel(intercepts=array([ 0.02700948,  0.0620018 ,  0.06357019,  0.10869483,  0.16560032,
-        0.24478853,  0.35799583,  1.06845837,  1.35438426,  1.87457441,
-        2.06931779,  2.56316491, -0.31175465, -0.31394745, -0.36253337,
-       -0.43204872, -0.48991848]), linear_terms=array([[0.],
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-       [[0.]]]), scale=0.35067418588867194, shift=array([3.50674186])), candidate_index=44, candidate_x=array([3.47618736]), index=44, x=array([3.47618736]), fval=18.32824621546995, rho=16.94386215773618, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([38, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=2.675431716170351e-06, relative_step_length=1.0000000000376654, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 45 entries., 'history': {'params': [{'CRRA': 3.506741858886719}, {'CRRA': 3.156067672998047}, {'CRRA': 3.857416044775391}, {'CRRA': 3.156067672998047}, {'CRRA': 3.156067672998047}, {'CRRA': 3.857416044775391}, {'CRRA': 3.156067672998047}, {'CRRA': 3.156067672998047}, {'CRRA': 3.857416044775391}, {'CRRA': 3.156067672998047}, {'CRRA': 3.857416044775391}, {'CRRA': 3.156067672998047}, {'CRRA': 3.857416044775391}, {'CRRA': 3.7329319926014666}, {'CRRA': 3.682078951831055}, {'CRRA': 3.594410405358887}, {'CRRA': 3.462907585650635}, {'CRRA': 3.4515731415346496}, {'CRRA': 3.4738661539596563}, {'CRRA': 3.4750860112745987}, {'CRRA': 3.477088976799838}, {'CRRA': 3.4825280018010147}, {'CRRA': 3.4880475451088593}, {'CRRA': 3.4716096926453277}, {'CRRA': 3.4743493347225827}, {'CRRA': 3.478458797838466}, {'CRRA': 3.477773887319152}, {'CRRA': 3.476746521540181}, {'CRRA': 3.477431432059495}, {'CRRA': 3.477088976799838}, {'CRRA': 3.476575293910353}, {'CRRA': 3.476232838650696}, {'CRRA': 3.47691774917001}, {'CRRA': 3.475890383391039}, {'CRRA': 3.4760616110208677}, {'CRRA': 3.4761472248357816}, {'CRRA': 3.4759759972059534}, {'CRRA': 3.476232838650696}, {'CRRA': 3.4761900317432386}, {'CRRA': 3.476275645558153}, {'CRRA': 3.4762328386506955}, {'CRRA': 3.47616862828951}, {'CRRA': 3.4761793300163744}, {'CRRA': 3.476195382606671}, {'CRRA': 3.4761873563115224}], 'criterion': [18.331685735235567, 18.85141327641509, 18.781224297805664, 18.85141327641509, 18.85141327641509, 18.781224297805664, 18.85141327641509, 18.85141327641509, 18.781224297805664, 18.85141327641509, 18.781224297805664, 18.85141327641509, 18.781224297805664, 18.539734759687963, 18.46460352957699, 18.37422823352925, 18.330919932338045, 18.33182660211879, 18.329610094874507, 18.329174632156235, 18.328624498210143, 18.329787669175076, 18.331617937354878, 18.331570600810426, 18.32922028717803, 18.329203520243695, 18.328991724557877, 18.328505154732994, 18.328780865247694, 18.328624498210143, 18.32841995457031, 18.328263453054372, 18.328565607712658, 18.328444597254837, 18.32828299329787, 18.32825711336178, 18.328360295077562, 18.328263453054372, 18.328247015084084, 18.328279900728017, 18.328263453054824, 18.328251299268185, 18.32824839383649, 18.328249068850873, 18.328246215469946], 'runtime': [0.0, 1.3381362649961375, 1.5250998559931759, 1.732069171994226, 1.9417264210060239, 2.1487989710003603, 2.3627909340139013, 2.5827782599953935, 2.7903999169939198, 2.99710913101444, 3.1823381860158406, 3.377834867016645, 3.689269199996488, 5.000087497988716, 6.068661086988868, 7.157922853017226, 8.22855682199588, 9.313979091006331, 10.36488386199926, 11.445925898995483, 12.514805935003096, 13.584923074988183, 14.740701767994324, 15.813816025009146, 16.90381552799954, 17.979513366997708, 19.0562784789945, 20.10668119598995, 21.163388766988646, 22.221325504011475, 23.296941690990934, 24.48212526901625, 25.575156384002184, 26.674849720991915, 27.75130777800223, 28.828439616016112, 29.908796656003688, 30.981264631001977, 32.05493979199673, 33.13654309499543, 34.29326524899807, 35.35521415100084, 36.430194757005665, 37.48134312700131, 38.537288337014616], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 3.50674186],
-       [ 4.25      ],
-       [ 5.375     ],
-       [ 6.5       ],
-       [ 8.75      ],
-       [11.        ],
-       [13.25      ],
-       [14.375     ],
-       [15.5       ],
-       [17.75      ]]), 'exploration_results': array([ 18.33168574,  20.0564337 ,  26.36368616,  34.93078814,
-        54.49645909,  74.0243029 ,  91.99444646, 100.52865702,
-       108.81279178, 124.35225438])}}"
diff --git a/content/tables/TRP/Portfolio_estimate_results.csv b/content/tables/TRP/Portfolio_estimate_results.csv
index 7e3ca0b..4376b27 100644
--- a/content/tables/TRP/Portfolio_estimate_results.csv
+++ b/content/tables/TRP/Portfolio_estimate_results.csv
@@ -1,9 +1,9 @@
-CRRA,6.374030002146488
-time_to_estimate,141.82105326652527
-params,{'CRRA': 6.374030002146488}
-criterion,0.895436577156568
-start_criterion,1.4314871471445485
-start_params,{'CRRA': 6.320605981087387}
+CRRA,6.29657511421741
+time_to_estimate,144.33734798431396
+params,{'CRRA': 6.29657511421741}
+criterion,0.9011997370633763
+start_criterion,1.3664989106503433
+start_params,{'CRRA': 6.374030002146401}
 algorithm,multistart_tranquilo_ls
 direction,minimize
 n_free,1
@@ -12,24 +12,24 @@ success,
 n_criterion_evaluations,
 n_derivative_evaluations,
 n_iterations,
-history,"{'params': [{'CRRA': 6.320605981087387}, {'CRRA': 5.688545382978648}, {'CRRA': 6.9526665791961255}, {'CRRA': 5.688545382978648}, {'CRRA': 5.688545382978648}, {'CRRA': 6.9526665791961255}, {'CRRA': 5.688545382978648}, {'CRRA': 5.688545382978648}, {'CRRA': 6.9526665791961255}, {'CRRA': 5.688545382978648}, {'CRRA': 6.9526665791961255}, {'CRRA': 5.688545382978648}, {'CRRA': 6.9526665791961255}, {'CRRA': 6.372356566128265}, {'CRRA': 6.258502784401016}, {'CRRA': 6.28588298365918}, {'CRRA': 6.378159608140937}, {'CRRA': 6.275271533691997}, {'CRRA': 6.591301832746367}, {'CRRA': 6.433286683219182}, {'CRRA': 6.354279108455589}, {'CRRA': 6.5122942579827745}, {'CRRA': 6.4332866832191815}, {'CRRA': 6.314775321073793}, {'CRRA': 6.374031002146488}, {'CRRA': 6.334527214764692}, {'CRRA': 6.393782895837386}, {'CRRA': 6.383906948991937}, {'CRRA': 6.369093028723763}, {'CRRA': 6.37649998885785}, {'CRRA': 6.375265495502169}, {'CRRA': 6.374648248824329}, {'CRRA': 6.373722378807567}, {'CRRA': 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46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101]}"
+history,"{'params': [{'CRRA': 6.374030002146401}, {'CRRA': 5.736627001931761}, {'CRRA': 7.0114330023610405}, {'CRRA': 5.736627001931761}, {'CRRA': 5.736627001931761}, {'CRRA': 7.0114330023610405}, {'CRRA': 5.736627001931761}, {'CRRA': 5.736627001931761}, {'CRRA': 7.0114330023610405}, {'CRRA': 5.736627001931761}, {'CRRA': 7.0114330023610405}, {'CRRA': 5.736627001931761}, {'CRRA': 7.0114330023610405}, {'CRRA': 5.736627001931761}, {'CRRA': 6.361380431680054}, {'CRRA': 6.349087996972414}, {'CRRA': 6.030386496865094}, {'CRRA': 6.356332645487532}, {'CRRA': 6.384217630019835}, {'CRRA': 6.196981895433872}, {'CRRA': 6.276657270460702}, {'CRRA': 6.117306520407042}, {'CRRA': 6.196981895433872}, {'CRRA': 6.316494957974117}, {'CRRA': 6.29657611421741}, {'CRRA': 6.256738426703994}, {'CRRA': 6.276657270460702}, {'CRRA': 6.286616692339056}, {'CRRA': 6.291596403278233}, {'CRRA': 6.299065969686998}, {'CRRA': 6.297821041952204}, {'CRRA': 6.295953650350013}, {'CRRA': 6.296264882283711}, {'CRRA': 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{'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.29657611421741}, {'CRRA': 6.29657611421741}, {'CRRA': 6.29657611421741}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.29657611421741}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.29657611421741}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296575675597013}, {'CRRA': 6.29657611421741}, {'CRRA': 6.296574114217409}, {'CRRA': 6.296574114217409}], 'criterion': [1.0261223490300269, 1.1531032939473036, 1.1600351271978586, 1.1531032939473036, 1.1531032939473036, 1.1600351271978586, 1.1531032939473036, 1.1531032939473036, 1.1600351271978586, 1.1531032939473036, 1.1600351271978586, 1.1531032939473036, 1.1600351271978586, 1.1531032939473036, 1.035358783605356, 1.0086641123528395, 1.0038717698180601, 0.9483696035077182, 1.0186413117362412, 1.0102274001070377, 0.9397985927899563, 1.0073621842072586, 1.0878352925386428, 1.0652872272305243, 0.926970389682947, 0.9784301973193992, 0.9716326492110329, 0.991920312592512, 1.000395945774921, 0.9748237943628951, 1.0289424683975323, 0.9964793810343495, 1.0009658635314844, 0.9731470116598234, 1.0382843955219399, 0.9803830266345497, 0.9468697534365581, 1.0398273178415605, 0.949917982410243, 1.006921838990962, 0.9614383536011047, 0.956231857066571, 1.000281630405482, 0.9011997370633763, 1.0043714231398144, 0.9866043200323095, 0.9631047242070258, 0.9519465158920384, 1.0493633689906203, 0.992825267042263, 0.9764981412863798, 0.9110817827635358, 1.0074893199273596, 1.007041407440611, 1.0205375644448083, 0.9635455516830169, 0.9750799790718206, 0.978202873039255, 0.9531924537809382, 1.0717077979172784, 0.9566492237159154, 0.9989879772283424, 0.9513529587331968, 0.9600195897190086, 1.041824634514844, 0.9455476619125787, 0.9608460918318201, 0.9436899949170492, 1.0311794878505747, 0.9837842985445832, 1.0654999541168788, 0.967819153725156, 1.007916032937257, 1.0008002866072994, 1.0309338017474108, 0.9965433058526093, 0.9620065957952713, 0.9776158838262109, 1.010357155857373, 1.072336581099133, 0.9346879806107663, 0.979583891959375, 0.957296111066683, 1.048757944339067, 0.9904347242727628, 1.0190887106374567, 0.9934483362875349, 0.9695270039263303, 1.0349613834081737, 0.9077786579579206, 1.0140711327624805, 0.9424592404452095, 0.9710568093008604, 0.9684666808232975, 1.0033752733935433, 0.967127867660258, 1.0060058111231347, 0.9873200897404102, 0.9891486848082092, 0.9718946621410105, 0.9509476608589312, 1.0576038811684425, 1.04075816523018, 1.0016818358628736, 0.9444487322956382, 0.9755219851169684, 0.9944766113036223, 1.0120726462049545, 1.0223798381953981, 1.0312034159176031, 1.023035516030945, 0.985581386835777, 1.045609953182023, 1.0048919368562614, 0.9853452144283645], 'runtime': [0.0, 3.0254122269980144, 3.213080532004824, 3.471466543007409, 3.6675723450025544, 3.945269768999424, 4.123211897996953, 4.391865629993845, 4.634594130999176, 4.873877653008094, 5.06490444400697, 5.295465546980267, 5.508014694991289, 19.48964652098948, 20.6531084899907, 21.703819187998306, 22.755856600997504, 23.817381478002062, 24.871905570995295, 25.924397726979805, 26.977923155995086, 28.048674616002245, 29.13243499698001, 30.29069487398374, 31.356947789987316, 32.42214214699925, 33.48172060100478, 34.542108650988666, 35.598250126000494, 36.64574316699873, 37.71886341899517, 38.77214415898197, 39.9163770529849, 40.96331929799635, 42.091774387983605, 43.21559783798875, 44.35532637598226, 45.44362674400327, 46.55186722599319, 47.655307487002574, 48.878156624996336, 50.10819440099294, 51.24104041498504, 52.396936535980785, 53.497915839980124, 54.67177055098, 55.76643407999654, 56.83563071498065, 57.93813267798396, 59.071907236997504, 60.28531087099691, 61.38101266499143, 62.481017563986825, 63.55210944497958, 64.63686765098828, 65.72370117899845, 66.8165898519801, 67.93921485199826, 69.20722721298807, 70.63138605700806, 71.86001750497962, 73.01747709899792, 74.11849385799724, 75.19314197299536, 76.25712275100523, 77.30609403198468, 78.36302636700566, 79.41567176699755, 80.59555132099194, 81.65214887598995, 82.71486713300692, 83.76768530899426, 84.87312564498279, 85.92956575800781, 86.99000929598697, 88.04363830099464, 89.09527927500312, 90.23464079000405, 91.28985196299618, 92.34907383398968, 93.41086707898648, 94.58495129400399, 95.74368918099208, 96.81647325400263, 97.88946050099912, 98.9447108739987, 100.08399510799791, 101.19633440999314, 102.40891915600514, 103.51557467298699, 104.66122408999945, 105.80039653897984, 106.89547288999893, 107.97579510498326, 109.05654924199916, 110.24342396997963, 111.45188566698926, 112.71343827899545, 113.86585319598089, 114.91961285099387, 116.00741267300327, 117.12092391500482, 118.19486285699531, 119.27271664398722, 120.49116064599366, 121.55139168098685, 122.63866486999905, 123.69799460098147, 124.77904707100242, 125.84307079398423, 126.93732125899987, 128.05140812098398, 129.17956323799444, 130.32409440999618, 131.38521518200287], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103]}"
 convergence_report,
-multistart_info,"{'start_parameters': [{'CRRA': 6.320605981087387}], 'local_optima': [Minimize with 1 free parameters terminated.
+multistart_info,"{'start_parameters': [{'CRRA': 6.374030002146401}], 'local_optima': [Minimize with 1 free parameters terminated.
 
 Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
 
                              one_step    five_steps
-relative_criterion_change    0.03138      0.1271   
-relative_params_change     1.569e-07*    0.01549   
-absolute_criterion_change     0.0281      0.1138   
-absolute_params_change         1e-06*    0.09876   
+relative_criterion_change     0.0286      0.1192   
+relative_params_change     1.588e-07*    0.04228   
+absolute_criterion_change    0.02577      0.1075   
+absolute_params_change         1e-06*     0.2662   
 
-(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 6.320605981087387}, {'CRRA': 6.5}, {'CRRA': 5.375}, {'CRRA': 8.75}, {'CRRA': 4.25}, {'CRRA': 11.0}, {'CRRA': 13.25}, {'CRRA': 14.375}, {'CRRA': 15.5}, {'CRRA': 17.75}], 'exploration_results': array([0.92284715, 0.9689525 , 1.43690922, 2.25496692, 3.67693587,
+(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 6.374030002146401}, {'CRRA': 6.5}, {'CRRA': 5.375}, {'CRRA': 8.75}, {'CRRA': 4.25}, {'CRRA': 11.0}, {'CRRA': 13.25}, {'CRRA': 14.375}, {'CRRA': 15.5}, {'CRRA': 17.75}], 'exploration_results': array([0.92003877, 0.9689525 , 1.43690922, 2.25496692, 3.67693587,
        3.97725656, 5.89387611, 6.6580109 , 7.17335069, 9.28222803])}"
-algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]), radius=0.6320605981087387, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=1.0251060829318852, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.6320605981087387, shift=array([6.32060598])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+algorithm_output,"{'states': [State(trustregion=Region(center=array([6.37403]), radius=0.6374030002146401, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=1.0261223490300269, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.6374030002146401, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -77,10 +77,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=0, candidate_x=array([6.32060598]), index=0, x=array([6.32060598]), fval=1.0251060829318852, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([6.32060598]), radius=0.6320605981087387, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=1.0403877497190288, linear_terms=array([-0.0192514]), square_terms=array([[0.23512879]]), scale=0.6320605981087387, shift=array([6.32060598])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=0, candidate_x=array([6.37403]), index=0, x=array([6.37403]), fval=1.0261223490300269, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([6.37403]), radius=0.6374030002146401, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), model=ScalarModel(intercept=1.03922718390553, linear_terms=array([0.00461476]), square_terms=array([[0.23253441]]), scale=0.6374030002146401, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -128,10 +128,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=13, candidate_x=array([6.37235657]), index=13, x=array([6.37235657]), fval=1.0160204843044787, rho=11.52828347349067, accepted=True, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.05175058504087815, relative_step_length=0.08187598656794465, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37235657]), radius=0.6320605981087387, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0,  2,  8, 10, 12, 13]), model=ScalarModel(intercept=1.0215118655511004, linear_terms=array([0.03599326]), square_terms=array([[0.19981701]]), scale=0.6320605981087387, shift=array([6.37235657])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=14, candidate_x=array([6.36138043]), index=0, x=array([6.37403]), fval=1.0261223490300269, rho=-201.70830611242548, accepted=False, new_indices=array([ 1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=0.31870150010732007, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=1.0366868269112581, linear_terms=array([0.00453863]), square_terms=array([[0.0579932]]), scale=0.31870150010732007, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -179,10 +179,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=14, candidate_x=array([6.25850278]), index=13, x=array([6.37235657]), fval=1.0160204843044787, rho=-6.160183981647245, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  8, 10, 12, 13]), old_indices_discarded=array([ 1,  3,  4,  5,  6,  7,  9, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37235657]), radius=0.31603029905436936, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0,  8, 10, 12, 13, 14]), model=ScalarModel(intercept=1.0267893707363318, linear_terms=array([0.01395961]), square_terms=array([[0.05101742]]), scale=0.31603029905436936, shift=array([6.37235657])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=15, candidate_x=array([6.349088]), index=15, x=array([6.349088]), fval=1.0086641123528395, rho=98.30118363118868, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 10, 11, 12, 13, 14]), old_indices_discarded=array([1, 2, 3, 4, 5, 6, 7, 8, 9]), step_length=0.024942005173986814, relative_step_length=0.07826133596982694, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.349088]), radius=0.31870150010732007, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 11, 13, 14, 15, 16]), model=ScalarModel(intercept=1.0137055022251003, linear_terms=array([-0.00155151]), square_terms=array([[0.06825286]]), scale=0.31870150010732007, shift=array([6.349088])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -230,10 +230,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=15, candidate_x=array([6.28588298]), index=15, x=array([6.28588298]), fval=1.009209012984012, rho=3.5665060661249157, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  8, 10, 12, 13, 14]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  9, 11]), step_length=0.08647358246908521, relative_step_length=0.2736243414882458, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.28588298]), radius=0.31603029905436936, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0,  9, 11, 13, 14, 15]), model=ScalarModel(intercept=1.0215447247561733, linear_terms=array([-0.01977416]), square_terms=array([[0.06772284]]), scale=0.31603029905436936, shift=array([6.28588298])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=17, candidate_x=array([6.35633265]), index=17, x=array([6.35633265]), fval=0.9483696035077183, rho=3419.168086364822, accepted=True, new_indices=array([16]), old_indices_used=array([ 0, 11, 13, 14, 15]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 12]), step_length=0.0072446485151180795, relative_step_length=0.022731767853864836, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35633265]), radius=0.31870150010732007, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.9982668600888471, linear_terms=array([-0.00587143]), square_terms=array([[0.06710545]]), scale=0.31870150010732007, shift=array([6.35633265])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -281,10 +281,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=16, candidate_x=array([6.37815961]), index=16, x=array([6.37815961]), fval=0.9536680614253905, rho=19.238986279195796, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  9, 11, 13, 14, 15]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8, 10, 12]), step_length=0.092276624481757, relative_step_length=0.29198663785677675, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37815961]), radius=0.31603029905436936, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=1.010452400479926, linear_terms=array([0.0172892]), square_terms=array([[0.05310538]]), scale=0.31603029905436936, shift=array([6.37815961])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=18, candidate_x=array([6.38421763]), index=17, x=array([6.35633265]), fval=0.9483696035077183, rho=-273.57733729961365, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35633265]), radius=0.15935075005366003, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=1.0053794558515663, linear_terms=array([0.02496362]), square_terms=array([[0.02297852]]), scale=0.15935075005366003, shift=array([6.35633265])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -332,10 +332,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=17, candidate_x=array([6.27527153]), index=17, x=array([6.27527153]), fval=0.9524968151553904, rho=0.41616642491251093, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]), step_length=0.10288807444893955, relative_step_length=0.325563956230788, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27527153]), radius=0.31603029905436936, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=1.0045189771362606, linear_terms=array([-0.06412789]), square_terms=array([[0.06173571]]), scale=0.31603029905436936, shift=array([6.27527153])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=19, candidate_x=array([6.1969819]), index=17, x=array([6.35633265]), fval=0.9483696035077183, rho=-4.590777056347129, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35633265]), radius=0.07967537502683002, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 17, 18, 19]), model=ScalarModel(intercept=1.006776384006398, linear_terms=array([0.00427617]), square_terms=array([[0.0040298]]), scale=0.07967537502683002, shift=array([6.35633265])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -383,10 +383,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=18, candidate_x=array([6.59130183]), index=17, x=array([6.27527153]), fval=0.9524968151553904, rho=-2.6914652702645263, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27527153]), radius=0.15801514952718468, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=1.0045189771362604, linear_terms=array([-0.03206395]), square_terms=array([[0.01543393]]), scale=0.15801514952718468, shift=array([6.27527153])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=20, candidate_x=array([6.27665727]), index=20, x=array([6.27665727]), fval=0.9397985927899563, rho=3.790354442652867, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 17, 18, 19]), old_indices_discarded=array([16]), step_length=0.07967537502682998, relative_step_length=0.9999999999999994, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27665727]), radius=0.15935075005366003, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 18, 19, 20]), model=ScalarModel(intercept=0.9937779072188607, linear_terms=array([0.02977405]), square_terms=array([[0.02375181]]), scale=0.15935075005366003, shift=array([6.27665727])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -434,10 +434,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=19, candidate_x=array([6.43328668]), index=17, x=array([6.27527153]), fval=0.9524968151553904, rho=-2.276794831504726, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27527153]), radius=0.07900757476359234, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=1.0045189771362606, linear_terms=array([-0.01603197]), square_terms=array([[0.00385848]]), scale=0.07900757476359234, shift=array([6.27527153])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=21, candidate_x=array([6.11730652]), index=20, x=array([6.27665727]), fval=0.9397985927899563, rho=-3.7748946860083574, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 18, 19, 20]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 16, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27665727]), radius=0.07967537502683002, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 17, 19, 20]), model=ScalarModel(intercept=0.9876587888585374, linear_terms=array([0.00695975]), square_terms=array([[0.00480284]]), scale=0.07967537502683002, shift=array([6.27665727])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -485,10 +485,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=20, candidate_x=array([6.35427911]), index=20, x=array([6.35427911]), fval=0.9340945430424806, rho=1.3048727658870296, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 14, 15, 16, 17]), old_indices_discarded=array([18, 19]), step_length=0.0790075747635921, relative_step_length=0.999999999999997, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35427911]), radius=0.15801514952718468, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19, 20]), model=ScalarModel(intercept=0.9802636388806935, linear_terms=array([-0.01901788]), square_terms=array([[0.01410849]]), scale=0.15801514952718468, shift=array([6.35427911])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=22, candidate_x=array([6.1969819]), index=20, x=array([6.27665727]), fval=0.9397985927899563, rho=-32.476038926444666, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 17, 19, 20]), old_indices_discarded=array([16, 18, 21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27665727]), radius=0.03983768751341501, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([14, 15, 17, 19, 20, 22]), model=ScalarModel(intercept=1.0059198573411452, linear_terms=array([-0.01138883]), square_terms=array([[0.00061497]]), scale=0.03983768751341501, shift=array([6.27665727])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -536,10 +536,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=21, candidate_x=array([6.51229426]), index=20, x=array([6.35427911]), fval=0.9340945430424806, rho=-5.076605751170216, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 19, 20]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 15, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35427911]), radius=0.07900757476359234, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 15, 16, 17, 20]), model=ScalarModel(intercept=0.9776664671616091, linear_terms=array([-0.00745835]), square_terms=array([[0.00368061]]), scale=0.07900757476359234, shift=array([6.35427911])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=23, candidate_x=array([6.31649496]), index=20, x=array([6.27665727]), fval=0.9397985927899563, rho=-11.324312750705486, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 17, 19, 20, 22]), old_indices_discarded=array([ 0, 18, 21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27665727]), radius=0.019918843756707504, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([15, 17, 19, 20, 22, 23]), model=ScalarModel(intercept=1.0091309106068849, linear_terms=array([-0.00695298]), square_terms=array([[0.00015443]]), scale=0.019918843756707504, shift=array([6.27665727])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -587,10 +587,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=22, candidate_x=array([6.43328668]), index=20, x=array([6.35427911]), fval=0.9340945430424806, rho=-28.88787876275111, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 15, 16, 17, 20]), old_indices_discarded=array([14, 18, 19, 21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35427911]), radius=0.03950378738179617, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 15, 16, 20, 22]), model=ScalarModel(intercept=1.001603598709247, linear_terms=array([0.01386041]), square_terms=array([[0.00165678]]), scale=0.03950378738179617, shift=array([6.35427911])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=24, candidate_x=array([6.29657611]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=1.8657127314789879, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([15, 17, 19, 20, 22, 23]), old_indices_discarded=array([ 0, 14]), step_length=0.019918843756707716, relative_step_length=1.0000000000000107, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.03983768751341501, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([14, 15, 17, 20, 23, 24]), model=ScalarModel(intercept=0.9633353155766339, linear_terms=array([0.02691519]), square_terms=array([[0.00406671]]), scale=0.03983768751341501, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -638,10 +638,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=23, candidate_x=array([6.31477532]), index=20, x=array([6.35427911]), fval=0.9340945430424806, rho=-10.0623315524537, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 15, 16, 20, 22]), old_indices_discarded=array([14, 17, 19, 21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35427911]), radius=0.019751893690898085, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 15, 16, 20, 23]), model=ScalarModel(intercept=0.9868075154288375, linear_terms=array([-0.01433558]), square_terms=array([[0.0002463]]), scale=0.019751893690898085, shift=array([6.35427911])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=25, candidate_x=array([6.25673843]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-2.068167896660778, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 17, 20, 23, 24]), old_indices_discarded=array([ 0, 18, 19, 21, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.019918843756707504, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([15, 17, 20, 23, 24, 25]), model=ScalarModel(intercept=0.9719824934312715, linear_terms=array([0.00559189]), square_terms=array([[0.00043118]]), scale=0.019918843756707504, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -689,10 +689,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=24, candidate_x=array([6.374031]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=0.7426784515001978, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 15, 16, 20, 23]), old_indices_discarded=array([14, 17, 19, 22]), step_length=0.01975189369089847, relative_step_length=1.0000000000000195, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.03950378738179617, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 16, 20, 22, 24]), model=ScalarModel(intercept=0.9893109254111845, linear_terms=array([0.02816249]), square_terms=array([[0.00292704]]), scale=0.03950378738179617, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=26, candidate_x=array([6.27665727]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-8.307248539999298, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 17, 20, 23, 24, 25]), old_indices_discarded=array([ 0, 14, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.009959421878353752, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([20, 23, 24, 25, 26]), model=ScalarModel(intercept=0.9876667111577927, linear_terms=array([0.0114311]), square_terms=array([[0.00034082]]), scale=0.009959421878353752, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -740,10 +740,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=25, candidate_x=array([6.33452721]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-1.9674909845378, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 16, 20, 22, 24]), old_indices_discarded=array([14, 15, 17, 19, 21, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.019751893690898085, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 16, 20, 24, 25]), model=ScalarModel(intercept=0.9539954380175584, linear_terms=array([-0.01694482]), square_terms=array([[0.00053969]]), scale=0.019751893690898085, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=27, candidate_x=array([6.28661669]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-5.767845500764676, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.004979710939176876, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([20, 23, 24, 26, 27]), model=ScalarModel(intercept=0.9898061757284117, linear_terms=array([0.01086956]), square_terms=array([[0.00023498]]), scale=0.004979710939176876, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -791,10 +791,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=26, candidate_x=array([6.3937829]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-2.690157305274892, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 16, 20, 24, 25]), old_indices_discarded=array([15, 19, 22, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.009875946845449042, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([13, 16, 20, 24, 25, 26]), model=ScalarModel(intercept=0.9607835489629618, linear_terms=array([-4.85050354e-05]), square_terms=array([[5.93867423e-05]]), scale=0.009875946845449042, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=28, candidate_x=array([6.2915964]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-6.828966566759263, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 24, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.002489855469588438, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 27, 28]), model=ScalarModel(intercept=0.9397592375412753, linear_terms=array([-0.01579103]), square_terms=array([[0.00039329]]), scale=0.002489855469588438, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -842,10 +842,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=27, candidate_x=array([6.38390695]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-3581.2018596441158, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 16, 20, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.004937973422724521, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([13, 16, 20, 24, 26, 27]), model=ScalarModel(intercept=0.9613170428384746, linear_terms=array([0.00468562]), square_terms=array([[8.14369693e-05]]), scale=0.004937973422724521, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=29, candidate_x=array([6.29906597]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-3.0686296821552284, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.001244927734794219, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 28, 29]), model=ScalarModel(intercept=0.9622473445073738, linear_terms=array([-0.00621048]), square_terms=array([[5.70291544e-05]]), scale=0.001244927734794219, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -893,10 +893,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=28, candidate_x=array([6.36909303]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-16.517494651838753, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 16, 20, 24, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.0024689867113622606, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([13, 16, 24, 27, 28]), model=ScalarModel(intercept=0.9771354368996926, linear_terms=array([-0.00243716]), square_terms=array([[4.0235556e-05]]), scale=0.0024689867113622606, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=30, candidate_x=array([6.29782104]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-16.495099323898966, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.0006224638673971095, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 29, 30]), model=ScalarModel(intercept=0.9507850276343404, linear_terms=array([0.01176136]), square_terms=array([[0.00044094]]), scale=0.0006224638673971095, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -944,10 +944,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=29, candidate_x=array([6.37649999]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-20.934942146147495, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 16, 24, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.0012344933556811303, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([13, 16, 24, 28, 29]), model=ScalarModel(intercept=0.9723306091896402, linear_terms=array([-0.00687119]), square_terms=array([[6.783946e-05]]), scale=0.0012344933556811303, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=31, candidate_x=array([6.29595365]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-6.022844977484244, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.00031123193369855475, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 30, 31]), model=ScalarModel(intercept=0.9770042041592212, linear_terms=array([0.00798633]), square_terms=array([[0.00013303]]), scale=0.00031123193369855475, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -995,10 +995,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=30, candidate_x=array([6.3752655]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-15.6742482390045, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 16, 24, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.0006172466778405652, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([13, 24, 29, 30]), model=ScalarModel(intercept=0.9856930933010559, linear_terms=array([-0.00146752]), square_terms=array([[3.2048337e-05]]), scale=0.0006172466778405652, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=32, candidate_x=array([6.29626488]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-9.343082191756242, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.00015561596684927738, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 31, 32]), model=ScalarModel(intercept=0.9394344463177127, linear_terms=array([-0.01653866]), square_terms=array([[0.00060385]]), scale=0.00015561596684927738, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1046,10 +1046,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=31, candidate_x=array([6.37464825]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-50.988952950892276, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 24, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.0003086233389202826, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 30, 31]), model=ScalarModel(intercept=0.9303978228444009, linear_terms=array([0.02463831]), square_terms=array([[0.00124497]]), scale=0.0003086233389202826, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=33, candidate_x=array([6.29673173]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-2.8439600418616675, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=7.780798342463869e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 32, 33]), model=ScalarModel(intercept=0.9617837838772493, linear_terms=array([-0.00652768]), square_terms=array([[4.92677082e-05]]), scale=7.780798342463869e-05, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1097,10 +1097,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=32, candidate_x=array([6.37372238]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-3.1656161632596316, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.0001543116694601413, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 31, 32]), model=ScalarModel(intercept=0.970866840040577, linear_terms=array([0.00236833]), square_terms=array([[3.09252925e-05]]), scale=0.0001543116694601413, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=34, candidate_x=array([6.29665392]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-17.117205500717674, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=3.8903991712319344e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 33, 34]), model=ScalarModel(intercept=0.9540200386931921, linear_terms=array([0.01147644]), square_terms=array([[0.00035954]]), scale=3.8903991712319344e-05, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1148,10 +1148,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=33, candidate_x=array([6.37387669]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-20.3758045253014, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=7.715583473007064e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 32, 33]), model=ScalarModel(intercept=0.9267147479348122, linear_terms=array([-0.01806129]), square_terms=array([[0.00057661]]), scale=7.715583473007064e-05, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=35, candidate_x=array([6.29653721]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-4.728176734884648, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=1.9451995856159672e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 34, 35]), model=ScalarModel(intercept=0.972188201116544, linear_terms=array([0.0117863]), square_terms=array([[0.00021062]]), scale=1.9451995856159672e-05, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1199,10 +1199,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=34, candidate_x=array([6.37410816]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-6.521729579502907, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=3.857791736503532e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 33, 34]), model=ScalarModel(intercept=0.9814421780382513, linear_terms=array([0.00759072]), square_terms=array([[6.41720368e-05]]), scale=3.857791736503532e-05, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=36, candidate_x=array([6.29655666]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-1.7035677785298975, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=9.725997928079836e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 35, 36]), model=ScalarModel(intercept=0.9249417998725926, linear_terms=array([-0.012476]), square_terms=array([[0.00042796]]), scale=9.725997928079836e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1250,10 +1250,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=35, candidate_x=array([6.37399242]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-7.4178706136732275, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=1.928895868251766e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 34, 35]), model=ScalarModel(intercept=0.970894147014174, linear_terms=array([0.01223018]), square_terms=array([[0.00020966]]), scale=1.928895868251766e-05, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=37, candidate_x=array([6.29658584]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-9.203780525169682, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=4.862998964039918e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 36, 37]), model=ScalarModel(intercept=0.9771620923084195, linear_terms=array([0.01222736]), square_terms=array([[0.00026769]]), scale=4.862998964039918e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1301,10 +1301,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=36, candidate_x=array([6.37401171]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-1.7785846257234272, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=9.64447934125883e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 35, 36]), model=ScalarModel(intercept=0.9216087520494944, linear_terms=array([-0.01315391]), square_terms=array([[0.00042281]]), scale=9.64447934125883e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=38, candidate_x=array([6.29657125]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-1.897511379820083, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=2.431499482019959e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 37, 38]), model=ScalarModel(intercept=0.959431534837272, linear_terms=array([0.01599091]), square_terms=array([[0.00047403]]), scale=2.431499482019959e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1352,10 +1352,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=37, candidate_x=array([6.37404065]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-9.067352766764817, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=4.822239670629415e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 36, 37]), model=ScalarModel(intercept=0.9760339180445036, linear_terms=array([0.01257345]), square_terms=array([[0.00026316]]), scale=4.822239670629415e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=39, candidate_x=array([6.29657368]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-5.075025280651171, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=1.2157497410099795e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39]), model=ScalarModel(intercept=0.9481238798141679, linear_terms=array([-0.00583462]), square_terms=array([[8.08382363e-05]]), scale=1.2157497410099795e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1403,10 +1403,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=38, candidate_x=array([6.37402618]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-1.8562082301201086, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=2.4111198353147076e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 37, 38]), model=ScalarModel(intercept=0.9570394711222546, linear_terms=array([0.01677996]), square_terms=array([[0.00046953]]), scale=2.4111198353147076e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=40, candidate_x=array([6.29657733]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-5.948703911554294, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40]), model=ScalarModel(intercept=0.9564868186794058, linear_terms=array([-0.00246996]), square_terms=array([[2.51370374e-05]]), scale=1e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1454,10 +1454,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=39, candidate_x=array([6.37402859]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-5.072855483429943, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=1.2055599176573538e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39]), model=ScalarModel(intercept=0.9460165876341217, linear_terms=array([-0.00587267]), square_terms=array([[8.04741154e-05]]), scale=1.2055599176573538e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=41, candidate_x=array([6.29657711]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-11.90753372784116, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40, 41]), model=ScalarModel(intercept=0.9570074462591432, linear_terms=array([-0.00235431]), square_terms=array([[1.11715152e-05]]), scale=1e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1505,10 +1505,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=40, candidate_x=array([6.37403221]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-6.026264047904024, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40]), model=ScalarModel(intercept=0.9541114142866567, linear_terms=array([-0.00259899]), square_terms=array([[2.55399204e-05]]), scale=1e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=42, candidate_x=array([6.29657711]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-31.213254935770046, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.9660020942409236, linear_terms=array([7.58335514e-05]), square_terms=array([[4.53705304e-06]]), scale=1e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1556,10 +1556,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=41, candidate_x=array([6.374032]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-11.280839901153268, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40, 41]), model=ScalarModel(intercept=0.9543560836259272, linear_terms=array([-0.00255752]), square_terms=array([[1.14602432e-05]]), scale=1e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=43, candidate_x=array([6.29657511]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=350.31120664478067, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=1.000000000139778e-06, relative_step_length=1.000000000139778, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=2e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.9583242400545079, linear_terms=array([-0.0022253]), square_terms=array([[0.00017439]]), scale=2e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1607,10 +1607,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=42, candidate_x=array([6.374032]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-29.958222990581294, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.9638706904692468, linear_terms=array([3.99425596e-05]), square_terms=array([[4.30374416e-06]]), scale=1e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=44, candidate_x=array([6.29657711]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-48.25392147842716, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 40, 41, 42, 43]), old_indices_discarded=array([38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 41, 42, 43, 44]), model=ScalarModel(intercept=0.961135716464267, linear_terms=array([0.00323214]), square_terms=array([[9.70829429e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1658,10 +1658,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=43, candidate_x=array([6.37403]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=743.6408267689862, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=1.000000000139778e-06, relative_step_length=1.000000000139778, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=2e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.9560499712746928, linear_terms=array([-0.00271948]), square_terms=array([[0.00017506]]), scale=2e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=45, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-26.82644996215815, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 41, 42, 43, 44]), old_indices_discarded=array([38, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 42, 43, 44, 45]), model=ScalarModel(intercept=0.9686338714668369, linear_terms=array([0.00131045]), square_terms=array([[3.71483458e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1709,10 +1709,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=44, candidate_x=array([6.374032]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-41.539522738037384, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 40, 41, 42, 43]), old_indices_discarded=array([38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 41, 42, 43, 44]), model=ScalarModel(intercept=0.9590165888199659, linear_terms=array([0.00335469]), square_terms=array([[9.8606967e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=46, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-47.91874458305761, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 42, 43, 44, 45]), old_indices_discarded=array([38, 40, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 43, 44, 45, 46]), model=ScalarModel(intercept=0.9628062685513716, linear_terms=array([-0.0045808]), square_terms=array([[7.05678932e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1760,10 +1760,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=45, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-27.24730914937388, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 41, 42, 43, 44]), old_indices_discarded=array([38, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 42, 43, 44, 45]), model=ScalarModel(intercept=0.9669575471102445, linear_terms=array([0.00149799]), square_terms=array([[3.85885665e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=47, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-11.164143107883334, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 43, 44, 45, 46]), old_indices_discarded=array([38, 40, 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 43, 45, 46, 47]), model=ScalarModel(intercept=0.9487961653597279, linear_terms=array([-0.02455412]), square_terms=array([[0.00125896]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1811,10 +1811,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=46, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-43.944161059460846, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 42, 43, 44, 45]), old_indices_discarded=array([38, 40, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 43, 44, 45, 46]), model=ScalarModel(intercept=0.9608303032554155, linear_terms=array([-0.00456942]), square_terms=array([[6.69843926e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=48, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-6.192931178391938, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 43, 45, 46, 47]), old_indices_discarded=array([38, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 43, 45, 46, 47, 48]), model=ScalarModel(intercept=0.9607377769592089, linear_terms=array([0.00226746]), square_terms=array([[5.41369674e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1862,10 +1862,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=47, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-11.82275374731701, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 43, 44, 45, 46]), old_indices_discarded=array([38, 40, 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 43, 45, 46, 47]), model=ScalarModel(intercept=0.9460450760451854, linear_terms=array([-0.02582148]), square_terms=array([[0.00124205]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=49, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-40.89704746217631, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 43, 45, 46, 47, 48]), old_indices_discarded=array([38, 39, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.9732525470526511, linear_terms=array([0.00572826]), square_terms=array([[0.00014173]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1913,10 +1913,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=48, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-6.248754779708172, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 43, 45, 46, 47]), old_indices_discarded=array([38, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 43, 45, 46, 47, 48]), model=ScalarModel(intercept=0.9585866091108527, linear_terms=array([0.00290552]), square_terms=array([[5.37773686e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=50, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-13.309724227891063, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 45, 46, 47, 48, 49]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=0.971902338533044, linear_terms=array([0.00737746]), square_terms=array([[0.00023813]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -1964,10 +1964,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=49, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-33.60349066864824, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 43, 45, 46, 47, 48]), old_indices_discarded=array([38, 39, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.9717892076795451, linear_terms=array([0.00645849]), square_terms=array([[0.00013996]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=51, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.3614647788328331, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 46, 47, 48, 49, 50]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=0.9643784307177906, linear_terms=array([0.01663158]), square_terms=array([[0.00061505]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2015,10 +2015,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=50, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-12.411571174571728, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 45, 46, 47, 48, 49]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=0.9703467337906204, linear_terms=array([0.00822181]), square_terms=array([[0.00023938]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=52, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-6.51122600421527, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 47, 48, 49, 50, 51]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=0.9812904134605892, linear_terms=array([0.02163235]), square_terms=array([[0.00107227]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2066,10 +2066,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=51, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.2160611010902618, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 46, 47, 48, 49, 50]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=0.9623901807830201, linear_terms=array([0.01801568]), square_terms=array([[0.00061893]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=53, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-5.0170919543990715, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 48, 49, 50, 51, 52]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.901199737063377, linear_terms=array([-0.07072977]), square_terms=array([[0.01171341]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2117,10 +2117,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=52, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-6.377746091990022, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 47, 48, 49, 50, 51]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=0.9805784248288837, linear_terms=array([0.02338952]), square_terms=array([[0.00107577]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=54, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.839558870179515, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 49, 50, 51, 52, 53]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.9726320968065401, linear_terms=array([0.00811743]), square_terms=array([[0.00012925]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2168,10 +2168,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=53, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.920574950162586, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 48, 49, 50, 51, 52]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.8954365771565683, linear_terms=array([-0.07521733]), square_terms=array([[0.01164434]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=55, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-7.742122314819738, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 50, 51, 52, 53, 54]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.9713629899413632, linear_terms=array([0.00999865]), square_terms=array([[0.00019659]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2219,10 +2219,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=54, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.8144846182260221, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 49, 50, 51, 52, 53]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.9710071801247617, linear_terms=array([0.00848024]), square_terms=array([[0.00013339]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=56, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-7.46238606112956, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 51, 52, 53, 54, 55]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.9777541386117766, linear_terms=array([0.00043123]), square_terms=array([[0.00013867]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2270,10 +2270,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=55, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-7.848353432941635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 50, 51, 52, 53, 54]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.9697072109383787, linear_terms=array([0.01040672]), square_terms=array([[0.0002038]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=57, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-212.7794913863689, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 52, 53, 54, 55, 56]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.9750271552437775, linear_terms=array([0.00449856]), square_terms=array([[0.00020812]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2321,10 +2321,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=56, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-7.552446687655948, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 51, 52, 53, 54, 55]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.9764798288002208, linear_terms=array([0.00026806]), square_terms=array([[0.000143]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=58, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-11.831325307573865, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 53, 54, 55, 56, 57]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.969987196069964, linear_terms=array([0.01202742]), square_terms=array([[0.00030133]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2372,10 +2372,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=57, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-416.08394865369763, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 52, 53, 54, 55, 56]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.9735745671797933, linear_terms=array([0.00460202]), square_terms=array([[0.0002148]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=59, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-14.356451753216454, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 54, 55, 56, 57, 58]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=0.9011997370633767, linear_terms=array([-0.0787215]), square_terms=array([[0.01385309]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2423,10 +2423,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=58, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-12.401911544499761, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 53, 54, 55, 56, 57]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.968265847993553, linear_terms=array([0.01253358]), square_terms=array([[0.00030935]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=60, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-0.7723312291690756, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 55, 56, 57, 58, 59]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 56, 57, 58, 59, 60]), model=ScalarModel(intercept=0.9560700282691561, linear_terms=array([-0.02855868]), square_terms=array([[0.0011524]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2474,10 +2474,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=59, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-14.52868124965449, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 54, 55, 56, 57, 58]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=0.895436577156569, linear_terms=array([-0.08387051]), square_terms=array([[0.0137684]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=61, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3.4946240913004023, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 56, 57, 58, 59, 60]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.9721177451239259, linear_terms=array([-0.0137734]), square_terms=array([[0.00028476]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2525,10 +2526,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=60, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-0.7765465531995202, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 55, 56, 57, 58, 59]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 56, 57, 58, 59, 60]), model=ScalarModel(intercept=0.9540449086499131, linear_terms=array([-0.02983055]), square_terms=array([[0.0011369]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=62, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3.679345058665512, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 57, 58, 59, 60, 61]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=0.9727247016349269, linear_terms=array([-0.01702576]), square_terms=array([[0.00043182]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2576,11 +2578,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=61, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-3.5512810508335253, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 56, 57, 58, 59, 60]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.9709984685022446, linear_terms=array([-0.01433599]), square_terms=array([[0.00028235]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=63, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3.499131124546599, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 58, 59, 60, 61, 62]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.9873536855941696, linear_terms=array([-0.03304009]), square_terms=array([[0.00164576]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2628,11 +2630,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=62, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-3.7409453568477486, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 57, 58, 59, 60, 61]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=0.9715381989668214, linear_terms=array([-0.01788953]), square_terms=array([[0.00043792]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=64, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-4.364899636918336, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 59, 60, 61, 62, 63]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=0.9011997370633775, linear_terms=array([0.07332833]), square_terms=array([[0.01257141]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2680,11 +2682,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=63, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-3.4819301877587026, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 58, 59, 60, 61, 62]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.9867026303307392, linear_terms=array([-0.03475504]), square_terms=array([[0.00164416]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=65, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-0.6614885149988173, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 60, 61, 62, 63, 64]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=0.949690571467247, linear_terms=array([0.02972132]), square_terms=array([[0.00160699]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2732,11 +2734,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=64, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.389939961604253, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 59, 60, 61, 62, 63]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=0.8954365771565688, linear_terms=array([0.07826238]), square_terms=array([[0.01248644]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=66, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.0626156826914683, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 61, 62, 63, 64, 65]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=0.9557481589759595, linear_terms=array([0.01710587]), square_terms=array([[0.00060795]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2784,11 +2786,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=65, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-0.6447577129958585, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 60, 61, 62, 63, 64]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=0.9466454837789505, linear_terms=array([0.03169689]), square_terms=array([[0.00160952]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=67, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.528896603153544, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 62, 63, 64, 65, 66]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 63, 64, 65, 66, 67]), model=ScalarModel(intercept=0.9609288195036394, linear_terms=array([0.02169846]), square_terms=array([[0.00081495]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2836,11 +2838,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=66, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.9973170508497473, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 61, 62, 63, 64, 65]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=0.9527779187772669, linear_terms=array([0.01851468]), square_terms=array([[0.00061051]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=68, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-6.104918808283114, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 63, 64, 65, 66, 67]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 64, 65, 66, 67, 68]), model=ScalarModel(intercept=0.9782254768179305, linear_terms=array([0.01946156]), square_terms=array([[0.00072685]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2888,11 +2890,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=67, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.397444810328198, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 62, 63, 64, 65, 66]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 63, 64, 65, 66, 67]), model=ScalarModel(intercept=0.958185799746539, linear_terms=array([0.02344806]), square_terms=array([[0.0008234]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=69, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-4.324222510133547, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 64, 65, 66, 67, 68]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.9011997370633769, linear_terms=array([-0.06621915]), square_terms=array([[0.00935899]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2940,11 +2942,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=68, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-5.992658251958798, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 63, 64, 65, 66, 67]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 64, 65, 66, 67, 68]), model=ScalarModel(intercept=0.9769156610452606, linear_terms=array([0.02113908]), square_terms=array([[0.0007336]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=70, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.669826779006717, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 65, 66, 67, 68, 69]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=0.9901984075284357, linear_terms=array([0.02341344]), square_terms=array([[0.00094961]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -2992,11 +2994,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=69, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.225335718625612, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 64, 65, 66, 67, 68]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.8954365771565692, linear_terms=array([-0.06996539]), square_terms=array([[0.00926287]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=71, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.904244575475609, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 66, 67, 68, 69, 70]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.990881047332608, linear_terms=array([0.02244335]), square_terms=array([[0.00078803]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3044,11 +3046,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=70, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.6578450467722483, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 65, 66, 67, 68, 69]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=0.989570000301797, linear_terms=array([0.0250848]), square_terms=array([[0.00095958]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=72, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-4.839887853269327, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 67, 68, 69, 70, 71]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.9971227899021522, linear_terms=array([0.01324147]), square_terms=array([[0.00030581]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3096,11 +3098,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=71, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.8876805760374538, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 66, 67, 68, 69, 70]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.990481321514407, linear_terms=array([0.02377403]), square_terms=array([[0.00079094]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=73, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-7.609739326531269, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 68, 69, 70, 71, 72]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.9942997108894068, linear_terms=array([0.01743208]), square_terms=array([[0.00043783]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3148,11 +3150,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=72, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.884990039640256, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 67, 68, 69, 70, 71]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.9973293427411523, linear_terms=array([0.01366283]), square_terms=array([[0.00030845]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=74, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-7.536909888327736, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 69, 70, 71, 72, 73]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=0.998746622653695, linear_terms=array([0.01083351]), square_terms=array([[0.00022245]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3200,11 +3202,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=73, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-7.746059595785719, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 68, 69, 70, 71, 72]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.9942193540998723, linear_terms=array([0.01828398]), square_terms=array([[0.00043995]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=75, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-8.892094724966096, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 70, 71, 72, 73, 74]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 71, 72, 73, 74, 75]), model=ScalarModel(intercept=0.901199737063377, linear_terms=array([-0.08981365]), square_terms=array([[0.01854973]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3252,11 +3254,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=74, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-7.617021469443004, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 69, 70, 71, 72, 73]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=0.9989224875607232, linear_terms=array([0.01130032]), square_terms=array([[0.00022684]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=76, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-0.7550009058115424, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 71, 72, 73, 74, 75]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.9635716678759568, linear_terms=array([-0.03533596]), square_terms=array([[0.00191405]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3304,11 +3306,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=75, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-9.154189998384652, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 70, 71, 72, 73, 74]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 71, 72, 73, 74, 75]), model=ScalarModel(intercept=0.8954365771565689, linear_terms=array([-0.09622057]), square_terms=array([[0.01850389]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=77, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.2227604926832742, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 72, 73, 74, 75, 76]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 73, 74, 75, 76, 77]), model=ScalarModel(intercept=0.9728307236425283, linear_terms=array([-0.02260775]), square_terms=array([[0.00077284]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3356,11 +3358,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=76, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-0.7405218856066593, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 71, 72, 73, 74, 75]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.9617952098147389, linear_terms=array([-0.03770685]), square_terms=array([[0.00195309]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=78, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-4.912281157098232, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 73, 74, 75, 76, 77]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
+       72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 74, 75, 76, 77, 78]), model=ScalarModel(intercept=0.980066068010876, linear_terms=array([-0.0114677]), square_terms=array([[0.00019952]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3408,11 +3411,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=77, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.189143519430688, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 72, 73, 74, 75, 76]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 73, 74, 75, 76, 77]), model=ScalarModel(intercept=0.9713937260321144, linear_terms=array([-0.0242172]), square_terms=array([[0.00079924]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=79, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-15.054335927132607, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 74, 75, 76, 77, 78]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
+       72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 75, 76, 77, 78, 79]), model=ScalarModel(intercept=0.9750883415685396, linear_terms=array([0.01857201]), square_terms=array([[0.0005707]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3460,12 +3464,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=78, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.805519080309879, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 73, 74, 75, 76, 77]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=80, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.8312942359312387, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 75, 76, 77, 78, 79]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 74, 75, 76, 77, 78]), model=ScalarModel(intercept=0.9791696313797453, linear_terms=array([-0.01284968]), square_terms=array([[0.00021469]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=0.9515258564941137, linear_terms=array([0.04410118]), square_terms=array([[0.00277261]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3513,12 +3517,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=79, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-14.126875865381736, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 74, 75, 76, 77, 78]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=81, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.8350552989804425, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 76, 77, 78, 79, 80]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 75, 76, 77, 78, 79]), model=ScalarModel(intercept=0.9740905740597539, linear_terms=array([0.01866858]), square_terms=array([[0.00053605]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=0.9712537829841698, linear_terms=array([0.03370378]), square_terms=array([[0.00154149]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3566,12 +3570,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=80, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.9854886747368625, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 75, 76, 77, 78, 79]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=82, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.703346667158081, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 77, 78, 79, 80, 81]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=0.9490112162030394, linear_terms=array([0.04590241]), square_terms=array([[0.00274205]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=0.9796725202880812, linear_terms=array([0.03743284]), square_terms=array([[0.00180059]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3619,12 +3623,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=81, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.8690707389694687, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 76, 77, 78, 79, 80]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=83, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-4.039089672729042, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 78, 79, 80, 81, 82]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=0.9696421156310081, linear_terms=array([0.03493073]), square_terms=array([[0.00153359]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 79, 80, 81, 82, 83]), model=ScalarModel(intercept=0.9925750608950611, linear_terms=array([0.02644582]), square_terms=array([[0.00081564]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3672,12 +3676,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=82, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.7146383619861583, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 77, 78, 79, 80, 81]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=84, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3.427105690800181, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 79, 80, 81, 82, 83]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=0.9782831926338201, linear_terms=array([0.03907187]), square_terms=array([[0.00180805]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 80, 81, 82, 83, 84]), model=ScalarModel(intercept=0.9011997370633774, linear_terms=array([-0.07302685]), square_terms=array([[0.01332498]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3725,12 +3729,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=83, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.089373842262245, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 78, 79, 80, 81, 82]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=85, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.776389896554375, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 80, 81, 82, 83, 84]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 79, 80, 81, 82, 83]), model=ScalarModel(intercept=0.9920082978715767, linear_terms=array([0.02756544]), square_terms=array([[0.00082584]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 81, 82, 83, 84, 85]), model=ScalarModel(intercept=0.9791911086080595, linear_terms=array([-0.00310459]), square_terms=array([[6.35738892e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3778,12 +3782,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=84, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-3.4973337435800445, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 79, 80, 81, 82, 83]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=86, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-30.020972618776277, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 81, 82, 83, 84, 85]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 80, 81, 82, 83, 84]), model=ScalarModel(intercept=0.8954365771565689, linear_terms=array([-0.07802743]), square_terms=array([[0.01316912]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 82, 83, 84, 85, 86]), model=ScalarModel(intercept=0.9831994724381408, linear_terms=array([0.00050188]), square_terms=array([[3.66012196e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3831,12 +3835,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=85, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.7467676046276963, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 80, 81, 82, 83, 84]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=87, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-141.29571188964906, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 82, 83, 84, 85, 86]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 81, 82, 83, 84, 85]), model=ScalarModel(intercept=0.9781312327290098, linear_terms=array([-0.0032118]), square_terms=array([[6.52377716e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 83, 84, 85, 86, 87]), model=ScalarModel(intercept=0.9849341114835274, linear_terms=array([-0.0016803]), square_terms=array([[7.13011777e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3884,12 +3888,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=86, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-30.610568294907033, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 81, 82, 83, 84, 85]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=88, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-81.33131468673191, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 83, 84, 85, 86, 87]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 82, 83, 84, 85, 86]), model=ScalarModel(intercept=0.9822317232877025, linear_terms=array([0.00051227]), square_terms=array([[3.94210705e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 84, 85, 86, 87, 88]), model=ScalarModel(intercept=0.9796315108609815, linear_terms=array([0.02090956]), square_terms=array([[0.00073164]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3937,12 +3941,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=87, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-146.25942178167136, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 82, 83, 84, 85, 86]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=89, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-0.32023964902722024, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 84, 85, 86, 87, 88]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 83, 84, 85, 86, 87]), model=ScalarModel(intercept=0.9841401608974134, linear_terms=array([-0.00188636]), square_terms=array([[7.21272905e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 85, 86, 87, 88, 89]), model=ScalarModel(intercept=0.9619729301842395, linear_terms=array([0.04070689]), square_terms=array([[0.00260845]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -3990,12 +3994,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=88, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-75.4262961727173, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 83, 84, 85, 86, 87]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=90, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.864562124188757, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 85, 86, 87, 88, 89]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 84, 85, 86, 87, 88]), model=ScalarModel(intercept=0.978289722334968, linear_terms=array([0.02170503]), square_terms=array([[0.0007227]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 86, 87, 88, 89, 90]), model=ScalarModel(intercept=0.9715462463919681, linear_terms=array([0.02229722]), square_terms=array([[0.00081231]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4043,12 +4047,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=89, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-0.25988738304116815, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 84, 85, 86, 87, 88]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=91, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.884764529180674, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 86, 87, 88, 89, 90]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 85, 86, 87, 88, 89]), model=ScalarModel(intercept=0.9592223363466464, linear_terms=array([0.04314273]), square_terms=array([[0.00260706]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 87, 88, 89, 90, 91]), model=ScalarModel(intercept=0.971116104827485, linear_terms=array([0.02375527]), square_terms=array([[0.00108583]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4096,12 +4100,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=90, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.8687535049131903, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 85, 86, 87, 88, 89]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=92, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3.0094787688452946, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 87, 88, 89, 90, 91]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 86, 87, 88, 89, 90]), model=ScalarModel(intercept=0.9693975226726794, linear_terms=array([0.02323999]), square_terms=array([[0.00081525]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 88, 89, 90, 91, 92]), model=ScalarModel(intercept=0.9712646251072484, linear_terms=array([0.02354412]), square_terms=array([[0.00105094]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4149,12 +4153,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=91, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.8961334720621483, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 86, 87, 88, 89, 90]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=93, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.922280345887979, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 88, 89, 90, 91, 92]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 87, 88, 89, 90, 91]), model=ScalarModel(intercept=0.9687171619126889, linear_terms=array([0.02456566]), square_terms=array([[0.00106407]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 89, 90, 91, 92, 93]), model=ScalarModel(intercept=0.9011997370633772, linear_terms=array([-0.05452557]), square_terms=array([[0.00743282]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4202,12 +4206,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=92, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-3.05920987872928, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 87, 88, 89, 90, 91]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=94, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.0109666785915747, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 89, 90, 91, 92, 93]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 88, 89, 90, 91, 92]), model=ScalarModel(intercept=0.9688604480212463, linear_terms=array([0.02436072]), square_terms=array([[0.00103408]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 90, 91, 92, 93, 94]), model=ScalarModel(intercept=0.9661294479541731, linear_terms=array([0.00167448]), square_terms=array([[4.55984038e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4255,12 +4259,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=93, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.9957360414277567, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 88, 89, 90, 91, 92]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=95, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-39.91584217353992, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 90, 91, 92, 93, 94]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 89, 90, 91, 92, 93]), model=ScalarModel(intercept=0.8954365771565687, linear_terms=array([-0.05774553]), square_terms=array([[0.00737231]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
+       89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 91, 92, 93, 94, 95]), model=ScalarModel(intercept=0.9618919514497689, linear_terms=array([0.00799851]), square_terms=array([[0.00014224]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4308,12 +4313,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=94, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.011783420567907, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 89, 90, 91, 92, 93]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=96, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-13.220750226677673, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 91, 92, 93, 94, 95]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 90, 91, 92, 93, 94]), model=ScalarModel(intercept=0.9644856666238031, linear_terms=array([0.00204837]), square_terms=array([[4.92290844e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
+       89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 92, 93, 94, 95, 96]), model=ScalarModel(intercept=0.9678354499179974, linear_terms=array([-0.0008971]), square_terms=array([[8.33138157e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4361,13 +4367,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=95, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-34.72262523507854, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 90, 91, 92, 93, 94]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=97, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-100.67371075348187, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 92, 93, 94, 95, 96]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 91, 92, 93, 94, 95]), model=ScalarModel(intercept=0.9599770032633489, linear_terms=array([0.00877807]), square_terms=array([[0.00014913]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 93, 94, 95, 96, 97]), model=ScalarModel(intercept=0.9716993823848853, linear_terms=array([0.00429668]), square_terms=array([[8.40196909e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4415,13 +4421,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=96, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-12.738581437762173, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 91, 92, 93, 94, 95]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=98, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-20.67117084790136, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 93, 94, 95, 96, 97]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 92, 93, 94, 95, 96]), model=ScalarModel(intercept=0.9663071204629095, linear_terms=array([-0.0006946]), square_terms=array([[8.16078971e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 94, 95, 96, 97, 98]), model=ScalarModel(intercept=0.9745628742669177, linear_terms=array([0.0007128]), square_terms=array([[9.53079999e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4469,13 +4475,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=97, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-137.65479109813177, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 92, 93, 94, 95, 96]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=99, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-106.28536094043946, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 94, 95, 96, 97, 98]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 93, 94, 95, 96, 97]), model=ScalarModel(intercept=0.9701094093974236, linear_terms=array([0.00430136]), square_terms=array([[7.91145485e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 95, 96, 97, 98, 99]), model=ScalarModel(intercept=0.9640395921164611, linear_terms=array([-0.0102239]), square_terms=array([[0.00073706]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4523,13 +4529,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=98, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-21.84646656095411, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 93, 94, 95, 96, 97]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=100, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-5.047798565349715, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 95, 96, 97, 98, 99]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 94, 95, 96, 97, 98]), model=ScalarModel(intercept=0.9731113186701557, linear_terms=array([0.00054501]), square_terms=array([[8.90271481e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  96,  97,  98,  99, 100]), model=ScalarModel(intercept=0.9644818516534253, linear_terms=array([-0.01243063]), square_terms=array([[0.00034918]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4577,13 +4583,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=99, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-149.52753243242998, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 94, 95, 96, 97, 98]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=101, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-12.761396554927043, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  96,  97,  98,  99, 100]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 95, 96, 97, 98, 99]), model=ScalarModel(intercept=0.9616405371544691, linear_terms=array([-0.01138011]), square_terms=array([[0.00071608]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  97,  98,  99, 100, 101]), model=ScalarModel(intercept=0.9725559518808135, linear_terms=array([0.0114335]), square_terms=array([[0.00027013]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4631,13 +4637,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=100, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.816749687222443, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 95, 96, 97, 98, 99]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=102, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-12.352014050324604, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  97,  98,  99, 100, 101]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  96,  97,  98,  99, 100]), model=ScalarModel(intercept=0.9623379458251654, linear_terms=array([-0.01333699]), square_terms=array([[0.00034843]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  98,  99, 100, 101, 102]), model=ScalarModel(intercept=0.9826031735985405, linear_terms=array([-0.00242597]), square_terms=array([[8.93270334e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4685,13 +4691,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=101, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-12.48056566835165, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  96,  97,  98,  99, 100]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=103, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-42.196247268613014, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  98,  99, 100, 101, 102]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  97,  98,  99, 100, 101]), model=ScalarModel(intercept=0.9708185701345904, linear_terms=array([0.0118014]), square_terms=array([[0.00026222]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  99, 100, 101, 102, 103]), model=ScalarModel(intercept=0.9846019792639674, linear_terms=array([0.00202494]), square_terms=array([[0.00031667]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4739,13 +4745,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=102, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-12.579493918723726, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  97,  98,  99, 100, 101]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=104, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-23.16983183076578, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  99, 100, 101, 102, 103]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  98,  99, 100, 101, 102]), model=ScalarModel(intercept=0.9814684482841655, linear_terms=array([-0.00256306]), square_terms=array([[8.80024795e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.9788412677462819, linear_terms=array([0.0088008]), square_terms=array([[0.00018827]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4793,13 +4799,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=103, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-41.857119241112876, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  98,  99, 100, 101, 102]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=105, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-8.536243236912528, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 100, 101, 102, 103, 104]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  99, 100, 101, 102, 103]), model=ScalarModel(intercept=0.9834518786866443, linear_terms=array([0.00206491]), square_terms=array([[0.00031529]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 101, 102, 103, 104, 105]), model=ScalarModel(intercept=0.9872819287534297, linear_terms=array([0.01708121]), square_terms=array([[0.00060456]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4847,13 +4853,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=104, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-23.98387228056005, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  99, 100, 101, 102, 103]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.9773530448918726, linear_terms=array([0.00923626]), square_terms=array([[0.00018451]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=106, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-5.559166101696771, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 101, 102, 103, 104, 105]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+        51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
+        64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
+        77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
+        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 102, 103, 104, 105, 106]), model=ScalarModel(intercept=0.9709037484204576, linear_terms=array([-0.00723915]), square_terms=array([[0.00064196]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4901,13 +4908,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=105, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-8.514800532082953, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 100, 101, 102, 103, 104]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 101, 102, 103, 104, 105]), model=ScalarModel(intercept=0.986034175254755, linear_terms=array([0.01790239]), square_terms=array([[0.00060868]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=107, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-16.02633467569126, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 102, 103, 104, 105, 106]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+        51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
+        64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
+        77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
+        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 103, 104, 105, 106, 107]), model=ScalarModel(intercept=0.9725604010289675, linear_terms=array([0.01428233]), square_terms=array([[0.00053636]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -4955,14 +4963,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=106, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-5.631861106735954, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 101, 102, 103, 104, 105]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=108, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-8.646982717239249, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 103, 104, 105, 106, 107]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
-        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 102, 103, 104, 105, 106]), model=ScalarModel(intercept=0.9688653297176435, linear_terms=array([-0.00784153]), square_terms=array([[0.00063712]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 104, 105, 106, 107, 108]), model=ScalarModel(intercept=0.9732184383362349, linear_terms=array([-0.0004789]), square_terms=array([[0.00021674]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5010,14 +5018,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=107, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-15.473220613417189, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 102, 103, 104, 105, 106]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=109, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-350.86495619867986, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 104, 105, 106, 107, 108]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
-        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 103, 104, 105, 106, 107]), model=ScalarModel(intercept=0.9706104470118138, linear_terms=array([0.01477564]), square_terms=array([[0.00053869]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
+       103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 105, 106, 107, 108, 109]), model=ScalarModel(intercept=0.98909291316177, linear_terms=array([0.00797517]), square_terms=array([[0.0002085]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5065,14 +5074,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=108, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-8.782745961255761, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 103, 104, 105, 106, 107]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=110, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-15.479232898142147, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 105, 106, 107, 108, 109]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
-        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 104, 105, 106, 107, 108]), model=ScalarModel(intercept=0.9712937793053485, linear_terms=array([-0.00068238]), square_terms=array([[0.00022246]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
+       103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 106, 107, 108, 109, 110]), model=ScalarModel(intercept=0.9955577361289816, linear_terms=array([-7.81162555e-05]), square_terms=array([[0.00013915]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5120,15 +5130,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=109, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-239.3582646941626, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 104, 105, 106, 107, 108]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=111, candidate_x=array([6.29657568]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3848.3978362689486, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 106, 107, 108, 109, 110]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
         90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
-       103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 105, 106, 107, 108, 109]), model=ScalarModel(intercept=0.9880226177975504, linear_terms=array([0.00825098]), square_terms=array([[0.00020887]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 107, 108, 109, 110, 111]), model=ScalarModel(intercept=0.9942288146388729, linear_terms=array([-0.00156615]), square_terms=array([[0.0001054]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5176,15 +5186,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=110, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-15.831643019937017, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 105, 106, 107, 108, 109]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=112, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-95.41787838927698, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 107, 108, 109, 110, 111]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
         90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
-       103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 106, 107, 108, 109, 110]), model=ScalarModel(intercept=0.9949893019142968, linear_terms=array([-0.00043047]), square_terms=array([[0.0001416]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 108, 109, 110, 111, 112]), model=ScalarModel(intercept=0.9989671358910293, linear_terms=array([0.00529815]), square_terms=array([[0.00010313]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5232,15 +5242,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=111, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-248.81126014190247, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 106, 107, 108, 109, 110]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=113, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-19.76374452996817, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 108, 109, 110, 111, 112]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
         90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
-       103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 107, 108, 109, 110, 111]), model=ScalarModel(intercept=0.9938523435854993, linear_terms=array([-0.00242294]), square_terms=array([[0.00010028]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 109, 110, 111, 112, 113]), model=ScalarModel(intercept=0.9957100526760587, linear_terms=array([0.00963924]), square_terms=array([[0.00021434]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5288,15 +5298,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=112, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-63.9512580925676, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 107, 108, 109, 110, 111]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=114, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-8.82761262988312, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 109, 110, 111, 112, 113]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
         90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
-       103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 113 entries., 'multistart_info': {'start_parameters': [array([6.32060598])], 'local_optima': [{'solution_x': array([6.37403]), 'solution_criterion': 0.895436577156568, 'states': [State(trustregion=Region(center=array([6.32060598]), radius=0.6320605981087387, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=1.0251060829318852, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.6320605981087387, shift=array([6.32060598])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       103, 104, 105, 106, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 115 entries., 'multistart_info': {'start_parameters': [array([6.37403])], 'local_optima': [{'solution_x': array([6.29657511]), 'solution_criterion': 0.9011997370633763, 'states': [State(trustregion=Region(center=array([6.37403]), radius=0.6374030002146401, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=[0], model=ScalarModel(intercept=1.0261223490300269, linear_terms=array([0.]), square_terms=array([[0.]]), scale=0.6374030002146401, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5344,10 +5354,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=0, candidate_x=array([6.32060598]), index=0, x=array([6.32060598]), fval=1.0251060829318852, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([6.32060598]), radius=0.6320605981087387, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=1.0403877497190288, linear_terms=array([-0.0192514]), square_terms=array([[0.23512879]]), scale=0.6320605981087387, shift=array([6.32060598])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=0, candidate_x=array([6.37403]), index=0, x=array([6.37403]), fval=1.0261223490300269, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([6.37403]), radius=0.6374030002146401, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), model=ScalarModel(intercept=1.03922718390553, linear_terms=array([0.00461476]), square_terms=array([[0.23253441]]), scale=0.6374030002146401, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5395,10 +5405,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=13, candidate_x=array([6.37235657]), index=13, x=array([6.37235657]), fval=1.0160204843044787, rho=11.52828347349067, accepted=True, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.05175058504087815, relative_step_length=0.08187598656794465, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37235657]), radius=0.6320605981087387, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0,  2,  8, 10, 12, 13]), model=ScalarModel(intercept=1.0215118655511004, linear_terms=array([0.03599326]), square_terms=array([[0.19981701]]), scale=0.6320605981087387, shift=array([6.37235657])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=14, candidate_x=array([6.36138043]), index=0, x=array([6.37403]), fval=1.0261223490300269, rho=-201.70830611242548, accepted=False, new_indices=array([ 1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=0.31870150010732007, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=1.0366868269112581, linear_terms=array([0.00453863]), square_terms=array([[0.0579932]]), scale=0.31870150010732007, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5446,10 +5456,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=14, candidate_x=array([6.25850278]), index=13, x=array([6.37235657]), fval=1.0160204843044787, rho=-6.160183981647245, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  2,  8, 10, 12, 13]), old_indices_discarded=array([ 1,  3,  4,  5,  6,  7,  9, 11]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37235657]), radius=0.31603029905436936, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0,  8, 10, 12, 13, 14]), model=ScalarModel(intercept=1.0267893707363318, linear_terms=array([0.01395961]), square_terms=array([[0.05101742]]), scale=0.31603029905436936, shift=array([6.37235657])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=15, candidate_x=array([6.349088]), index=15, x=array([6.349088]), fval=1.0086641123528395, rho=98.30118363118868, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 10, 11, 12, 13, 14]), old_indices_discarded=array([1, 2, 3, 4, 5, 6, 7, 8, 9]), step_length=0.024942005173986814, relative_step_length=0.07826133596982694, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.349088]), radius=0.31870150010732007, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 11, 13, 14, 15, 16]), model=ScalarModel(intercept=1.0137055022251003, linear_terms=array([-0.00155151]), square_terms=array([[0.06825286]]), scale=0.31870150010732007, shift=array([6.349088])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5497,10 +5507,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=15, candidate_x=array([6.28588298]), index=15, x=array([6.28588298]), fval=1.009209012984012, rho=3.5665060661249157, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  8, 10, 12, 13, 14]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  9, 11]), step_length=0.08647358246908521, relative_step_length=0.2736243414882458, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.28588298]), radius=0.31603029905436936, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0,  9, 11, 13, 14, 15]), model=ScalarModel(intercept=1.0215447247561733, linear_terms=array([-0.01977416]), square_terms=array([[0.06772284]]), scale=0.31603029905436936, shift=array([6.28588298])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=17, candidate_x=array([6.35633265]), index=17, x=array([6.35633265]), fval=0.9483696035077183, rho=3419.168086364822, accepted=True, new_indices=array([16]), old_indices_used=array([ 0, 11, 13, 14, 15]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 12]), step_length=0.0072446485151180795, relative_step_length=0.022731767853864836, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35633265]), radius=0.31870150010732007, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=0.9982668600888471, linear_terms=array([-0.00587143]), square_terms=array([[0.06710545]]), scale=0.31870150010732007, shift=array([6.35633265])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5548,10 +5558,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=16, candidate_x=array([6.37815961]), index=16, x=array([6.37815961]), fval=0.9536680614253905, rho=19.238986279195796, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  9, 11, 13, 14, 15]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8, 10, 12]), step_length=0.092276624481757, relative_step_length=0.29198663785677675, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37815961]), radius=0.31603029905436936, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 12, 13, 14, 15, 16]), model=ScalarModel(intercept=1.010452400479926, linear_terms=array([0.0172892]), square_terms=array([[0.05310538]]), scale=0.31603029905436936, shift=array([6.37815961])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=18, candidate_x=array([6.38421763]), index=17, x=array([6.35633265]), fval=0.9483696035077183, rho=-273.57733729961365, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35633265]), radius=0.15935075005366003, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 16, 17, 18]), model=ScalarModel(intercept=1.0053794558515663, linear_terms=array([0.02496362]), square_terms=array([[0.02297852]]), scale=0.15935075005366003, shift=array([6.35633265])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5599,10 +5609,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=17, candidate_x=array([6.27527153]), index=17, x=array([6.27527153]), fval=0.9524968151553904, rho=0.41616642491251093, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 12, 13, 14, 15, 16]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]), step_length=0.10288807444893955, relative_step_length=0.325563956230788, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27527153]), radius=0.31603029905436936, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=1.0045189771362606, linear_terms=array([-0.06412789]), square_terms=array([[0.06173571]]), scale=0.31603029905436936, shift=array([6.27527153])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=19, candidate_x=array([6.1969819]), index=17, x=array([6.35633265]), fval=0.9483696035077183, rho=-4.590777056347129, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 16, 17, 18]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35633265]), radius=0.07967537502683002, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 17, 18, 19]), model=ScalarModel(intercept=1.006776384006398, linear_terms=array([0.00427617]), square_terms=array([[0.0040298]]), scale=0.07967537502683002, shift=array([6.35633265])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5650,10 +5660,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=18, candidate_x=array([6.59130183]), index=17, x=array([6.27527153]), fval=0.9524968151553904, rho=-2.6914652702645263, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27527153]), radius=0.15801514952718468, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=1.0045189771362604, linear_terms=array([-0.03206395]), square_terms=array([[0.01543393]]), scale=0.15801514952718468, shift=array([6.27527153])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=20, candidate_x=array([6.27665727]), index=20, x=array([6.27665727]), fval=0.9397985927899563, rho=3.790354442652867, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 17, 18, 19]), old_indices_discarded=array([16]), step_length=0.07967537502682998, relative_step_length=0.9999999999999994, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27665727]), radius=0.15935075005366003, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 18, 19, 20]), model=ScalarModel(intercept=0.9937779072188607, linear_terms=array([0.02977405]), square_terms=array([[0.02375181]]), scale=0.15935075005366003, shift=array([6.27665727])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5701,10 +5711,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=19, candidate_x=array([6.43328668]), index=17, x=array([6.27527153]), fval=0.9524968151553904, rho=-2.276794831504726, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 14, 15, 16, 17]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27527153]), radius=0.07900757476359234, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 14, 15, 16, 17]), model=ScalarModel(intercept=1.0045189771362606, linear_terms=array([-0.01603197]), square_terms=array([[0.00385848]]), scale=0.07900757476359234, shift=array([6.27527153])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=21, candidate_x=array([6.11730652]), index=20, x=array([6.27665727]), fval=0.9397985927899563, rho=-3.7748946860083574, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 18, 19, 20]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 16, 17]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27665727]), radius=0.07967537502683002, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 14, 15, 17, 19, 20]), model=ScalarModel(intercept=0.9876587888585374, linear_terms=array([0.00695975]), square_terms=array([[0.00480284]]), scale=0.07967537502683002, shift=array([6.27665727])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5752,10 +5762,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=20, candidate_x=array([6.35427911]), index=20, x=array([6.35427911]), fval=0.9340945430424806, rho=1.3048727658870296, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 14, 15, 16, 17]), old_indices_discarded=array([18, 19]), step_length=0.0790075747635921, relative_step_length=0.999999999999997, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35427911]), radius=0.15801514952718468, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 14, 16, 17, 19, 20]), model=ScalarModel(intercept=0.9802636388806935, linear_terms=array([-0.01901788]), square_terms=array([[0.01410849]]), scale=0.15801514952718468, shift=array([6.35427911])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=22, candidate_x=array([6.1969819]), index=20, x=array([6.27665727]), fval=0.9397985927899563, rho=-32.476038926444666, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 15, 17, 19, 20]), old_indices_discarded=array([16, 18, 21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27665727]), radius=0.03983768751341501, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([14, 15, 17, 19, 20, 22]), model=ScalarModel(intercept=1.0059198573411452, linear_terms=array([-0.01138883]), square_terms=array([[0.00061497]]), scale=0.03983768751341501, shift=array([6.27665727])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5803,10 +5813,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=21, candidate_x=array([6.51229426]), index=20, x=array([6.35427911]), fval=0.9340945430424806, rho=-5.076605751170216, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 14, 16, 17, 19, 20]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 15, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35427911]), radius=0.07900757476359234, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 15, 16, 17, 20]), model=ScalarModel(intercept=0.9776664671616091, linear_terms=array([-0.00745835]), square_terms=array([[0.00368061]]), scale=0.07900757476359234, shift=array([6.35427911])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=23, candidate_x=array([6.31649496]), index=20, x=array([6.27665727]), fval=0.9397985927899563, rho=-11.324312750705486, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 17, 19, 20, 22]), old_indices_discarded=array([ 0, 18, 21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.27665727]), radius=0.019918843756707504, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([15, 17, 19, 20, 22, 23]), model=ScalarModel(intercept=1.0091309106068849, linear_terms=array([-0.00695298]), square_terms=array([[0.00015443]]), scale=0.019918843756707504, shift=array([6.27665727])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5854,10 +5864,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=22, candidate_x=array([6.43328668]), index=20, x=array([6.35427911]), fval=0.9340945430424806, rho=-28.88787876275111, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 15, 16, 17, 20]), old_indices_discarded=array([14, 18, 19, 21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35427911]), radius=0.03950378738179617, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 15, 16, 20, 22]), model=ScalarModel(intercept=1.001603598709247, linear_terms=array([0.01386041]), square_terms=array([[0.00165678]]), scale=0.03950378738179617, shift=array([6.35427911])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=24, candidate_x=array([6.29657611]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=1.8657127314789879, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([15, 17, 19, 20, 22, 23]), old_indices_discarded=array([ 0, 14]), step_length=0.019918843756707716, relative_step_length=1.0000000000000107, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.03983768751341501, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([14, 15, 17, 20, 23, 24]), model=ScalarModel(intercept=0.9633353155766339, linear_terms=array([0.02691519]), square_terms=array([[0.00406671]]), scale=0.03983768751341501, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5905,10 +5915,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=23, candidate_x=array([6.31477532]), index=20, x=array([6.35427911]), fval=0.9340945430424806, rho=-10.0623315524537, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 15, 16, 20, 22]), old_indices_discarded=array([14, 17, 19, 21]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.35427911]), radius=0.019751893690898085, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 15, 16, 20, 23]), model=ScalarModel(intercept=0.9868075154288375, linear_terms=array([-0.01433558]), square_terms=array([[0.0002463]]), scale=0.019751893690898085, shift=array([6.35427911])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=25, candidate_x=array([6.25673843]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-2.068167896660778, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 15, 17, 20, 23, 24]), old_indices_discarded=array([ 0, 18, 19, 21, 22]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.019918843756707504, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([15, 17, 20, 23, 24, 25]), model=ScalarModel(intercept=0.9719824934312715, linear_terms=array([0.00559189]), square_terms=array([[0.00043118]]), scale=0.019918843756707504, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -5956,10 +5966,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=24, candidate_x=array([6.374031]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=0.7426784515001978, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 15, 16, 20, 23]), old_indices_discarded=array([14, 17, 19, 22]), step_length=0.01975189369089847, relative_step_length=1.0000000000000195, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.03950378738179617, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 16, 20, 22, 24]), model=ScalarModel(intercept=0.9893109254111845, linear_terms=array([0.02816249]), square_terms=array([[0.00292704]]), scale=0.03950378738179617, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=26, candidate_x=array([6.27665727]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-8.307248539999298, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 17, 20, 23, 24, 25]), old_indices_discarded=array([ 0, 14, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.009959421878353752, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([20, 23, 24, 25, 26]), model=ScalarModel(intercept=0.9876667111577927, linear_terms=array([0.0114311]), square_terms=array([[0.00034082]]), scale=0.009959421878353752, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6007,10 +6017,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=25, candidate_x=array([6.33452721]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-1.9674909845378, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 16, 20, 22, 24]), old_indices_discarded=array([14, 15, 17, 19, 21, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.019751893690898085, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 0, 13, 16, 20, 24, 25]), model=ScalarModel(intercept=0.9539954380175584, linear_terms=array([-0.01694482]), square_terms=array([[0.00053969]]), scale=0.019751893690898085, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=27, candidate_x=array([6.28661669]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-5.767845500764676, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.004979710939176876, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([20, 23, 24, 26, 27]), model=ScalarModel(intercept=0.9898061757284117, linear_terms=array([0.01086956]), square_terms=array([[0.00023498]]), scale=0.004979710939176876, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6058,10 +6068,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=26, candidate_x=array([6.3937829]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-2.690157305274892, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 13, 16, 20, 24, 25]), old_indices_discarded=array([15, 19, 22, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.009875946845449042, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([13, 16, 20, 24, 25, 26]), model=ScalarModel(intercept=0.9607835489629618, linear_terms=array([-4.85050354e-05]), square_terms=array([[5.93867423e-05]]), scale=0.009875946845449042, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=28, candidate_x=array([6.2915964]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-6.828966566759263, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([20, 23, 24, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.002489855469588438, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 27, 28]), model=ScalarModel(intercept=0.9397592375412753, linear_terms=array([-0.01579103]), square_terms=array([[0.00039329]]), scale=0.002489855469588438, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6109,10 +6119,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=27, candidate_x=array([6.38390695]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-3581.2018596441158, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 16, 20, 24, 25, 26]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.004937973422724521, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([13, 16, 20, 24, 26, 27]), model=ScalarModel(intercept=0.9613170428384746, linear_terms=array([0.00468562]), square_terms=array([[8.14369693e-05]]), scale=0.004937973422724521, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=29, candidate_x=array([6.29906597]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-3.0686296821552284, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.001244927734794219, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 28, 29]), model=ScalarModel(intercept=0.9622473445073738, linear_terms=array([-0.00621048]), square_terms=array([[5.70291544e-05]]), scale=0.001244927734794219, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6160,10 +6170,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=28, candidate_x=array([6.36909303]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-16.517494651838753, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 16, 20, 24, 26, 27]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.0024689867113622606, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([13, 16, 24, 27, 28]), model=ScalarModel(intercept=0.9771354368996926, linear_terms=array([-0.00243716]), square_terms=array([[4.0235556e-05]]), scale=0.0024689867113622606, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=30, candidate_x=array([6.29782104]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-16.495099323898966, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.0006224638673971095, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 29, 30]), model=ScalarModel(intercept=0.9507850276343404, linear_terms=array([0.01176136]), square_terms=array([[0.00044094]]), scale=0.0006224638673971095, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6211,10 +6221,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=29, candidate_x=array([6.37649999]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-20.934942146147495, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 16, 24, 27, 28]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.0012344933556811303, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([13, 16, 24, 28, 29]), model=ScalarModel(intercept=0.9723306091896402, linear_terms=array([-0.00687119]), square_terms=array([[6.783946e-05]]), scale=0.0012344933556811303, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=31, candidate_x=array([6.29595365]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-6.022844977484244, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.00031123193369855475, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 30, 31]), model=ScalarModel(intercept=0.9770042041592212, linear_terms=array([0.00798633]), square_terms=array([[0.00013303]]), scale=0.00031123193369855475, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6262,10 +6272,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=30, candidate_x=array([6.3752655]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-15.6742482390045, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 16, 24, 28, 29]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.0006172466778405652, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([13, 24, 29, 30]), model=ScalarModel(intercept=0.9856930933010559, linear_terms=array([-0.00146752]), square_terms=array([[3.2048337e-05]]), scale=0.0006172466778405652, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=32, candidate_x=array([6.29626488]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-9.343082191756242, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=0.00015561596684927738, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 31, 32]), model=ScalarModel(intercept=0.9394344463177127, linear_terms=array([-0.01653866]), square_terms=array([[0.00060385]]), scale=0.00015561596684927738, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6313,10 +6323,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=31, candidate_x=array([6.37464825]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-50.988952950892276, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([13, 24, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.0003086233389202826, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 30, 31]), model=ScalarModel(intercept=0.9303978228444009, linear_terms=array([0.02463831]), square_terms=array([[0.00124497]]), scale=0.0003086233389202826, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=33, candidate_x=array([6.29673173]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-2.8439600418616675, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=7.780798342463869e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 32, 33]), model=ScalarModel(intercept=0.9617837838772493, linear_terms=array([-0.00652768]), square_terms=array([[4.92677082e-05]]), scale=7.780798342463869e-05, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6364,10 +6374,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=32, candidate_x=array([6.37372238]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-3.1656161632596316, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=0.0001543116694601413, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 31, 32]), model=ScalarModel(intercept=0.970866840040577, linear_terms=array([0.00236833]), square_terms=array([[3.09252925e-05]]), scale=0.0001543116694601413, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=34, candidate_x=array([6.29665392]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-17.117205500717674, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=3.8903991712319344e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 33, 34]), model=ScalarModel(intercept=0.9540200386931921, linear_terms=array([0.01147644]), square_terms=array([[0.00035954]]), scale=3.8903991712319344e-05, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6415,10 +6425,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=33, candidate_x=array([6.37387669]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-20.3758045253014, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=7.715583473007064e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 32, 33]), model=ScalarModel(intercept=0.9267147479348122, linear_terms=array([-0.01806129]), square_terms=array([[0.00057661]]), scale=7.715583473007064e-05, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=35, candidate_x=array([6.29653721]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-4.728176734884648, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=1.9451995856159672e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 34, 35]), model=ScalarModel(intercept=0.972188201116544, linear_terms=array([0.0117863]), square_terms=array([[0.00021062]]), scale=1.9451995856159672e-05, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6466,10 +6476,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=34, candidate_x=array([6.37410816]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-6.521729579502907, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=3.857791736503532e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 33, 34]), model=ScalarModel(intercept=0.9814421780382513, linear_terms=array([0.00759072]), square_terms=array([[6.41720368e-05]]), scale=3.857791736503532e-05, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=36, candidate_x=array([6.29655666]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-1.7035677785298975, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=9.725997928079836e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 35, 36]), model=ScalarModel(intercept=0.9249417998725926, linear_terms=array([-0.012476]), square_terms=array([[0.00042796]]), scale=9.725997928079836e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6517,10 +6527,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=35, candidate_x=array([6.37399242]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-7.4178706136732275, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=1.928895868251766e-05, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 34, 35]), model=ScalarModel(intercept=0.970894147014174, linear_terms=array([0.01223018]), square_terms=array([[0.00020966]]), scale=1.928895868251766e-05, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=37, candidate_x=array([6.29658584]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-9.203780525169682, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=4.862998964039918e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 36, 37]), model=ScalarModel(intercept=0.9771620923084195, linear_terms=array([0.01222736]), square_terms=array([[0.00026769]]), scale=4.862998964039918e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6568,10 +6578,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=36, candidate_x=array([6.37401171]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-1.7785846257234272, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 34, 35]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=9.64447934125883e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 35, 36]), model=ScalarModel(intercept=0.9216087520494944, linear_terms=array([-0.01315391]), square_terms=array([[0.00042281]]), scale=9.64447934125883e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=38, candidate_x=array([6.29657125]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-1.897511379820083, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=2.431499482019959e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 37, 38]), model=ScalarModel(intercept=0.959431534837272, linear_terms=array([0.01599091]), square_terms=array([[0.00047403]]), scale=2.431499482019959e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6619,10 +6629,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=37, candidate_x=array([6.37404065]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-9.067352766764817, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=4.822239670629415e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 36, 37]), model=ScalarModel(intercept=0.9760339180445036, linear_terms=array([0.01257345]), square_terms=array([[0.00026316]]), scale=4.822239670629415e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=39, candidate_x=array([6.29657368]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-5.075025280651171, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=1.2157497410099795e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39]), model=ScalarModel(intercept=0.9481238798141679, linear_terms=array([-0.00583462]), square_terms=array([[8.08382363e-05]]), scale=1.2157497410099795e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6670,10 +6680,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=38, candidate_x=array([6.37402618]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-1.8562082301201086, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=2.4111198353147076e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 37, 38]), model=ScalarModel(intercept=0.9570394711222546, linear_terms=array([0.01677996]), square_terms=array([[0.00046953]]), scale=2.4111198353147076e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=40, candidate_x=array([6.29657733]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-5.948703911554294, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40]), model=ScalarModel(intercept=0.9564868186794058, linear_terms=array([-0.00246996]), square_terms=array([[2.51370374e-05]]), scale=1e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6721,10 +6731,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=39, candidate_x=array([6.37402859]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-5.072855483429943, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=1.2055599176573538e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39]), model=ScalarModel(intercept=0.9460165876341217, linear_terms=array([-0.00587267]), square_terms=array([[8.04741154e-05]]), scale=1.2055599176573538e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=41, candidate_x=array([6.29657711]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-11.90753372784116, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40, 41]), model=ScalarModel(intercept=0.9570074462591432, linear_terms=array([-0.00235431]), square_terms=array([[1.11715152e-05]]), scale=1e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6772,10 +6782,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=40, candidate_x=array([6.37403221]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-6.026264047904024, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40]), model=ScalarModel(intercept=0.9541114142866567, linear_terms=array([-0.00259899]), square_terms=array([[2.55399204e-05]]), scale=1e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=42, candidate_x=array([6.29657711]), index=24, x=array([6.29657611]), fval=0.926970389682947, rho=-31.213254935770046, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657611]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.9660020942409236, linear_terms=array([7.58335514e-05]), square_terms=array([[4.53705304e-06]]), scale=1e-06, shift=array([6.29657611])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6823,10 +6833,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=41, candidate_x=array([6.374032]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-11.280839901153268, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40, 41]), model=ScalarModel(intercept=0.9543560836259272, linear_terms=array([-0.00255752]), square_terms=array([[1.14602432e-05]]), scale=1e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=43, candidate_x=array([6.29657511]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=350.31120664478067, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=1.000000000139778e-06, relative_step_length=1.000000000139778, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=2e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.9583242400545079, linear_terms=array([-0.0022253]), square_terms=array([[0.00017439]]), scale=2e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6874,10 +6884,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=42, candidate_x=array([6.374032]), index=24, x=array([6.374031]), fval=0.9235392753063931, rho=-29.958222990581294, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40, 41]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.374031]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=0.9638706904692468, linear_terms=array([3.99425596e-05]), square_terms=array([[4.30374416e-06]]), scale=1e-06, shift=array([6.374031])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=44, candidate_x=array([6.29657711]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-48.25392147842716, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 40, 41, 42, 43]), old_indices_discarded=array([38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 41, 42, 43, 44]), model=ScalarModel(intercept=0.961135716464267, linear_terms=array([0.00323214]), square_terms=array([[9.70829429e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6925,10 +6935,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=43, candidate_x=array([6.37403]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=743.6408267689862, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([24, 38, 39, 40, 41, 42]), old_indices_discarded=array([], dtype=int64), step_length=1.000000000139778e-06, relative_step_length=1.000000000139778, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=2e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.9560499712746928, linear_terms=array([-0.00271948]), square_terms=array([[0.00017506]]), scale=2e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=45, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-26.82644996215815, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 41, 42, 43, 44]), old_indices_discarded=array([38, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 42, 43, 44, 45]), model=ScalarModel(intercept=0.9686338714668369, linear_terms=array([0.00131045]), square_terms=array([[3.71483458e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -6976,10 +6986,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=44, candidate_x=array([6.374032]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-41.539522738037384, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 40, 41, 42, 43]), old_indices_discarded=array([38]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 41, 42, 43, 44]), model=ScalarModel(intercept=0.9590165888199659, linear_terms=array([0.00335469]), square_terms=array([[9.8606967e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=46, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-47.91874458305761, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 42, 43, 44, 45]), old_indices_discarded=array([38, 40, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 43, 44, 45, 46]), model=ScalarModel(intercept=0.9628062685513716, linear_terms=array([-0.0045808]), square_terms=array([[7.05678932e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7027,10 +7037,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=45, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-27.24730914937388, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 41, 42, 43, 44]), old_indices_discarded=array([38, 40]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 42, 43, 44, 45]), model=ScalarModel(intercept=0.9669575471102445, linear_terms=array([0.00149799]), square_terms=array([[3.85885665e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=47, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-11.164143107883334, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 43, 44, 45, 46]), old_indices_discarded=array([38, 40, 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 43, 45, 46, 47]), model=ScalarModel(intercept=0.9487961653597279, linear_terms=array([-0.02455412]), square_terms=array([[0.00125896]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7078,10 +7088,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=46, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-43.944161059460846, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 42, 43, 44, 45]), old_indices_discarded=array([38, 40, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 43, 44, 45, 46]), model=ScalarModel(intercept=0.9608303032554155, linear_terms=array([-0.00456942]), square_terms=array([[6.69843926e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=48, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-6.192931178391938, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 43, 45, 46, 47]), old_indices_discarded=array([38, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 43, 45, 46, 47, 48]), model=ScalarModel(intercept=0.9607377769592089, linear_terms=array([0.00226746]), square_terms=array([[5.41369674e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7129,10 +7139,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=47, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-11.82275374731701, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 43, 44, 45, 46]), old_indices_discarded=array([38, 40, 41, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 39, 43, 45, 46, 47]), model=ScalarModel(intercept=0.9460450760451854, linear_terms=array([-0.02582148]), square_terms=array([[0.00124205]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=49, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-40.89704746217631, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 43, 45, 46, 47, 48]), old_indices_discarded=array([38, 39, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.9732525470526511, linear_terms=array([0.00572826]), square_terms=array([[0.00014173]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7180,10 +7190,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=48, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-6.248754779708172, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 39, 43, 45, 46, 47]), old_indices_discarded=array([38, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([24, 43, 45, 46, 47, 48]), model=ScalarModel(intercept=0.9585866091108527, linear_terms=array([0.00290552]), square_terms=array([[5.37773686e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=50, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-13.309724227891063, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 45, 46, 47, 48, 49]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=0.971902338533044, linear_terms=array([0.00737746]), square_terms=array([[0.00023813]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7231,10 +7241,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=49, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-33.60349066864824, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([24, 43, 45, 46, 47, 48]), old_indices_discarded=array([38, 39, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=0.9717892076795451, linear_terms=array([0.00645849]), square_terms=array([[0.00013996]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=51, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.3614647788328331, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 46, 47, 48, 49, 50]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=0.9643784307177906, linear_terms=array([0.01663158]), square_terms=array([[0.00061505]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7282,10 +7292,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=50, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-12.411571174571728, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 45, 46, 47, 48, 49]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=0.9703467337906204, linear_terms=array([0.00822181]), square_terms=array([[0.00023938]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=52, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-6.51122600421527, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 47, 48, 49, 50, 51]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=0.9812904134605892, linear_terms=array([0.02163235]), square_terms=array([[0.00107227]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7333,10 +7343,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=51, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.2160611010902618, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 46, 47, 48, 49, 50]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=0.9623901807830201, linear_terms=array([0.01801568]), square_terms=array([[0.00061893]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=53, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-5.0170919543990715, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 48, 49, 50, 51, 52]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.901199737063377, linear_terms=array([-0.07072977]), square_terms=array([[0.01171341]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7384,10 +7394,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=52, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-6.377746091990022, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 47, 48, 49, 50, 51]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 48, 49, 50, 51, 52]), model=ScalarModel(intercept=0.9805784248288837, linear_terms=array([0.02338952]), square_terms=array([[0.00107577]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=54, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.839558870179515, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 49, 50, 51, 52, 53]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.9726320968065401, linear_terms=array([0.00811743]), square_terms=array([[0.00012925]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7435,10 +7445,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=53, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.920574950162586, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 48, 49, 50, 51, 52]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=0.8954365771565683, linear_terms=array([-0.07521733]), square_terms=array([[0.01164434]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=55, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-7.742122314819738, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 50, 51, 52, 53, 54]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.9713629899413632, linear_terms=array([0.00999865]), square_terms=array([[0.00019659]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7486,10 +7496,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=54, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.8144846182260221, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 49, 50, 51, 52, 53]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 50, 51, 52, 53, 54]), model=ScalarModel(intercept=0.9710071801247617, linear_terms=array([0.00848024]), square_terms=array([[0.00013339]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=56, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-7.46238606112956, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 51, 52, 53, 54, 55]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.9777541386117766, linear_terms=array([0.00043123]), square_terms=array([[0.00013867]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7537,10 +7547,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=55, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-7.848353432941635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 50, 51, 52, 53, 54]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 51, 52, 53, 54, 55]), model=ScalarModel(intercept=0.9697072109383787, linear_terms=array([0.01040672]), square_terms=array([[0.0002038]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=57, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-212.7794913863689, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 52, 53, 54, 55, 56]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.9750271552437775, linear_terms=array([0.00449856]), square_terms=array([[0.00020812]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7588,10 +7598,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=56, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-7.552446687655948, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 51, 52, 53, 54, 55]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 52, 53, 54, 55, 56]), model=ScalarModel(intercept=0.9764798288002208, linear_terms=array([0.00026806]), square_terms=array([[0.000143]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=58, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-11.831325307573865, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 53, 54, 55, 56, 57]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.969987196069964, linear_terms=array([0.01202742]), square_terms=array([[0.00030133]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7639,10 +7649,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=57, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-416.08394865369763, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 52, 53, 54, 55, 56]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 53, 54, 55, 56, 57]), model=ScalarModel(intercept=0.9735745671797933, linear_terms=array([0.00460202]), square_terms=array([[0.0002148]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=59, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-14.356451753216454, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 54, 55, 56, 57, 58]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=0.9011997370633767, linear_terms=array([-0.0787215]), square_terms=array([[0.01385309]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7690,10 +7700,10 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=58, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-12.401911544499761, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 53, 54, 55, 56, 57]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 54, 55, 56, 57, 58]), model=ScalarModel(intercept=0.968265847993553, linear_terms=array([0.01253358]), square_terms=array([[0.00030935]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=60, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-0.7723312291690756, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 55, 56, 57, 58, 59]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 56, 57, 58, 59, 60]), model=ScalarModel(intercept=0.9560700282691561, linear_terms=array([-0.02855868]), square_terms=array([[0.0011524]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7741,10 +7751,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=59, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-14.52868124965449, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 54, 55, 56, 57, 58]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 55, 56, 57, 58, 59]), model=ScalarModel(intercept=0.895436577156569, linear_terms=array([-0.08387051]), square_terms=array([[0.0137684]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=61, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3.4946240913004023, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 56, 57, 58, 59, 60]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.9721177451239259, linear_terms=array([-0.0137734]), square_terms=array([[0.00028476]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7792,10 +7803,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=60, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-0.7765465531995202, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 55, 56, 57, 58, 59]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 56, 57, 58, 59, 60]), model=ScalarModel(intercept=0.9540449086499131, linear_terms=array([-0.02983055]), square_terms=array([[0.0011369]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=62, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3.679345058665512, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 57, 58, 59, 60, 61]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=0.9727247016349269, linear_terms=array([-0.01702576]), square_terms=array([[0.00043182]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7843,11 +7855,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=61, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-3.5512810508335253, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 56, 57, 58, 59, 60]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=0.9709984685022446, linear_terms=array([-0.01433599]), square_terms=array([[0.00028235]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=63, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3.499131124546599, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 58, 59, 60, 61, 62]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.9873536855941696, linear_terms=array([-0.03304009]), square_terms=array([[0.00164576]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7895,11 +7907,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=62, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-3.7409453568477486, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 57, 58, 59, 60, 61]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 58, 59, 60, 61, 62]), model=ScalarModel(intercept=0.9715381989668214, linear_terms=array([-0.01788953]), square_terms=array([[0.00043792]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=64, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-4.364899636918336, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 59, 60, 61, 62, 63]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=0.9011997370633775, linear_terms=array([0.07332833]), square_terms=array([[0.01257141]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7947,11 +7959,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=63, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-3.4819301877587026, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 58, 59, 60, 61, 62]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=0.9867026303307392, linear_terms=array([-0.03475504]), square_terms=array([[0.00164416]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=65, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-0.6614885149988173, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 60, 61, 62, 63, 64]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=0.949690571467247, linear_terms=array([0.02972132]), square_terms=array([[0.00160699]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -7999,11 +8011,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=64, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.389939961604253, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 59, 60, 61, 62, 63]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=0.8954365771565688, linear_terms=array([0.07826238]), square_terms=array([[0.01248644]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=66, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.0626156826914683, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 61, 62, 63, 64, 65]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=0.9557481589759595, linear_terms=array([0.01710587]), square_terms=array([[0.00060795]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8051,11 +8063,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=65, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-0.6447577129958585, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 60, 61, 62, 63, 64]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=0.9466454837789505, linear_terms=array([0.03169689]), square_terms=array([[0.00160952]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=67, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.528896603153544, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 62, 63, 64, 65, 66]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 63, 64, 65, 66, 67]), model=ScalarModel(intercept=0.9609288195036394, linear_terms=array([0.02169846]), square_terms=array([[0.00081495]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8103,11 +8115,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=66, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.9973170508497473, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 61, 62, 63, 64, 65]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=0.9527779187772669, linear_terms=array([0.01851468]), square_terms=array([[0.00061051]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=68, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-6.104918808283114, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 63, 64, 65, 66, 67]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 64, 65, 66, 67, 68]), model=ScalarModel(intercept=0.9782254768179305, linear_terms=array([0.01946156]), square_terms=array([[0.00072685]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8155,11 +8167,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=67, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.397444810328198, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 62, 63, 64, 65, 66]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 63, 64, 65, 66, 67]), model=ScalarModel(intercept=0.958185799746539, linear_terms=array([0.02344806]), square_terms=array([[0.0008234]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=69, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-4.324222510133547, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 64, 65, 66, 67, 68]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.9011997370633769, linear_terms=array([-0.06621915]), square_terms=array([[0.00935899]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8207,11 +8219,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=68, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-5.992658251958798, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 63, 64, 65, 66, 67]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 64, 65, 66, 67, 68]), model=ScalarModel(intercept=0.9769156610452606, linear_terms=array([0.02113908]), square_terms=array([[0.0007336]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=70, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.669826779006717, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 65, 66, 67, 68, 69]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=0.9901984075284357, linear_terms=array([0.02341344]), square_terms=array([[0.00094961]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8259,11 +8271,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=69, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.225335718625612, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 64, 65, 66, 67, 68]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 65, 66, 67, 68, 69]), model=ScalarModel(intercept=0.8954365771565692, linear_terms=array([-0.06996539]), square_terms=array([[0.00926287]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=71, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.904244575475609, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 66, 67, 68, 69, 70]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.990881047332608, linear_terms=array([0.02244335]), square_terms=array([[0.00078803]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8311,11 +8323,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=70, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.6578450467722483, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 65, 66, 67, 68, 69]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 66, 67, 68, 69, 70]), model=ScalarModel(intercept=0.989570000301797, linear_terms=array([0.0250848]), square_terms=array([[0.00095958]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=72, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-4.839887853269327, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 67, 68, 69, 70, 71]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.9971227899021522, linear_terms=array([0.01324147]), square_terms=array([[0.00030581]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8363,11 +8375,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=71, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.8876805760374538, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 66, 67, 68, 69, 70]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 67, 68, 69, 70, 71]), model=ScalarModel(intercept=0.990481321514407, linear_terms=array([0.02377403]), square_terms=array([[0.00079094]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=73, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-7.609739326531269, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 68, 69, 70, 71, 72]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.9942997108894068, linear_terms=array([0.01743208]), square_terms=array([[0.00043783]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8415,11 +8427,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=72, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.884990039640256, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 67, 68, 69, 70, 71]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 68, 69, 70, 71, 72]), model=ScalarModel(intercept=0.9973293427411523, linear_terms=array([0.01366283]), square_terms=array([[0.00030845]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=74, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-7.536909888327736, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 69, 70, 71, 72, 73]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=0.998746622653695, linear_terms=array([0.01083351]), square_terms=array([[0.00022245]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8467,11 +8479,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=73, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-7.746059595785719, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 68, 69, 70, 71, 72]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 69, 70, 71, 72, 73]), model=ScalarModel(intercept=0.9942193540998723, linear_terms=array([0.01828398]), square_terms=array([[0.00043995]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=75, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-8.892094724966096, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 70, 71, 72, 73, 74]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 71, 72, 73, 74, 75]), model=ScalarModel(intercept=0.901199737063377, linear_terms=array([-0.08981365]), square_terms=array([[0.01854973]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8519,11 +8531,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=74, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-7.617021469443004, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 69, 70, 71, 72, 73]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 70, 71, 72, 73, 74]), model=ScalarModel(intercept=0.9989224875607232, linear_terms=array([0.01130032]), square_terms=array([[0.00022684]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=76, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-0.7550009058115424, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 71, 72, 73, 74, 75]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.9635716678759568, linear_terms=array([-0.03533596]), square_terms=array([[0.00191405]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8571,11 +8583,11 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=75, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-9.154189998384652, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 70, 71, 72, 73, 74]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 71, 72, 73, 74, 75]), model=ScalarModel(intercept=0.8954365771565689, linear_terms=array([-0.09622057]), square_terms=array([[0.01850389]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=77, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.2227604926832742, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 72, 73, 74, 75, 76]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 73, 74, 75, 76, 77]), model=ScalarModel(intercept=0.9728307236425283, linear_terms=array([-0.02260775]), square_terms=array([[0.00077284]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8623,11 +8635,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=76, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-0.7405218856066593, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 71, 72, 73, 74, 75]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=0.9617952098147389, linear_terms=array([-0.03770685]), square_terms=array([[0.00195309]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=78, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-4.912281157098232, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 73, 74, 75, 76, 77]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
+       72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 74, 75, 76, 77, 78]), model=ScalarModel(intercept=0.980066068010876, linear_terms=array([-0.0114677]), square_terms=array([[0.00019952]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8675,11 +8688,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=77, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.189143519430688, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 72, 73, 74, 75, 76]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 73, 74, 75, 76, 77]), model=ScalarModel(intercept=0.9713937260321144, linear_terms=array([-0.0242172]), square_terms=array([[0.00079924]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=79, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-15.054335927132607, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 74, 75, 76, 77, 78]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
+       72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 75, 76, 77, 78, 79]), model=ScalarModel(intercept=0.9750883415685396, linear_terms=array([0.01857201]), square_terms=array([[0.0005707]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8727,12 +8741,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=78, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.805519080309879, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 73, 74, 75, 76, 77]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=80, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.8312942359312387, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 75, 76, 77, 78, 79]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 74, 75, 76, 77, 78]), model=ScalarModel(intercept=0.9791696313797453, linear_terms=array([-0.01284968]), square_terms=array([[0.00021469]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=0.9515258564941137, linear_terms=array([0.04410118]), square_terms=array([[0.00277261]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8780,12 +8794,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=79, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-14.126875865381736, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 74, 75, 76, 77, 78]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=81, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.8350552989804425, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 76, 77, 78, 79, 80]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 75, 76, 77, 78, 79]), model=ScalarModel(intercept=0.9740905740597539, linear_terms=array([0.01866858]), square_terms=array([[0.00053605]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=0.9712537829841698, linear_terms=array([0.03370378]), square_terms=array([[0.00154149]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8833,12 +8847,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=80, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.9854886747368625, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 75, 76, 77, 78, 79]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=82, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.703346667158081, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 77, 78, 79, 80, 81]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=0.9490112162030394, linear_terms=array([0.04590241]), square_terms=array([[0.00274205]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=0.9796725202880812, linear_terms=array([0.03743284]), square_terms=array([[0.00180059]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8886,12 +8900,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=81, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.8690707389694687, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 76, 77, 78, 79, 80]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=83, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-4.039089672729042, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 78, 79, 80, 81, 82]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=0.9696421156310081, linear_terms=array([0.03493073]), square_terms=array([[0.00153359]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 79, 80, 81, 82, 83]), model=ScalarModel(intercept=0.9925750608950611, linear_terms=array([0.02644582]), square_terms=array([[0.00081564]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8939,12 +8953,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=82, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.7146383619861583, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 77, 78, 79, 80, 81]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=84, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3.427105690800181, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 79, 80, 81, 82, 83]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=0.9782831926338201, linear_terms=array([0.03907187]), square_terms=array([[0.00180805]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 80, 81, 82, 83, 84]), model=ScalarModel(intercept=0.9011997370633774, linear_terms=array([-0.07302685]), square_terms=array([[0.01332498]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -8992,12 +9006,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=83, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.089373842262245, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 78, 79, 80, 81, 82]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=85, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.776389896554375, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 80, 81, 82, 83, 84]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 79, 80, 81, 82, 83]), model=ScalarModel(intercept=0.9920082978715767, linear_terms=array([0.02756544]), square_terms=array([[0.00082584]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 81, 82, 83, 84, 85]), model=ScalarModel(intercept=0.9791911086080595, linear_terms=array([-0.00310459]), square_terms=array([[6.35738892e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9045,12 +9059,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=84, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-3.4973337435800445, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 79, 80, 81, 82, 83]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=86, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-30.020972618776277, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 81, 82, 83, 84, 85]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 80, 81, 82, 83, 84]), model=ScalarModel(intercept=0.8954365771565689, linear_terms=array([-0.07802743]), square_terms=array([[0.01316912]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 82, 83, 84, 85, 86]), model=ScalarModel(intercept=0.9831994724381408, linear_terms=array([0.00050188]), square_terms=array([[3.66012196e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9098,12 +9112,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=85, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.7467676046276963, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 80, 81, 82, 83, 84]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=87, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-141.29571188964906, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 82, 83, 84, 85, 86]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 81, 82, 83, 84, 85]), model=ScalarModel(intercept=0.9781312327290098, linear_terms=array([-0.0032118]), square_terms=array([[6.52377716e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 83, 84, 85, 86, 87]), model=ScalarModel(intercept=0.9849341114835274, linear_terms=array([-0.0016803]), square_terms=array([[7.13011777e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9151,12 +9165,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=86, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-30.610568294907033, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 81, 82, 83, 84, 85]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=88, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-81.33131468673191, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 83, 84, 85, 86, 87]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 82, 83, 84, 85, 86]), model=ScalarModel(intercept=0.9822317232877025, linear_terms=array([0.00051227]), square_terms=array([[3.94210705e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 84, 85, 86, 87, 88]), model=ScalarModel(intercept=0.9796315108609815, linear_terms=array([0.02090956]), square_terms=array([[0.00073164]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9204,12 +9218,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=87, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-146.25942178167136, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 82, 83, 84, 85, 86]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=89, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-0.32023964902722024, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 84, 85, 86, 87, 88]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 83, 84, 85, 86, 87]), model=ScalarModel(intercept=0.9841401608974134, linear_terms=array([-0.00188636]), square_terms=array([[7.21272905e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 85, 86, 87, 88, 89]), model=ScalarModel(intercept=0.9619729301842395, linear_terms=array([0.04070689]), square_terms=array([[0.00260845]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9257,12 +9271,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=88, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-75.4262961727173, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 83, 84, 85, 86, 87]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=90, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.864562124188757, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 85, 86, 87, 88, 89]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 84, 85, 86, 87, 88]), model=ScalarModel(intercept=0.978289722334968, linear_terms=array([0.02170503]), square_terms=array([[0.0007227]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 86, 87, 88, 89, 90]), model=ScalarModel(intercept=0.9715462463919681, linear_terms=array([0.02229722]), square_terms=array([[0.00081231]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9310,12 +9324,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=89, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-0.25988738304116815, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 84, 85, 86, 87, 88]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=91, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-1.884764529180674, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 86, 87, 88, 89, 90]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 85, 86, 87, 88, 89]), model=ScalarModel(intercept=0.9592223363466464, linear_terms=array([0.04314273]), square_terms=array([[0.00260706]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 87, 88, 89, 90, 91]), model=ScalarModel(intercept=0.971116104827485, linear_terms=array([0.02375527]), square_terms=array([[0.00108583]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9363,12 +9377,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=90, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.8687535049131903, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 85, 86, 87, 88, 89]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=92, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3.0094787688452946, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 87, 88, 89, 90, 91]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 86, 87, 88, 89, 90]), model=ScalarModel(intercept=0.9693975226726794, linear_terms=array([0.02323999]), square_terms=array([[0.00081525]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 88, 89, 90, 91, 92]), model=ScalarModel(intercept=0.9712646251072484, linear_terms=array([0.02354412]), square_terms=array([[0.00105094]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9416,12 +9430,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=91, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-1.8961334720621483, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 86, 87, 88, 89, 90]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=93, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.922280345887979, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 88, 89, 90, 91, 92]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 87, 88, 89, 90, 91]), model=ScalarModel(intercept=0.9687171619126889, linear_terms=array([0.02456566]), square_terms=array([[0.00106407]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 89, 90, 91, 92, 93]), model=ScalarModel(intercept=0.9011997370633772, linear_terms=array([-0.05452557]), square_terms=array([[0.00743282]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9469,12 +9483,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=92, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-3.05920987872928, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 87, 88, 89, 90, 91]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=94, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-2.0109666785915747, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 89, 90, 91, 92, 93]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 88, 89, 90, 91, 92]), model=ScalarModel(intercept=0.9688604480212463, linear_terms=array([0.02436072]), square_terms=array([[0.00103408]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 90, 91, 92, 93, 94]), model=ScalarModel(intercept=0.9661294479541731, linear_terms=array([0.00167448]), square_terms=array([[4.55984038e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9522,12 +9536,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=93, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.9957360414277567, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 88, 89, 90, 91, 92]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=95, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-39.91584217353992, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 90, 91, 92, 93, 94]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 89, 90, 91, 92, 93]), model=ScalarModel(intercept=0.8954365771565687, linear_terms=array([-0.05774553]), square_terms=array([[0.00737231]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
+       89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 91, 92, 93, 94, 95]), model=ScalarModel(intercept=0.9618919514497689, linear_terms=array([0.00799851]), square_terms=array([[0.00014224]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9575,12 +9590,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=94, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-2.011783420567907, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 89, 90, 91, 92, 93]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=96, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-13.220750226677673, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 91, 92, 93, 94, 95]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 90, 91, 92, 93, 94]), model=ScalarModel(intercept=0.9644856666238031, linear_terms=array([0.00204837]), square_terms=array([[4.92290844e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
+       89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 92, 93, 94, 95, 96]), model=ScalarModel(intercept=0.9678354499179974, linear_terms=array([-0.0008971]), square_terms=array([[8.33138157e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9628,13 +9644,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=95, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-34.72262523507854, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 90, 91, 92, 93, 94]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=97, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-100.67371075348187, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 92, 93, 94, 95, 96]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 91, 92, 93, 94, 95]), model=ScalarModel(intercept=0.9599770032633489, linear_terms=array([0.00877807]), square_terms=array([[0.00014913]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 93, 94, 95, 96, 97]), model=ScalarModel(intercept=0.9716993823848853, linear_terms=array([0.00429668]), square_terms=array([[8.40196909e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9682,13 +9698,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=96, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-12.738581437762173, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 91, 92, 93, 94, 95]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=98, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-20.67117084790136, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 93, 94, 95, 96, 97]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 92, 93, 94, 95, 96]), model=ScalarModel(intercept=0.9663071204629095, linear_terms=array([-0.0006946]), square_terms=array([[8.16078971e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 94, 95, 96, 97, 98]), model=ScalarModel(intercept=0.9745628742669177, linear_terms=array([0.0007128]), square_terms=array([[9.53079999e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9736,13 +9752,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=97, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-137.65479109813177, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 92, 93, 94, 95, 96]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=99, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-106.28536094043946, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 94, 95, 96, 97, 98]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 93, 94, 95, 96, 97]), model=ScalarModel(intercept=0.9701094093974236, linear_terms=array([0.00430136]), square_terms=array([[7.91145485e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 95, 96, 97, 98, 99]), model=ScalarModel(intercept=0.9640395921164611, linear_terms=array([-0.0102239]), square_terms=array([[0.00073706]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9790,13 +9806,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=98, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-21.84646656095411, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 93, 94, 95, 96, 97]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=100, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-5.047798565349715, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 95, 96, 97, 98, 99]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 94, 95, 96, 97, 98]), model=ScalarModel(intercept=0.9731113186701557, linear_terms=array([0.00054501]), square_terms=array([[8.90271481e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  96,  97,  98,  99, 100]), model=ScalarModel(intercept=0.9644818516534253, linear_terms=array([-0.01243063]), square_terms=array([[0.00034918]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9844,13 +9860,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=99, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-149.52753243242998, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 94, 95, 96, 97, 98]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=101, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-12.761396554927043, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  96,  97,  98,  99, 100]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([43, 95, 96, 97, 98, 99]), model=ScalarModel(intercept=0.9616405371544691, linear_terms=array([-0.01138011]), square_terms=array([[0.00071608]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  97,  98,  99, 100, 101]), model=ScalarModel(intercept=0.9725559518808135, linear_terms=array([0.0114335]), square_terms=array([[0.00027013]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9898,13 +9914,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=100, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-4.816749687222443, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([43, 95, 96, 97, 98, 99]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=102, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-12.352014050324604, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  97,  98,  99, 100, 101]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  96,  97,  98,  99, 100]), model=ScalarModel(intercept=0.9623379458251654, linear_terms=array([-0.01333699]), square_terms=array([[0.00034843]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  98,  99, 100, 101, 102]), model=ScalarModel(intercept=0.9826031735985405, linear_terms=array([-0.00242597]), square_terms=array([[8.93270334e-05]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -9952,13 +9968,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=101, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-12.48056566835165, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  96,  97,  98,  99, 100]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=103, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-42.196247268613014, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  98,  99, 100, 101, 102]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  97,  98,  99, 100, 101]), model=ScalarModel(intercept=0.9708185701345904, linear_terms=array([0.0118014]), square_terms=array([[0.00026222]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  99, 100, 101, 102, 103]), model=ScalarModel(intercept=0.9846019792639674, linear_terms=array([0.00202494]), square_terms=array([[0.00031667]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10006,13 +10022,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=102, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-12.579493918723726, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  97,  98,  99, 100, 101]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=104, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-23.16983183076578, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  99, 100, 101, 102, 103]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94, 95, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  98,  99, 100, 101, 102]), model=ScalarModel(intercept=0.9814684482841655, linear_terms=array([-0.00256306]), square_terms=array([[8.80024795e-05]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.9788412677462819, linear_terms=array([0.0088008]), square_terms=array([[0.00018827]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10060,13 +10076,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=103, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-41.857119241112876, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  98,  99, 100, 101, 102]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=105, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-8.536243236912528, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 100, 101, 102, 103, 104]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
        55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
        72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94, 95, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43,  99, 100, 101, 102, 103]), model=ScalarModel(intercept=0.9834518786866443, linear_terms=array([0.00206491]), square_terms=array([[0.00031529]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 101, 102, 103, 104, 105]), model=ScalarModel(intercept=0.9872819287534297, linear_terms=array([0.01708121]), square_terms=array([[0.00060456]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10114,13 +10130,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=104, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-23.98387228056005, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43,  99, 100, 101, 102, 103]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94, 95, 96, 97, 98]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 100, 101, 102, 103, 104]), model=ScalarModel(intercept=0.9773530448918726, linear_terms=array([0.00923626]), square_terms=array([[0.00018451]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=106, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-5.559166101696771, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 101, 102, 103, 104, 105]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+        51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
+        64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
+        77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
+        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 102, 103, 104, 105, 106]), model=ScalarModel(intercept=0.9709037484204576, linear_terms=array([-0.00723915]), square_terms=array([[0.00064196]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10168,13 +10185,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=105, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-8.514800532082953, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 100, 101, 102, 103, 104]), old_indices_discarded=array([24, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
-       55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
-       89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 101, 102, 103, 104, 105]), model=ScalarModel(intercept=0.986034175254755, linear_terms=array([0.01790239]), square_terms=array([[0.00060868]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=107, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-16.02633467569126, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 102, 103, 104, 105, 106]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+        51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
+        64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
+        77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
+        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 103, 104, 105, 106, 107]), model=ScalarModel(intercept=0.9725604010289675, linear_terms=array([0.01428233]), square_terms=array([[0.00053636]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10222,14 +10240,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=106, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-5.631861106735954, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 101, 102, 103, 104, 105]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=108, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-8.646982717239249, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 103, 104, 105, 106, 107]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
-        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 102, 103, 104, 105, 106]), model=ScalarModel(intercept=0.9688653297176435, linear_terms=array([-0.00784153]), square_terms=array([[0.00063712]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 104, 105, 106, 107, 108]), model=ScalarModel(intercept=0.9732184383362349, linear_terms=array([-0.0004789]), square_terms=array([[0.00021674]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10277,14 +10295,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=107, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-15.473220613417189, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 102, 103, 104, 105, 106]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=109, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-350.86495619867986, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 104, 105, 106, 107, 108]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
-        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 103, 104, 105, 106, 107]), model=ScalarModel(intercept=0.9706104470118138, linear_terms=array([0.01477564]), square_terms=array([[0.00053869]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
+       103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 105, 106, 107, 108, 109]), model=ScalarModel(intercept=0.98909291316177, linear_terms=array([0.00797517]), square_terms=array([[0.0002085]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10332,14 +10351,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=108, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-8.782745961255761, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 103, 104, 105, 106, 107]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=110, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-15.479232898142147, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 105, 106, 107, 108, 109]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
-        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 104, 105, 106, 107, 108]), model=ScalarModel(intercept=0.9712937793053485, linear_terms=array([-0.00068238]), square_terms=array([[0.00022246]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+        90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
+       103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 106, 107, 108, 109, 110]), model=ScalarModel(intercept=0.9955577361289816, linear_terms=array([-7.81162555e-05]), square_terms=array([[0.00013915]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10387,15 +10407,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=109, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-239.3582646941626, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 104, 105, 106, 107, 108]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=111, candidate_x=array([6.29657568]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-3848.3978362689486, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 106, 107, 108, 109, 110]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
         90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
-       103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 105, 106, 107, 108, 109]), model=ScalarModel(intercept=0.9880226177975504, linear_terms=array([0.00825098]), square_terms=array([[0.00020887]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 107, 108, 109, 110, 111]), model=ScalarModel(intercept=0.9942288146388729, linear_terms=array([-0.00156615]), square_terms=array([[0.0001054]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10443,15 +10463,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=110, candidate_x=array([6.374029]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-15.831643019937017, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 105, 106, 107, 108, 109]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=112, candidate_x=array([6.29657611]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-95.41787838927698, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 107, 108, 109, 110, 111]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
         90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
-       103, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 106, 107, 108, 109, 110]), model=ScalarModel(intercept=0.9949893019142968, linear_terms=array([-0.00043047]), square_terms=array([[0.0001416]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 108, 109, 110, 111, 112]), model=ScalarModel(intercept=0.9989671358910293, linear_terms=array([0.00529815]), square_terms=array([[0.00010313]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10499,15 +10519,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=111, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-248.81126014190247, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 106, 107, 108, 109, 110]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=113, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-19.76374452996817, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 108, 109, 110, 111, 112]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
         90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
-       103, 104, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.37403]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 107, 108, 109, 110, 111]), model=ScalarModel(intercept=0.9938523435854993, linear_terms=array([-0.00242294]), square_terms=array([[0.00010028]]), scale=1e-06, shift=array([6.37403])), vector_model=VectorModel(intercepts=array([ 0.04103311,  0.09550075,  0.10690454,  0.14582254,  0.17608627,
-        0.2066805 ,  0.24380269,  0.34635195,  0.33202466,  0.50058059,
-        0.31213959,  0.42995133,  0.17469135,  0.18032817,  0.13653344,
-        0.06075554, -0.00283504]), linear_terms=array([[0.],
+       103, 104, 105, 106, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([6.29657511]), radius=1e-06, bounds=Bounds(lower=array([2.]), upper=array([20.]))), model_indices=array([ 43, 109, 110, 111, 112, 113]), model=ScalarModel(intercept=0.9957100526760587, linear_terms=array([0.00963924]), square_terms=array([[0.00021434]]), scale=1e-06, shift=array([6.29657511])), vector_model=VectorModel(intercepts=array([ 0.04150399,  0.09673483,  0.10863824,  0.14795565,  0.17864071,
+        0.20978839,  0.24689147,  0.34942558,  0.33472616,  0.50358888,
+        0.31416312,  0.43167627,  0.16017407,  0.16675229,  0.12475296,
+        0.04988131, -0.01311336]), linear_terms=array([[0.],
        [0.],
        [0.],
        [0.],
@@ -10555,19 +10575,19 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([6.32060598]
 
        [[0.]],
 
-       [[0.]]]), scale=0.6320605981087387, shift=array([6.32060598])), candidate_index=112, candidate_x=array([6.374031]), index=43, x=array([6.37403]), fval=0.895436577156568, rho=-63.9512580925676, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 107, 108, 109, 110, 111]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
+       [[0.]]]), scale=0.6374030002146401, shift=array([6.37403])), candidate_index=114, candidate_x=array([6.29657411]), index=43, x=array([6.29657511]), fval=0.9011997370633763, rho=-8.82761262988312, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 43, 109, 110, 111, 112, 113]), old_indices_discarded=array([ 24,  38,  39,  40,  41,  42,  44,  45,  46,  47,  48,  49,  50,
         51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,
         64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,
         77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,
         90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
-       103, 104, 105, 106]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 113 entries., 'history': {'params': [{'CRRA': 6.320605981087387}, {'CRRA': 5.688545382978648}, {'CRRA': 6.9526665791961255}, {'CRRA': 5.688545382978648}, {'CRRA': 5.688545382978648}, {'CRRA': 6.9526665791961255}, {'CRRA': 5.688545382978648}, {'CRRA': 5.688545382978648}, {'CRRA': 6.9526665791961255}, {'CRRA': 5.688545382978648}, {'CRRA': 6.9526665791961255}, {'CRRA': 5.688545382978648}, {'CRRA': 6.9526665791961255}, {'CRRA': 6.372356566128265}, {'CRRA': 6.258502784401016}, {'CRRA': 6.28588298365918}, {'CRRA': 6.378159608140937}, {'CRRA': 6.275271533691997}, {'CRRA': 6.591301832746367}, {'CRRA': 6.433286683219182}, {'CRRA': 6.354279108455589}, {'CRRA': 6.5122942579827745}, {'CRRA': 6.4332866832191815}, {'CRRA': 6.314775321073793}, {'CRRA': 6.374031002146488}, {'CRRA': 6.334527214764692}, {'CRRA': 6.393782895837386}, {'CRRA': 6.383906948991937}, {'CRRA': 6.369093028723763}, {'CRRA': 6.37649998885785}, {'CRRA': 6.375265495502169}, {'CRRA': 6.374648248824329}, {'CRRA': 6.373722378807567}, {'CRRA': 6.373876690477028}, {'CRRA': 6.374108157981218}, {'CRRA': 6.373992424229123}, {'CRRA': 6.374011713187805}, {'CRRA': 6.374040646625829}, {'CRRA': 6.374026179906817}, {'CRRA': 6.3740285910266525}, {'CRRA': 6.3740322077064056}, {'CRRA': 6.374032002146488}, {'CRRA': 6.374032002146488}, {'CRRA': 6.374030002146488}, {'CRRA': 6.374032002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374031002146488}, {'CRRA': 6.374031002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374031002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 6.374029002146488}, {'CRRA': 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126.93732125899987, 128.05140812098398, 129.17956323799444, 130.32409440999618, 131.38521518200287], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 6.37403],
+       [ 6.5    ],
+       [ 5.375  ],
+       [ 8.75   ],
+       [ 4.25   ],
+       [11.     ],
+       [13.25   ],
+       [14.375  ],
+       [15.5    ],
+       [17.75   ]]), 'exploration_results': array([0.92003877, 0.9689525 , 1.43690922, 2.25496692, 3.67693587,
        3.97725656, 5.89387611, 6.6580109 , 7.17335069, 9.28222803])}}"
diff --git a/content/tables/TRP/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/TRP/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv
deleted file mode 100644
index f8e811b..0000000
--- a/content/tables/TRP/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv
+++ /dev/null
@@ -1,23953 +0,0 @@
-CRRA,2.946981513820785
-BeqShift,52.46209696182195
-BeqFac,45.67673738839485
-time_to_estimate,299.5191719532013
-params,"{'CRRA': 2.946981513820785, 'BeqShift': 52.46209696182195, 'BeqFac': 45.67673738839485}"
-criterion,49.566764326034985
-start_criterion,1.2692906053975521
-start_params,"{'CRRA': 2.9470100356768127, 'BeqShift': 52.46210215341304, 'BeqFac': 45.67673653744757}"
-algorithm,multistart_tranquilo_ls
-direction,minimize
-n_free,3
-message,
-success,
-n_criterion_evaluations,
-n_derivative_evaluations,
-n_iterations,
-history,"{'params': [{'CRRA': 2.9470100356768127, 'BeqShift': 52.46210215341305, 'BeqFac': 45.67673653744757}, {'CRRA': 2.3269886157442796, 'BeqShift': 48.35030013421974, 'BeqFac': 49.90516086555216}, {'CRRA': 6.1993751209418395, 'BeqShift': 48.233677825308455, 'BeqFac': 48.382193970401474}, {'CRRA': 7.1754343637814095, 'BeqShift': 54.80500136753705, 'BeqFac': 48.39411059113678}, {'CRRA': 4.883744706909134, 'BeqShift': 56.690526481517644, 'BeqFac': 41.56048848650462}, {'CRRA': 4.433308787710377, 'BeqShift': 56.690526481517644, 'BeqFac': 49.61518883026489}, {'CRRA': 7.153094150285612, 'BeqShift': 56.50163346927271, 'BeqFac': 49.90516086555216}, {'CRRA': 2.1857567061221572, 'BeqShift': 56.690526481517644, 'BeqFac': 47.169774123833335}, {'CRRA': 2.0025224198529896, 'BeqShift': 52.359484020273065, 'BeqFac': 49.90516086555216}, {'CRRA': 7.1754343637814095, 'BeqShift': 53.40928436953874, 'BeqFac': 41.58567450149966}, {'CRRA': 4.72255697937215, 'BeqShift': 48.352805007580784, 'BeqFac': 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71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108]}"
-convergence_report,
-multistart_info,"{'start_parameters': [{'CRRA': 2.9470100356768127, 'BeqShift': 52.46210215341305, 'BeqFac': 45.67673653744757}, {'CRRA': 3.0222209790837002, 'BeqShift': 58.025394387590964, 'BeqFac': 46.71149492214287}, {'CRRA': 3.0828691879208954, 'BeqShift': 48.856328735935456, 'BeqFac': 45.26582967150993}], 'local_optima': [Minimize with 3 free parameters terminated.
-
-Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
-
-                             one_step     five_steps 
-relative_criterion_change  4.898e-11***  1.073e-07*  
-relative_params_change     3.763e-08*    9.736e-06*  
-absolute_criterion_change  2.428e-09**   5.317e-06*  
-absolute_params_change     1.723e-06*    0.0001632   
-
-(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated.
-
-The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance.
-
-Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
-
-                             one_step    five_steps 
-relative_criterion_change  4.221e-08*    0.001161   
-relative_params_change     1.244e-06*     0.02465   
-absolute_criterion_change  2.092e-06*     0.05755   
-absolute_params_change     2.213e-05       0.7425   
-
-(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated.
-
-The tranquilo_ls algorithm reported: Relative criterion change smaller than tolerance.
-
-Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
-
-                             one_step     five_steps 
-relative_criterion_change  7.919e-10**    1.54e-06*  
-relative_params_change     3.282e-05     0.0001984   
-absolute_criterion_change  3.925e-08*    7.635e-05   
-absolute_params_change     0.0001491      0.000692   
-
-(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 2.9470100356768127, 'BeqShift': 52.46210215341304, 'BeqFac': 45.67673653744757}, {'CRRA': 3.125, 'BeqShift': 65.625, 'BeqFac': 48.125}, {'CRRA': 3.6875, 'BeqShift': 32.8125, 'BeqFac': 43.4375}, {'CRRA': 4.25, 'BeqShift': 43.75, 'BeqFac': 38.75}, {'CRRA': 4.8125, 'BeqShift': 10.9375, 'BeqFac': 46.5625}, {'CRRA': 5.375, 'BeqShift': 21.875, 'BeqFac': 66.875}, {'CRRA': 5.9375, 'BeqShift': 59.0625, 'BeqFac': 24.6875}, {'CRRA': 6.5, 'BeqShift': 52.5, 'BeqFac': 57.5}, {'CRRA': 7.0625, 'BeqShift': 19.6875, 'BeqFac': 27.8125}, {'CRRA': 7.625, 'BeqShift': 13.125, 'BeqFac': 35.625}, {'CRRA': 8.1875, 'BeqShift': 50.3125, 'BeqFac': 55.9375}, {'CRRA': 8.75, 'BeqShift': 26.25, 'BeqFac': 51.25}, {'CRRA': 9.3125, 'BeqShift': 63.4375, 'BeqFac': 34.0625}, {'CRRA': 9.875, 'BeqShift': 39.375, 'BeqFac': 29.375}, {'CRRA': 10.4375, 'BeqShift': 6.5625, 'BeqFac': 62.1875}, {'CRRA': 11.0, 'BeqShift': 35.0, 'BeqFac': 45.0}, {'CRRA': 12.125, 'BeqShift': 30.625, 'BeqFac': 23.125}, {'CRRA': 12.6875, 'BeqShift': 67.8125, 'BeqFac': 68.4375}, {'CRRA': 13.25, 'BeqShift': 8.75, 'BeqFac': 63.75}, {'CRRA': 13.8125, 'BeqShift': 45.9375, 'BeqFac': 21.5625}, {'CRRA': 14.375, 'BeqShift': 56.875, 'BeqFac': 41.875}, {'CRRA': 14.9375, 'BeqShift': 24.0625, 'BeqFac': 49.6875}, {'CRRA': 15.5, 'BeqShift': 17.5, 'BeqFac': 32.5}, {'CRRA': 16.0625, 'BeqShift': 54.6875, 'BeqFac': 52.8125}, {'CRRA': 16.625, 'BeqShift': 48.125, 'BeqFac': 60.625}, {'CRRA': 17.1875, 'BeqShift': 15.3125, 'BeqFac': 30.9375}, {'CRRA': 17.75, 'BeqShift': 61.25, 'BeqFac': 26.25}, {'CRRA': 18.3125, 'BeqShift': 28.4375, 'BeqFac': 59.0625}, {'CRRA': 18.875, 'BeqShift': 4.375, 'BeqFac': 54.375}, {'CRRA': 19.4375, 'BeqShift': 41.5625, 'BeqFac': 37.1875}], 'exploration_results': array([  49.56676961,   49.96316854,   56.86500302,   70.86254256,
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48.813375469779, 'BeqFac': 45.37742195478209}, {'CRRA': 2.9479552100695012, 'BeqShift': 48.812564965234586, 'BeqFac': 45.377475872959984}, {'CRRA': 2.947751175528098, 'BeqShift': 48.812661627767575, 'BeqFac': 45.37719940989655}, {'CRRA': 2.947322825076778, 'BeqShift': 48.81277528844998, 'BeqFac': 45.377194641641644}, {'CRRA': 2.947612336262163, 'BeqShift': 48.81272776552039, 'BeqFac': 45.3771413000922}, {'CRRA': 2.9471799353798964, 'BeqShift': 48.812744385989006, 'BeqFac': 45.37722392801185}, {'CRRA': 2.947457934931892, 'BeqShift': 48.8126791525307, 'BeqFac': 45.37713801339607}, {'CRRA': 2.947058082204029, 'BeqShift': 48.812758360864535, 'BeqFac': 45.37730870197858}, {'CRRA': 2.9473144934064366, 'BeqShift': 48.812701208761155, 'BeqFac': 45.37716760836229}, {'CRRA': 2.9469616355947976, 'BeqShift': 48.81279150482121, 'BeqFac': 45.377417474214596}], 'criterion': [49.7691038102104, 64.15568362626459, 167.65142726958572, 243.64696821212618, 88.46134222894958, 73.72786293970255, 243.64696821212618, 75.57231926100292, 75.57231926100292, 241.01915391547365, 84.7344562068525, 72.05079526442951, 232.02677204834976, 75.57231926100292, 75.57231926100292, 68.81886603342944, 55.23827378546333, 51.53414155700791, 53.730145101289885, 49.59390577896997, 55.43587418506999, 49.831347572120826, 52.87601049312321, 49.984536549290816, 50.39510232068094, 49.87667593831827, 50.14630706628617, 51.6572173171824, 56.861955173630264, 50.10819520562711, 49.583460610831146, 50.04840989247583, 49.777049640508025, 49.57267383971162, 49.571115458441426, 49.58325659107447, 49.58263474900733, 49.57178812930155, 49.572753724245494, 49.56902509640774, 49.566879566502074, 49.57748058896903, 49.56935609060321, 49.572785698090975, 49.583717038434195, 49.57386771262084, 49.56874400216965, 49.57269327285592, 49.56884473771366, 49.57936019343187, 49.57118593616847, 49.56694328330173, 49.57513061329408, 49.568930970898954, 49.566936077814994, 49.56709022005296, 49.566937938352204, 49.56728175731045, 49.566867486308425, 49.566888385492625, 49.56727201465832, 49.5668811596781, 49.56698545087935, 49.56692679902501, 49.56713656453872, 49.56686836378427, 49.56685486035354, 49.56685089650021, 49.57066781017938, 49.5681913761637, 49.56687773683666, 49.56686663742819, 49.566830414700576, 49.566858573433, 49.5668018168137, 49.5668501954106, 49.56677855415064, 49.566828696339186, 49.56677851489639], 'runtime': [0.0, 1.5056391060061287, 1.697081268997863, 1.8982690559932962, 2.111090096004773, 2.3088916770066135, 2.5306704599934164, 2.746226871997351, 2.965704840986291, 3.177069836005103, 3.4124651419988368, 3.625424617988756, 3.9922279339807574, 5.1636393849912565, 6.286245822004275, 7.399391749990173, 8.859027100988897, 9.111364087992115, 9.271626701985952, 9.479991973988945, 9.680996465001954, 9.905234421981731, 10.146303401998011, 10.354709526989609, 10.624613102001604, 10.796914353006287, 11.01030663200072, 11.237588905001758, 12.504392015980557, 13.6071954969957, 14.726336385996547, 15.847772677981993, 17.05973192700185, 18.182647648005513, 19.2807268720062, 20.69027915998595, 20.86839538998902, 21.079239287006203, 21.275484159996267, 21.471265971980756, 21.714039904996753, 21.954399673006264, 22.161604313005228, 22.38379011399229, 22.593363556981785, 22.812654743989697, 23.01980967898271, 24.328957083984278, 25.433844871004112, 26.519953128998168, 27.619738965004217, 28.850522697990527, 29.92549228700227, 31.03513433499029, 32.123559184983606, 33.51298559800489, 33.70072010799777, 33.90028235397767, 34.092039959999966, 34.28944023299846, 34.50014835500042, 34.71129972298513, 34.92342933299369, 35.134057201998075, 35.34491016899119, 35.551694634981686, 35.76641875799396, 36.94687600599718, 38.0510822449869, 39.15171169198584, 40.35883477400057, 41.43956157998764, 42.5320724209887, 43.62070867998409, 44.7046304109972, 45.7883627830015, 46.89310046899482, 48.01903305799351, 49.144519161985954], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]}}], 'exploration_sample': array([[ 2.94701004, 52.46210215, 45.67673654],
-       [ 3.125     , 65.625     , 48.125     ],
-       [ 3.6875    , 32.8125    , 43.4375    ],
-       [ 4.25      , 43.75      , 38.75      ],
-       [ 4.8125    , 10.9375    , 46.5625    ],
-       [ 5.375     , 21.875     , 66.875     ],
-       [ 5.9375    , 59.0625    , 24.6875    ],
-       [ 6.5       , 52.5       , 57.5       ],
-       [ 7.0625    , 19.6875    , 27.8125    ],
-       [ 7.625     , 13.125     , 35.625     ],
-       [ 8.1875    , 50.3125    , 55.9375    ],
-       [ 8.75      , 26.25      , 51.25      ],
-       [ 9.3125    , 63.4375    , 34.0625    ],
-       [ 9.875     , 39.375     , 29.375     ],
-       [10.4375    ,  6.5625    , 62.1875    ],
-       [11.        , 35.        , 45.        ],
-       [12.125     , 30.625     , 23.125     ],
-       [12.6875    , 67.8125    , 68.4375    ],
-       [13.25      ,  8.75      , 63.75      ],
-       [13.8125    , 45.9375    , 21.5625    ],
-       [14.375     , 56.875     , 41.875     ],
-       [14.9375    , 24.0625    , 49.6875    ],
-       [15.5       , 17.5       , 32.5       ],
-       [16.0625    , 54.6875    , 52.8125    ],
-       [16.625     , 48.125     , 60.625     ],
-       [17.1875    , 15.3125    , 30.9375    ],
-       [17.75      , 61.25      , 26.25      ],
-       [18.3125    , 28.4375    , 59.0625    ],
-       [18.875     ,  4.375     , 54.375     ],
-       [19.4375    , 41.5625    , 37.1875    ]]), 'exploration_results': array([  49.56676961,   49.96316854,   56.86500302,   70.86254256,
-         91.41859722,  118.79512928,  153.7769545 ,  196.64909171,
-        247.59160327,  306.14702953,  372.35214376,  445.09485148,
-        524.49118597,  609.1817252 ,  698.99760542,  792.97556867,
-        989.27833265, 1090.64652425, 1194.12805754, 1298.8054274 ,
-       1403.03278309, 1507.84324756, 1613.13935315, 1716.94116338,
-       1821.14091508, 1923.85751365, 2025.26551176, 2125.31701641,
-       2224.53237299, 2322.97072014])}}"
diff --git a/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv b/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv
index 487e1ed..24f47e2 100644
--- a/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv
+++ b/content/tables/TRP/WarmGlowPortfolio_estimate_results.csv
@@ -1,11 +1,11 @@
-CRRA,4.705614650734349
-BeqShift,16.964074896995744
-BeqFac,46.46325681615148
-time_to_estimate,471.4703788757324
-params,"{'CRRA': 4.705614650734349, 'BeqShift': 16.964074896995744, 'BeqFac': 46.46325681615148}"
-criterion,7.718691297994154
-start_criterion,7.578841818920466
-start_params,"{'CRRA': 4.497998813498684, 'BeqShift': 16.449904656848112, 'BeqFac': 46.24342040168151}"
+CRRA,4.682346985798744
+BeqShift,17.382474388564322
+BeqFac,46.394111222352684
+time_to_estimate,481.5962610244751
+params,"{'CRRA': 4.682346985798744, 'BeqShift': 17.382474388564322, 'BeqFac': 46.394111222352684}"
+criterion,7.7200111553560635
+start_criterion,6.289461969687898
+start_params,"{'CRRA': 4.705676392381167, 'BeqShift': 16.966223080560113, 'BeqFac': 46.465318252398745}"
 algorithm,multistart_tranquilo_ls
 direction,minimize
 n_free,3
@@ -14,27 +14,27 @@ success,
 n_criterion_evaluations,
 n_derivative_evaluations,
 n_iterations,
-history,"{'params': [{'CRRA': 4.497998813498684, 'BeqShift': 16.449904656848112, 'BeqFac': 46.24342040168151}, {'CRRA': 2.725481740266993, 'BeqShift': 12.72270357616253, 'BeqFac': 49.60460564415341}, {'CRRA': 8.225199894184266, 'BeqShift': 14.340937894776207, 'BeqFac': 49.79459449356107}, {'CRRA': 8.14508780260925, 'BeqShift': 20.177105737533694, 'BeqFac': 46.63833846775536}, {'CRRA': 5.697213871559821, 'BeqShift': 20.177105737533694, 'BeqFac': 42.68401645432884}, {'CRRA': 4.320475839845306, 'BeqShift': 19.917861322269456, 'BeqFac': 49.97062148236709}, {'CRRA': 7.867262047690456, 'BeqShift': 20.177105737533694, 'BeqFac': 49.47643682518483}, {'CRRA': 2.0097973845108674, 'BeqShift': 20.177105737533694, 'BeqFac': 47.743021342008234}, {'CRRA': 2.0, 'BeqShift': 15.507088453629825, 'BeqFac': 49.86590436822275}, {'CRRA': 7.616691453453694, 'BeqShift': 17.392134059472802, 'BeqFac': 42.51621932099593}, {'CRRA': 5.002785189877985, 'BeqShift': 12.72270357616253, 'BeqFac': 42.688963288278025}, {'CRRA': 2.1708146709693863, 'BeqShift': 20.177105737533694, 'BeqFac': 43.12295749735683}, {'CRRA': 8.225199894184266, 'BeqShift': 12.865535130923446, 'BeqFac': 48.13645283403052}, {'CRRA': 5.146523432091769, 'BeqShift': 12.72270357616253, 'BeqFac': 42.51621932099593}, {'CRRA': 5.060041550766472, 'BeqShift': 15.02502269564325, 'BeqFac': 44.33684394345911}, {'CRRA': 5.288356518436867, 'BeqShift': 15.644323489431292, 'BeqFac': 45.992606950133634}, {'CRRA': 4.973918628791782, 'BeqShift': 16.669085644476336, 'BeqFac': 45.99930270321213}, {'CRRA': 4.07411602465922, 'BeqShift': 16.245824065044296, 'BeqFac': 46.57928930468828}, {'CRRA': 3.934197231421301, 'BeqShift': 16.568931307516102, 'BeqFac': 46.197661696388536}, {'CRRA': 4.275126151735581, 'BeqShift': 16.93526685687272, 'BeqFac': 46.46451933442022}, {'CRRA': 5.012634337789958, 'BeqShift': 16.297233483174914, 'BeqFac': 46.457838722957895}, {'CRRA': 4.224885890886242, 'BeqShift': 16.459320390681384, 'BeqFac': 45.73405402853664}, {'CRRA': 4.8088540306397425, 'BeqShift': 16.83227945703964, 'BeqFac': 46.545566016885445}, {'CRRA': 4.4801398100080085, 'BeqShift': 16.931761606874883, 'BeqFac': 45.924628365492545}, {'CRRA': 4.273905540044453, 'BeqShift': 15.96827929087305, 'BeqFac': 46.01548918777204}, {'CRRA': 4.5112755773962, 'BeqShift': 16.417757891807785, 'BeqFac': 46.82041584331874}, {'CRRA': 4.572450844546997, 'BeqShift': 15.914924247657249, 'BeqFac': 46.44929969516934}, {'CRRA': 4.745599114964927, 'BeqShift': 16.249113886627253, 'BeqFac': 45.76122688872201}, {'CRRA': 4.7160682025182865, 'BeqShift': 16.969577836161843, 'BeqFac': 46.4769232146932}, {'CRRA': 5.032021596867327, 'BeqShift': 18.024771482538164, 'BeqFac': 46.94434338261181}, {'CRRA': 4.9641153021135285, 'BeqShift': 17.54464666638368, 'BeqFac': 46.46229468549674}, {'CRRA': 4.971075579038686, 'BeqShift': 17.013912048130152, 'BeqFac': 46.616915964497856}, {'CRRA': 4.62004715205243, 'BeqShift': 17.005873423830113, 'BeqFac': 46.37520828290948}, {'CRRA': 4.678685916308889, 'BeqShift': 16.956502975810015, 'BeqFac': 46.537358696785}, {'CRRA': 4.748949294503104, 'BeqShift': 16.9824877534617, 'BeqFac': 46.4693484854585}, {'CRRA': 4.705630902110383, 'BeqShift': 16.9554110914229, 'BeqFac': 46.48100589495773}, {'CRRA': 4.708304859403353, 'BeqShift': 16.9858814311731, 'BeqFac': 46.47739804922683}, {'CRRA': 4.731725886370099, 'BeqShift': 16.969216029655755, 'BeqFac': 46.48592367062023}, {'CRRA': 4.710904975162749, 'BeqShift': 16.960694010462166, 'BeqFac': 46.462066536105496}, {'CRRA': 4.715678991920344, 'BeqShift': 16.95290618163614, 'BeqFac': 46.46998011560622}, {'CRRA': 4.720388240449541, 'BeqShift': 16.958425040131196, 'BeqFac': 46.49046037050293}, {'CRRA': 4.703517491103345, 'BeqShift': 16.976485494778238, 'BeqFac': 46.46592021192742}, {'CRRA': 4.723920604127898, 'BeqShift': 16.976719858708243, 'BeqFac': 46.462307008709644}, {'CRRA': 4.71096714346186, 'BeqShift': 16.976668909552714, 'BeqFac': 46.49273454130148}, {'CRRA': 4.703693983846474, 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24.687053499961213, 8.510344063324736, 28.283764159321276, 23.54611401082387, 8.491491287950968, 9.28096102259703, 9.99197124397522, 8.066946801033449, 8.00533348481103, 8.200923238243508, 7.850300978331116, 8.162739014986265, 7.9087497261220525, 8.102967791208021, 7.841883789232617, 7.959333216932288, 7.847778180681784, 7.883249270254372, 7.971474700493697, 8.066657471859852, 8.343589985637175, 7.902897717558958, 8.141521643551082, 7.938140261779154, 7.982704412286408, 8.153801534417667, 7.7249497274541925, 8.14129260014515, 8.072392172824758, 7.9316786031099324, 7.985675067813611, 8.307050560829015, 8.307614651071198, 8.312122414901815, 8.30847320729214, 8.308637353220327, 8.308350257818564, 8.307607826811116, 8.310810637885758, 8.307816199142684, 8.307168492771904, 8.311836822951761, 8.310675891063827, 8.312077067983921, 8.085011205587413, 8.041735817351269, 7.80927169683411, 7.809274723556541, 7.809268998351818, 7.809152152436264, 7.809133654051072, 7.809272616240788, 7.80913413738995, 7.80945411448954, 7.809173540233162, 7.809254932574984, 7.8091579335572705, 7.809161530095363, 7.809277867825939, 8.14009730531154, 8.135714822388614, 8.212885567939475, 8.212879033832097, 8.212938845820409, 8.212867934094248, 8.21287723120987, 8.212902999339526, 8.212937400747036, 8.212885321710827, 8.21284650249054, 8.212938916215451, 8.212925675861689, 8.212834428864952, 8.212899512117858, 7.950419550925067, 8.053993101909251, 7.983218016024817, 7.983214402553283, 7.983212606215708, 7.983210748798216, 7.983215281703747, 7.98321816464199, 7.983217586383812, 7.9832161452917525, 7.983213385895906, 7.983211684757234, 7.983219403948549, 7.9832138805507, 7.983211935054066, 7.912773626985095, 8.402306758402728, 7.975172115335465, 7.975171705434421, 7.975171491768015, 7.97517012119938, 7.975172538157683, 7.9751716181573205, 7.9751719305529125, 7.975170662338434, 7.975170237555833, 7.975170699380063, 7.975170554006723, 7.975171392515417, 7.975171105844443, 8.129430565838975, 7.9162160317665755, 7.974367353682289, 8.22971941406081, 7.922566513200727, 8.01266034671834, 7.894609968169266, 8.256117155683556, 8.069356735257058, 8.333330462548712, 7.9793014567018155, 8.133180671133783, 8.1657388494595, 8.2511642557358, 8.075636986885993, 7.892421647379318, 8.005220763537583, 8.212647580885456, 8.397318952657365, 7.8149063638822565, 8.011297073397916, 7.944701809920572, 8.260774784024074, 8.093302995080853, 8.173443458523044, 8.094595459289339, 7.950975984623478, 8.318774292618226, 7.720011155356064, 8.131938963716014, 7.87385711281936, 7.978526260537514, 8.041134109732843, 8.118813675691431, 7.944976710767091, 8.169184800332983, 8.09798951013525, 8.0800302399593, 7.997299582925643, 7.919367094655789, 8.37822762137622, 8.230223971586216, 8.188683846053754, 7.94297614744859, 7.996376035759811, 8.05329657154966, 8.202349505598388, 8.266150593432574, 8.267612481719526, 8.21835379856386, 8.104830496522696, 8.360701795878128, 8.170806324291185, 8.02158093002115, 7.9601339915472895, 8.032064235746452, 8.092120369392902, 8.222154731568352, 8.117113927377938, 7.752495888197335, 8.317973921877698, 7.9510650569732615, 7.876350363186919, 8.071803964394052, 8.129749520721182, 8.202995365790775, 7.924933199652802, 8.059192018850913, 8.052973563360885, 8.015982454385007, 8.265847980809395, 7.985939338706451], 'runtime': [0.0, 1.5322700169926975, 1.7228302310104482, 1.9208646310144104, 2.1332449990150053, 2.361938640999142, 2.590823261998594, 2.891958430001978, 3.1140725759905763, 3.340094521001447, 3.611819473997457, 3.8394388669985346, 4.089107859006617, 106.00706634900416, 107.15606766199926, 108.87616579601308, 110.52282074399409, 110.73537979699904, 110.93869999601156, 111.14044919100706, 111.3500367430097, 111.57059045799542, 111.79390888899798, 112.06702691101236, 112.34688970199204, 112.63267803500639, 112.86494285200024, 113.1287798040139, 114.55466434301343, 115.68195928900968, 116.79288036099751, 117.93154299500748, 119.0491025139927, 120.16628614600631, 121.32941477998975, 122.58392835099949, 123.71397000801517, 124.86803827100084, 126.03855085599935, 127.26059040700784, 128.73114981499384, 128.95051324600354, 129.14707206899766, 129.36354031600058, 129.57636769599048, 129.78265580101288, 130.02725651900982, 130.25321358200745, 130.4791061040014, 130.71467895701062, 130.92645657999674, 131.19377070799237, 132.60481945201172, 133.7063623490103, 134.9575633490167, 136.40434595299303, 136.61054131001583, 136.8220401740109, 137.04561336900224, 137.28777280001668, 137.50862265200703, 137.73497709000367, 137.942509763001, 138.14999196500867, 138.3573253430077, 138.5587816509942, 138.79298727901187, 140.10094936800306, 141.23441704799188, 142.40413849698962, 143.92890803300543, 144.1291088339931, 144.32582504898892, 144.53999949101126, 144.78109319499345, 145.03109854401555, 145.4065304780088, 145.6440454669937, 145.88544622299378, 146.08050681700115, 146.28858716899413, 146.48553202001494, 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243.9742569969967, 245.08989404101158, 246.31563435398857], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 20, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 24, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 28, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 32, 33, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107]}"
 convergence_report,
-multistart_info,"{'start_parameters': [{'CRRA': 4.497998813498684, 'BeqShift': 16.449904656848112, 'BeqFac': 46.24342040168151}, {'CRRA': 4.7507897148290485, 'BeqShift': 14.416944639071934, 'BeqFac': 46.50520192108982}, {'CRRA': 4.622007804545175, 'BeqShift': 21.879383736410876, 'BeqFac': 45.047847818205156}], 'local_optima': [Minimize with 3 free parameters terminated.
+multistart_info,"{'start_parameters': [{'CRRA': 4.705676392381167, 'BeqShift': 16.966223080560113, 'BeqFac': 46.465318252398745}, {'CRRA': 4.737356122215063, 'BeqShift': 14.658507698157855, 'BeqFac': 46.465280693896815}, {'CRRA': 4.6030098356790825, 'BeqShift': 22.22100549073852, 'BeqFac': 44.991390677282226}], 'local_optima': [Minimize with 3 free parameters terminated.
 
 Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
 
                              one_step    five_steps
-relative_criterion_change  0.0006604     0.01746   
-relative_params_change     0.0008586     0.04557   
-absolute_criterion_change   0.005097      0.1348   
-absolute_params_change       0.01287       1.003   
+relative_criterion_change  0.0006397     0.03782   
+relative_params_change     1.967e-07*     0.2094   
+absolute_criterion_change   0.004939       0.292   
+absolute_params_change         1e-06*      5.031   
 
 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated.
 
 Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
 
-                            one_step    five_steps
-relative_criterion_change  0.009489     0.05143   
-relative_params_change      0.01291      0.4275   
-absolute_criterion_change    0.0738         0.4   
-absolute_params_change       0.1018       14.64   
+                            one_step   five_steps
+relative_criterion_change   0.01299     0.0513   
+relative_params_change     0.006029     0.2603   
+absolute_criterion_change    0.1009     0.3988   
+absolute_params_change       0.1238      9.927   
 
 (***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated.
 
@@ -42,13 +42,13 @@ The tranquilo_ls algorithm reported: Absolute params change smaller than toleran
 
 Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
 
-                             one_step    five_steps 
-relative_criterion_change   0.003528      0.01503   
-relative_params_change     3.561e-08*    0.003419   
-absolute_criterion_change    0.02747       0.1171   
-absolute_params_change         1e-06*      0.0176   
+                             one_step    five_steps
+relative_criterion_change   0.003173     0.01507   
+relative_params_change     1.694e-07*     0.0269   
+absolute_criterion_change    0.02472      0.1174   
+absolute_params_change         1e-06*     0.5706   
 
-(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.497998813498684, 'BeqShift': 16.449904656848112, 'BeqFac': 46.24342040168151}, {'CRRA': 4.8125, 'BeqShift': 10.9375, 'BeqFac': 46.5625}, {'CRRA': 4.25, 'BeqShift': 43.75, 'BeqFac': 38.75}, {'CRRA': 5.375, 'BeqShift': 21.875, 'BeqFac': 66.875}, {'CRRA': 3.6875, 'BeqShift': 32.8125, 'BeqFac': 43.4375}, {'CRRA': 5.9375, 'BeqShift': 59.0625, 'BeqFac': 24.6875}, {'CRRA': 3.125, 'BeqShift': 65.625, 'BeqFac': 48.125}, {'CRRA': 6.5, 'BeqShift': 52.5, 'BeqFac': 57.5}, {'CRRA': 7.0625, 'BeqShift': 19.6875, 'BeqFac': 27.8125}, {'CRRA': 7.625, 'BeqShift': 13.125, 'BeqFac': 35.625}, {'CRRA': 8.1875, 'BeqShift': 50.3125, 'BeqFac': 55.9375}, {'CRRA': 8.75, 'BeqShift': 26.25, 'BeqFac': 51.25}, {'CRRA': 9.3125, 'BeqShift': 63.4375, 'BeqFac': 34.0625}, {'CRRA': 9.875, 'BeqShift': 39.375, 'BeqFac': 29.375}, {'CRRA': 10.4375, 'BeqShift': 6.5625, 'BeqFac': 62.1875}, {'CRRA': 11.0, 'BeqShift': 35.0, 'BeqFac': 45.0}, {'CRRA': 12.125, 'BeqShift': 30.625, 'BeqFac': 23.125}, {'CRRA': 12.6875, 'BeqShift': 67.8125, 'BeqFac': 68.4375}, {'CRRA': 13.25, 'BeqShift': 8.75, 'BeqFac': 63.75}, {'CRRA': 13.8125, 'BeqShift': 45.9375, 'BeqFac': 21.5625}, {'CRRA': 14.375, 'BeqShift': 56.875, 'BeqFac': 41.875}, {'CRRA': 14.9375, 'BeqShift': 24.0625, 'BeqFac': 49.6875}, {'CRRA': 15.5, 'BeqShift': 17.5, 'BeqFac': 32.5}, {'CRRA': 16.0625, 'BeqShift': 54.6875, 'BeqFac': 52.8125}, {'CRRA': 16.625, 'BeqShift': 48.125, 'BeqFac': 60.625}, {'CRRA': 17.1875, 'BeqShift': 15.3125, 'BeqFac': 30.9375}, {'CRRA': 17.75, 'BeqShift': 61.25, 'BeqFac': 26.25}, {'CRRA': 18.3125, 'BeqShift': 28.4375, 'BeqFac': 59.0625}, {'CRRA': 18.875, 'BeqShift': 4.375, 'BeqFac': 54.375}, {'CRRA': 19.4375, 'BeqShift': 41.5625, 'BeqFac': 37.1875}], 'exploration_results': array([  7.8484842 ,   8.04234718,   8.48661498,   8.95471279,
+(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 4.705676392381167, 'BeqShift': 16.966223080560113, 'BeqFac': 46.465318252398745}, {'CRRA': 4.8125, 'BeqShift': 10.9375, 'BeqFac': 46.5625}, {'CRRA': 4.25, 'BeqShift': 43.75, 'BeqFac': 38.75}, {'CRRA': 5.375, 'BeqShift': 21.875, 'BeqFac': 66.875}, {'CRRA': 3.6875, 'BeqShift': 32.8125, 'BeqFac': 43.4375}, {'CRRA': 5.9375, 'BeqShift': 59.0625, 'BeqFac': 24.6875}, {'CRRA': 3.125, 'BeqShift': 65.625, 'BeqFac': 48.125}, {'CRRA': 6.5, 'BeqShift': 52.5, 'BeqFac': 57.5}, {'CRRA': 7.0625, 'BeqShift': 19.6875, 'BeqFac': 27.8125}, {'CRRA': 7.625, 'BeqShift': 13.125, 'BeqFac': 35.625}, {'CRRA': 8.1875, 'BeqShift': 50.3125, 'BeqFac': 55.9375}, {'CRRA': 8.75, 'BeqShift': 26.25, 'BeqFac': 51.25}, {'CRRA': 9.3125, 'BeqShift': 63.4375, 'BeqFac': 34.0625}, {'CRRA': 9.875, 'BeqShift': 39.375, 'BeqFac': 29.375}, {'CRRA': 10.4375, 'BeqShift': 6.5625, 'BeqFac': 62.1875}, {'CRRA': 11.0, 'BeqShift': 35.0, 'BeqFac': 45.0}, {'CRRA': 12.125, 'BeqShift': 30.625, 'BeqFac': 23.125}, {'CRRA': 12.6875, 'BeqShift': 67.8125, 'BeqFac': 68.4375}, {'CRRA': 13.25, 'BeqShift': 8.75, 'BeqFac': 63.75}, {'CRRA': 13.8125, 'BeqShift': 45.9375, 'BeqFac': 21.5625}, {'CRRA': 14.375, 'BeqShift': 56.875, 'BeqFac': 41.875}, {'CRRA': 14.9375, 'BeqShift': 24.0625, 'BeqFac': 49.6875}, {'CRRA': 15.5, 'BeqShift': 17.5, 'BeqFac': 32.5}, {'CRRA': 16.0625, 'BeqShift': 54.6875, 'BeqFac': 52.8125}, {'CRRA': 16.625, 'BeqShift': 48.125, 'BeqFac': 60.625}, {'CRRA': 17.1875, 'BeqShift': 15.3125, 'BeqFac': 30.9375}, {'CRRA': 17.75, 'BeqShift': 61.25, 'BeqFac': 26.25}, {'CRRA': 18.3125, 'BeqShift': 28.4375, 'BeqFac': 59.0625}, {'CRRA': 18.875, 'BeqShift': 4.375, 'BeqFac': 54.375}, {'CRRA': 19.4375, 'BeqShift': 41.5625, 'BeqFac': 37.1875}], 'exploration_results': array([  7.81310671,   8.04234718,   8.48661498,   8.95471279,
          9.52815641,  10.27586568,  12.00230193,  12.3747888 ,
         16.07272175,  20.10794912,  23.5871464 ,  29.56480172,
         34.04180187,  40.97293748,  51.53022936,  55.11303949,
@@ -56,12 +56,12 @@ absolute_params_change         1e-06*      0.0176
        110.50500968, 115.56550732, 125.07154219, 143.19028124,
        154.89677554, 155.61803332, 182.42573074, 185.61349462,
        196.97106057, 216.83078809])}"
-algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881, 16.44990466, 46.2434204 ]), radius=4.624342040168151, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=8.145256723269389, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.],
+algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.70567639, 16.96622308, 46.46531825]), radius=4.646531825239875, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=8.140218982858876, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.],
        [0., 0., 0.],
-       [0., 0., 0.]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+       [0., 0., 0.]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -143,12 +143,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=0, candidate_x=array([ 4.49799881, 16.44990466, 46.2434204 ]), index=0, x=array([ 4.49799881, 16.44990466, 46.2434204 ]), fval=8.145256723269389, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.49799881, 16.44990466, 46.2434204 ]), radius=4.624342040168151, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=15.006948940519543, linear_terms=array([0.01323854, 1.10352104, 2.39437648]), square_terms=array([[7.03892333e+00, 8.57793675e-02, 4.17471910e-03],
-       [8.57793675e-02, 4.23198942e-02, 8.86295613e-02],
-       [4.17471910e-03, 8.86295613e-02, 1.91682780e-01]]), scale=array([3.11259995, 3.72720108, 3.72720108]), shift=array([ 5.11259995, 16.44990466, 46.2434204 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=0, candidate_x=array([ 4.70567639, 16.96622308, 46.46531825]), index=0, x=array([ 4.70567639, 16.96622308, 46.46531825]), fval=8.140218982858874, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.70567639, 16.96622308, 46.46531825]), radius=4.646531825239875, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=16.490411097157534, linear_terms=array([-0.03818026,  0.33176217,  2.4502688 ]), square_terms=array([[ 7.36404859e+00,  1.27470230e-02, -2.48061738e-02],
+       [ 1.27470230e-02,  3.38443075e-03,  2.47121766e-02],
+       [-2.48061738e-02,  2.47121766e-02,  1.82734843e-01]]), scale=array([3.22538118, 3.74508596, 3.74508596]), shift=array([ 5.22538118, 16.96622308, 46.46531825])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -230,12 +230,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=13, candidate_x=array([ 5.14652343, 12.72270358, 42.51621932]), index=0, x=array([ 4.49799881, 16.44990466, 46.2434204 ]), fval=8.145256723269389, rho=-0.060946909275941985, accepted=False, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.49799881, 16.44990466, 46.2434204 ]), radius=2.3121710200840755, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  3,  4,  5,  7,  8,  9, 10, 11, 12, 13]), model=ScalarModel(intercept=14.580432746581428, linear_terms=array([-1.35115423,  1.10781698,  1.4968029 ]), square_terms=array([[ 3.86103295,  0.02862604, -0.02446957],
-       [ 0.02862604,  0.04424158,  0.05807645],
-       [-0.02446957,  0.05807645,  0.07760107]]), scale=2.3121710200840755, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=13, candidate_x=array([ 5.23682197, 13.22113712, 42.72023229]), index=0, x=array([ 4.70567639, 16.96622308, 46.46531825]), fval=8.140218982858874, rho=-0.12699405829214627, accepted=False, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70567639, 16.96622308, 46.46531825]), radius=2.3232659126199375, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13]), model=ScalarModel(intercept=15.299412767135175, linear_terms=array([-1.79046808,  1.42748659,  1.30013449]), square_terms=array([[ 3.81432883, -0.01997852, -0.05904408],
+       [-0.01997852,  0.06821013,  0.06117938],
+       [-0.05904408,  0.06117938,  0.05550258]]), scale=2.3232659126199375, shift=array([ 4.70567639, 16.96622308, 46.46531825])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -317,12 +317,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=14, candidate_x=array([ 5.06004155, 15.0250227 , 44.33684394]), index=0, x=array([ 4.49799881, 16.44990466, 46.2434204 ]), fval=8.145256723269389, rho=-0.21388051286164697, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  3,  4,  5,  7,  8,  9, 10, 11, 12, 13]), old_indices_discarded=array([2, 6]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.49799881, 16.44990466, 46.2434204 ]), radius=1.1560855100420377, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  3,  4,  5,  7,  8,  9, 10, 11, 13, 14]), model=ScalarModel(intercept=13.338681860248593, linear_terms=array([-1.60670822,  0.9508896 ,  0.29728755]), square_terms=array([[ 0.98966811, -0.02693823, -0.02007844],
-       [-0.02693823,  0.03530776,  0.01051071],
-       [-0.02007844,  0.01051071,  0.00333429]]), scale=1.1560855100420377, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=14, candidate_x=array([ 5.43402295, 15.17155435, 44.84689792]), index=0, x=array([ 4.70567639, 16.96622308, 46.46531825]), fval=8.140218982858874, rho=-0.4964514456620586, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13]), old_indices_discarded=array([ 2, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70567639, 16.96622308, 46.46531825]), radius=1.1616329563099688, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  4,  5,  7,  8,  9, 10, 11, 13, 14]), model=ScalarModel(intercept=13.053662317110621, linear_terms=array([-2.55034397,  0.43395241, -0.23182703]), square_terms=array([[ 1.1067579 , -0.03430609,  0.00962644],
+       [-0.03430609,  0.00735824, -0.00405499],
+       [ 0.00962644, -0.00405499,  0.00237418]]), scale=1.1616329563099688, shift=array([ 4.70567639, 16.96622308, 46.46531825])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -404,12 +404,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=15, candidate_x=array([ 5.28835652, 15.64432349, 45.99260695]), index=0, x=array([ 4.49799881, 16.44990466, 46.2434204 ]), fval=8.145256723269389, rho=-0.30102023579118353, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  3,  4,  5,  7,  8,  9, 10, 11, 13, 14]), old_indices_discarded=array([ 2, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.49799881, 16.44990466, 46.2434204 ]), radius=0.5780427550210189, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=8.024369229911517, linear_terms=array([-0.15788203, -0.11944546, -0.05215894]), square_terms=array([[ 0.313711  , -0.00880867, -0.00850489],
-       [-0.00880867,  0.00133093,  0.00075035],
-       [-0.00850489,  0.00075035,  0.00050391]]), scale=0.5780427550210189, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=15, candidate_x=array([ 5.85458923, 17.02330601, 46.30366444]), index=0, x=array([ 4.70567639, 16.96622308, 46.46531825]), fval=8.140218982858874, rho=-0.9592394226356723, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  4,  5,  7,  8,  9, 10, 11, 13, 14]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70567639, 16.96622308, 46.46531825]), radius=0.5808164781549844, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=8.005185836487234, linear_terms=array([ 0.36477503, -0.04619487, -0.08305498]), square_terms=array([[ 0.33740382, -0.00962398, -0.01068073],
+       [-0.00962398,  0.00041438,  0.00052782],
+       [-0.01068073,  0.00052782,  0.00073221]]), scale=0.5808164781549844, shift=array([ 4.70567639, 16.96622308, 46.46531825])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -491,12 +491,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=28, candidate_x=array([ 4.7160682 , 16.96957784, 46.47692321]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=1.748632163549693, accepted=True, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.6100311757687438, relative_step_length=1.0553391950160533, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=1.1560855100420377, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=7.888984808494836, linear_terms=array([-0.34386436, -0.05194838, -0.02312738]), square_terms=array([[ 1.21657627e+00, -4.24486844e-03, -1.44110029e-03],
-       [-4.24486844e-03,  2.03874438e-04,  8.44861153e-05],
-       [-1.44110029e-03,  8.44861153e-05,  3.99633748e-05]]), scale=1.1560855100420377, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=28, candidate_x=array([ 4.25725028, 17.1474243 , 46.81622412]), index=28, x=array([ 4.25725028, 17.1474243 , 46.81622412]), fval=8.066657471859852, rho=0.3089444772758513, accepted=True, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.5975406126185681, relative_step_length=1.0287941804211709, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.25725028, 17.1474243 , 46.81622412]), radius=1.1616329563099688, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=7.972477076287903, linear_terms=array([-0.30891814, -0.0205982 ,  0.02489372]), square_terms=array([[ 1.24232601e+00, -1.97645441e-03,  7.55419362e-04],
+       [-1.97645441e-03,  3.29059590e-05, -3.46870075e-05],
+       [ 7.55419362e-04, -3.46870075e-05,  4.26055898e-05]]), scale=1.1616329563099688, shift=array([ 4.25725028, 17.1474243 , 46.81622412])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -578,12 +578,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=29, candidate_x=array([ 5.0320216 , 18.02477148, 46.94434338]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-7.24606288417893, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 23, 25, 26, 27, 28]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 14, 15, 22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.5780427550210189, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 28]), model=ScalarModel(intercept=7.86487963078101, linear_terms=array([-0.16654267, -0.09029353,  0.00243157]), square_terms=array([[ 3.03961339e-01, -3.19690397e-03, -2.41147826e-04],
-       [-3.19690397e-03,  5.96151742e-04, -1.14490562e-05],
-       [-2.41147826e-04, -1.14490562e-05,  8.93603588e-07]]), scale=0.5780427550210189, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=29, candidate_x=array([ 4.54026495, 17.88507428, 45.93860604]), index=28, x=array([ 4.25725028, 17.1474243 , 46.81622412]), fval=8.066657471859852, rho=-3.91761543536851, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1,  4,  5,  7,  8, 10, 11, 13, 14, 15, 17, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.25725028, 17.1474243 , 46.81622412]), radius=0.5808164781549844, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28]), model=ScalarModel(intercept=8.029837216385943, linear_terms=array([-0.21170276, -0.00943844,  0.02072123]), square_terms=array([[ 3.15125217e-01, -7.20879321e-04, -2.34436529e-03],
+       [-7.20879321e-04,  8.96320181e-06, -1.98856116e-06],
+       [-2.34436529e-03, -1.98856116e-06,  6.54857806e-05]]), scale=0.5808164781549844, shift=array([ 4.25725028, 17.1474243 , 46.81622412])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -665,12 +665,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=30, candidate_x=array([ 4.9641153 , 17.54464667, 46.46229469]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-1.4644299520714406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 28]), old_indices_discarded=array([15, 24, 26, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.28902137751050944, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 22, 23, 25, 27, 28, 30]), model=ScalarModel(intercept=7.941661553246127, linear_terms=array([-0.07751077, -0.00136076, -0.00569786]), square_terms=array([[ 7.57663178e-02, -6.33754053e-04, -3.46700316e-04],
-       [-6.33754053e-04,  8.19469644e-05,  1.60473808e-05],
-       [-3.46700316e-04,  1.60473808e-05,  6.25046163e-06]]), scale=0.28902137751050944, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=30, candidate_x=array([ 4.61419506, 17.35931809, 46.40359963]), index=30, x=array([ 4.61419506, 17.35931809, 46.40359963]), fval=7.902897717558958, rho=1.8634535269130141, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28]), old_indices_discarded=array([15, 16, 27, 29]), step_length=0.5852926755287875, relative_step_length=1.00770673274977, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.61419506, 17.35931809, 46.40359963]), radius=1.1616329563099688, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([16, 17, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=8.04777209526033, linear_terms=array([ 0.01960014,  0.2358811 , -0.06678174]), square_terms=array([[ 1.2126301 ,  0.06254486, -0.03042362],
+       [ 0.06254486,  0.01034081, -0.00401734],
+       [-0.03042362, -0.00401734,  0.00165776]]), scale=1.1616329563099688, shift=array([ 4.61419506, 17.35931809, 46.40359963])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -752,12 +752,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=31, candidate_x=array([ 4.97107558, 17.01391205, 46.61691596]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-11.111797991447878, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 22, 23, 25, 27, 28, 30]), old_indices_discarded=array([21, 24, 26, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.14451068875525472, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 19, 22, 23, 25, 28, 30, 31]), model=ScalarModel(intercept=7.978558495501097, linear_terms=array([ 0.02652198, -0.00028642,  0.00624314]), square_terms=array([[ 2.05648441e-02, -5.42557035e-04,  2.51148716e-04],
-       [-5.42557035e-04,  4.09091464e-05, -7.41486358e-06],
-       [ 2.51148716e-04, -7.41486358e-06,  8.64371733e-06]]), scale=0.14451068875525472, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=31, candidate_x=array([ 4.65344865, 16.2390077 , 46.71701767]), index=30, x=array([ 4.61419506, 17.35931809, 46.40359963]), fval=7.902897717558958, rho=-0.9919738918629465, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 17, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([ 0,  1,  4,  5,  7,  8,  9, 10, 11, 13, 14, 15, 18, 19, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.61419506, 17.35931809, 46.40359963]), radius=0.5808164781549844, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 22, 23, 25, 28, 29, 30]), model=ScalarModel(intercept=8.007846882803051, linear_terms=array([-0.0556707 ,  0.07919637, -0.05762226]), square_terms=array([[ 0.30197476,  0.0146575 , -0.00818423],
+       [ 0.0146575 ,  0.0019494 , -0.0010771 ],
+       [-0.00818423, -0.0010771 ,  0.00061556]]), scale=0.5808164781549844, shift=array([ 4.61419506, 17.35931809, 46.40359963])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -839,12 +839,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=32, candidate_x=array([ 4.62004715, 17.00587342, 46.37520828]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-4.4393778330120055, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 19, 22, 23, 25, 28, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.07225534437762736, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([22, 28, 31, 32]), model=ScalarModel(intercept=7.849521073607839, linear_terms=array([ 0.27833608,  0.0261772 , -0.30529537]), square_terms=array([[ 0.02270853,  0.00153107, -0.01599417],
-       [ 0.00153107,  0.00013776, -0.00100599],
-       [-0.01599417, -0.00100599,  0.01422192]]), scale=0.07225534437762736, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=32, candidate_x=array([ 4.71868512, 16.8935651 , 46.74203922]), index=30, x=array([ 4.61419506, 17.35931809, 46.40359963]), fval=7.902897717558958, rho=-0.33903410676392864, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 21, 22, 23, 25, 28, 29, 30]), old_indices_discarded=array([14, 15, 20, 24, 26, 27, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.61419506, 17.35931809, 46.40359963]), radius=0.2904082390774922, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 18, 19, 21, 22, 23, 25, 28, 29, 30, 32]), model=ScalarModel(intercept=8.008165668299897, linear_terms=array([-0.02148056,  0.04374795, -0.02926837]), square_terms=array([[ 0.07668834,  0.00382458, -0.00203623],
+       [ 0.00382458,  0.00047944, -0.00025807],
+       [-0.00203623, -0.00025807,  0.00014286]]), scale=0.2904082390774922, shift=array([ 4.61419506, 17.35931809, 46.40359963])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -926,12 +926,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=33, candidate_x=array([ 4.67868592, 16.95650298, 46.5373587 ]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-0.3501719214659062, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 28, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.03612767218881368, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([22, 28, 32, 33]), model=ScalarModel(intercept=7.849521073607844, linear_terms=array([-0.07066411, -0.03896864,  0.02686239]), square_terms=array([[ 0.00240414,  0.00059596, -0.00085165],
-       [ 0.00059596,  0.00034167, -0.00034783],
-       [-0.00085165, -0.00034783,  0.00056658]]), scale=0.03612767218881368, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=33, candidate_x=array([ 4.67166491, 17.12030492, 46.5633123 ]), index=30, x=array([ 4.61419506, 17.35931809, 46.40359963]), fval=7.902897717558958, rho=-1.4406835601923031, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 18, 19, 21, 22, 23, 25, 28, 29, 30, 32]), old_indices_discarded=array([15, 17, 20, 24, 26, 27, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.61419506, 17.35931809, 46.40359963]), radius=0.1452041195387461, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 18, 19, 21, 22, 23, 28, 29, 30, 32, 33]), model=ScalarModel(intercept=8.013230759011146, linear_terms=array([-0.00958804,  0.02117261, -0.01053588]), square_terms=array([[ 1.91446579e-02,  9.57451186e-04, -5.80541252e-04],
+       [ 9.57451186e-04,  1.08214274e-04, -5.56889234e-05],
+       [-5.80541252e-04, -5.56889234e-05,  3.00777236e-05]]), scale=0.1452041195387461, shift=array([ 4.61419506, 17.35931809, 46.40359963])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1013,12 +1013,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=34, candidate_x=array([ 4.74894929, 16.98248775, 46.46934849]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-3.972641796941464, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 28, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.01806383609440684, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([28, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=7.7722491279935175, linear_terms=array([0.05933982, 0.02513362, 0.06787715]), square_terms=array([[0.0018065 , 0.000499  , 0.00123775],
-       [0.000499  , 0.00014611, 0.00036662],
-       [0.00123775, 0.00036662, 0.00094791]]), scale=0.01806383609440684, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=34, candidate_x=array([ 4.64974776, 17.23288482, 46.46665088]), index=30, x=array([ 4.61419506, 17.35931809, 46.40359963]), fval=7.902897717558958, rho=-10.042100518414895, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 18, 19, 21, 22, 23, 28, 29, 30, 32, 33]), old_indices_discarded=array([25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.61419506, 17.35931809, 46.40359963]), radius=0.07260205976937305, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([19, 23, 30, 33, 34]), model=ScalarModel(intercept=7.9535751109453, linear_terms=array([-0.10600093, -0.06484184, -0.01057826]), square_terms=array([[0.02315565, 0.00490555, 0.00184373],
+       [0.00490555, 0.00196284, 0.00056369],
+       [0.00184373, 0.00056369, 0.00019232]]), scale=0.07260205976937305, shift=array([ 4.61419506, 17.35931809, 46.40359963])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1100,12 +1100,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=47, candidate_x=array([ 4.70561459, 16.96407505, 46.4632578 ]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=1.3592037741862977, accepted=True, new_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_used=array([28, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.018063836094405265, relative_step_length=0.9999999999999128, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.03612767218881368, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([28, 35, 36, 37, 39, 40, 41, 42, 43, 45, 46, 47]), model=ScalarModel(intercept=7.733977086304757, linear_terms=array([-0.00031022,  0.0001404 ,  0.00211586]), square_terms=array([[1.11066890e-03, 1.32662670e-05, 3.33650815e-05],
-       [1.32662670e-05, 6.83876882e-07, 1.16854914e-06],
-       [3.33650815e-05, 1.16854914e-06, 2.61595057e-06]]), scale=0.03612767218881368, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=35, candidate_x=array([ 4.68234791, 17.38247432, 46.39411084]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=1.6587128458247886, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 30, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.07260205976937305, relative_step_length=1.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.1452041195387461, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 18, 19, 22, 23, 28, 30, 32, 33, 34, 35]), model=ScalarModel(intercept=7.9034674515792265, linear_terms=array([-0.0020679 , -0.05545576, -0.00111844]), square_terms=array([[ 1.95785478e-02, -1.81540373e-03,  5.46280827e-05],
+       [-1.81540373e-03,  4.22105484e-04, -1.10012082e-05],
+       [ 5.46280827e-05, -1.10012082e-05,  6.47099121e-06]]), scale=0.1452041195387461, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1187,12 +1187,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=48, candidate_x=array([ 4.7094481 , 16.96169381, 46.42740856]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-198.231940320406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 35, 36, 37, 39, 40, 41, 42, 43, 45, 46, 47]), old_indices_discarded=array([32, 33, 34, 38, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.01806383609440684, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([28, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 47]), model=ScalarModel(intercept=7.726839188660502, linear_terms=array([0.00879896, 0.00526178, 0.00582683]), square_terms=array([[5.51747809e-04, 1.10445807e-04, 1.13316640e-04],
-       [1.10445807e-04, 3.27458616e-05, 3.33962352e-05],
-       [1.13316640e-04, 3.33962352e-05, 3.41224344e-05]]), scale=0.01806383609440684, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=36, candidate_x=array([ 4.68987942, 17.52745381, 46.39707325]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-7.520985100519123, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 18, 19, 22, 23, 28, 30, 32, 33, 34, 35]), old_indices_discarded=array([21, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.07260205976937305, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([19, 23, 30, 33, 34, 35, 36]), model=ScalarModel(intercept=7.975131384361624, linear_terms=array([ 0.10245776, -0.00847563,  0.01481371]), square_terms=array([[ 9.61461860e-03, -3.05900830e-04,  5.31345067e-04],
+       [-3.05900830e-04,  1.17098733e-04, -1.85801690e-05],
+       [ 5.31345067e-04, -1.85801690e-05,  4.31818780e-05]]), scale=0.07260205976937305, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1274,12 +1274,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=49, candidate_x=array([ 4.69366858, 16.9549441 , 46.45324676]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-30.45775065724263, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 47]), old_indices_discarded=array([33, 34, 37, 46, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.00903191804720342, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([28, 35, 36, 38, 39, 40, 41, 42, 44, 45, 47, 49]), model=ScalarModel(intercept=7.804413499313456, linear_terms=array([-0.03091731, -0.02194317, -0.02545883]), square_terms=array([[8.40899570e-05, 7.03887114e-05, 7.94924297e-05],
-       [7.03887114e-05, 8.76474355e-05, 1.00200482e-04],
-       [7.94924297e-05, 1.00200482e-04, 1.15689861e-04]]), scale=0.00903191804720342, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=37, candidate_x=array([ 4.61001961, 17.38408728, 46.38802192]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-3.52091474572316, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 30, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.036301029884686524, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([30, 34, 35, 36, 37]), model=ScalarModel(intercept=7.736319471622591, linear_terms=array([-0.13924985,  0.09569219,  0.32447151]), square_terms=array([[ 0.00207511, -0.0019659 , -0.00635419],
+       [-0.0019659 ,  0.00241474,  0.00784056],
+       [-0.00635419,  0.00784056,  0.02582089]]), scale=0.036301029884686524, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1361,12 +1361,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=50, candidate_x=array([ 4.71175707, 16.96835254, 46.46831236]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-4.664413684425712, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 35, 36, 38, 39, 40, 41, 42, 44, 45, 47, 49]), old_indices_discarded=array([37, 43, 46, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.00451595902360171, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([28, 35, 38, 39, 41, 42, 44, 47, 49, 50]), model=ScalarModel(intercept=7.819759695884859, linear_terms=array([-0.0184351 , -0.00690095, -0.01815452]), square_terms=array([[3.22753081e-05, 1.18663247e-05, 3.36580092e-05],
-       [1.18663247e-05, 1.84364654e-05, 3.39520031e-05],
-       [3.36580092e-05, 3.39520031e-05, 7.21619318e-05]]), scale=0.00451595902360171, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=38, candidate_x=array([ 4.69156463, 17.37787165, 46.35930233]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-0.6007840565845133, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([30, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.018150514942343262, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([30, 35, 37, 38]), model=ScalarModel(intercept=7.7249497274541925, linear_terms=array([-0.07424609,  0.02850284, -0.12615741]), square_terms=array([[ 0.00056555, -0.00017033,  0.00138401],
+       [-0.00017033,  0.00054861,  0.00033325],
+       [ 0.00138401,  0.00033325,  0.00586802]]), scale=0.018150514942343262, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1448,12 +1448,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=51, candidate_x=array([ 4.70872878, 16.96522582, 46.46631908]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-9.750702226133074, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 35, 38, 39, 41, 42, 44, 47, 49, 50]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.002257979511800855, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([38, 44, 47, 50, 51]), model=ScalarModel(intercept=7.711709399926576, linear_terms=array([-0.04491751, -0.19266887,  0.31333725]), square_terms=array([[ 0.00074431,  0.00211792, -0.00257367],
-       [ 0.00211792,  0.00796962, -0.01179223],
-       [-0.00257367, -0.01179223,  0.01919665]]), scale=0.002257979511800855, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=39, candidate_x=array([ 4.69077163, 17.37833807, 46.40964704]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-1.7832014670167953, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([30, 35, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.009075257471171631, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=8.235548908421132, linear_terms=array([-0.0775429 ,  0.03941567,  0.02310366]), square_terms=array([[ 0.00085966, -0.00051305, -0.0001847 ],
+       [-0.00051305,  0.00031468,  0.0001103 ],
+       [-0.0001847 ,  0.0001103 ,  0.00013357]]), scale=0.009075257471171631, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1535,12 +1535,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=52, candidate_x=array([ 4.70605376, 16.96501762, 46.46125352]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.670182060224612, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([38, 44, 47, 50, 51]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.0011289897559004275, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([44, 47, 51, 52]), model=ScalarModel(intercept=7.724404600954944, linear_terms=array([ 0.15706182,  0.25063012, -0.16051657]), square_terms=array([[ 0.00559721,  0.00893029, -0.00582589],
-       [ 0.00893029,  0.01429041, -0.00937304],
-       [-0.00582589, -0.00937304,  0.00620876]]), scale=0.0011289897559004275, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=52, candidate_x=array([ 4.69044162, 17.37887834, 46.39213068]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-6.580762395292093, accepted=False, new_indices=array([40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), old_indices_used=array([35, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.0045376287355858155, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52]), model=ScalarModel(intercept=8.256636552922478, linear_terms=array([ 0.00561754, -0.00241631, -0.00535758]), square_terms=array([[ 3.60721443e-05, -6.02291934e-06, -1.26800788e-05],
+       [-6.02291934e-06,  1.19018933e-06,  2.52528763e-06],
+       [-1.26800788e-05,  2.52528763e-06,  5.38340499e-06]]), scale=0.0045376287355858155, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1622,12 +1622,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=53, candidate_x=array([ 4.70496849, 16.96342162, 46.46391371]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.1476151112107937, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([44, 47, 51, 52]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.0005644948779502137, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=8.032744143893979, linear_terms=array([-0.02352272,  0.01076142, -0.05482852]), square_terms=array([[ 1.60641111e-04, -4.64426692e-06,  2.64479134e-04],
-       [-4.64426692e-06,  6.08149184e-05, -1.26242645e-04],
-       [ 2.64479134e-04, -1.26242645e-04,  6.86685396e-04]]), scale=0.0005644948779502137, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=53, candidate_x=array([ 4.67926273, 17.38385896, 46.39713648]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-44.386134325605916, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52]), old_indices_discarded=array([39, 43, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.0022688143677929077, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 40, 42, 44, 45, 46, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=8.231039150006058, linear_terms=array([ 0.00910109, -0.00965067, -0.01320036]), square_terms=array([[ 3.41638836e-05, -2.54257345e-05, -3.55308250e-05],
+       [-2.54257345e-05,  1.97403092e-05,  2.76469541e-05],
+       [-3.55308250e-05,  2.76469541e-05,  3.90654416e-05]]), scale=0.0022688143677929077, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1709,12 +1709,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=66, candidate_x=array([ 4.70582045, 16.96398708, 46.46377601]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-5.348742433713813, accepted=False, new_indices=array([54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), old_indices_used=array([47, 52, 53]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.00028224743897510687, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 54, 55, 56, 57, 58, 59, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=8.002354293534912, linear_terms=array([-0.01609135, -0.00394256,  0.01908672]), square_terms=array([[ 4.98645298e-05,  1.25322573e-05, -6.07331796e-05],
-       [ 1.25322573e-05,  3.15154476e-06, -1.52731335e-05],
-       [-6.07331796e-05, -1.52731335e-05,  7.40179510e-05]]), scale=0.00028224743897510687, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=54, candidate_x=array([ 4.68125936, 17.38365099, 46.39571646]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-16.968936842314275, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 40, 42, 44, 45, 46, 47, 49, 50, 51, 52, 53]), old_indices_discarded=array([41, 43, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.0011344071838964539, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=7.811900251844641, linear_terms=array([-0.0342071 ,  0.03065072,  0.04523505]), square_terms=array([[ 0.00027609, -0.00024653, -0.00037284],
+       [-0.00024653,  0.00023121,  0.00034213],
+       [-0.00037284,  0.00034213,  0.0005113 ]]), scale=0.0011344071838964539, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1796,12 +1796,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=67, candidate_x=array([ 4.70579084, 16.96412186, 46.46304238]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-3.39343113052912, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 54, 55, 56, 57, 58, 59, 62, 63, 64, 65, 66]), old_indices_discarded=array([53, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.00014112371948755344, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 54, 55, 56, 57, 58, 62, 63, 64, 65, 66, 67]), model=ScalarModel(intercept=7.940414587718634, linear_terms=array([-0.02084255,  0.00402229,  0.03057654]), square_terms=array([[ 9.17349633e-05, -1.44372171e-05, -1.33718159e-04],
-       [-1.44372171e-05,  4.64572232e-06,  2.26408140e-05],
-       [-1.33718159e-04,  2.26408140e-05,  1.96000407e-04]]), scale=0.00014112371948755344, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=67, candidate_x=array([ 4.6829256 , 17.38192261, 46.39330538]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-1.3188906121302586, accepted=False, new_indices=array([55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), old_indices_used=array([35, 53, 54]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.0005672035919482269, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 55, 56, 57, 58, 59, 60, 62, 63, 65, 66, 67]), model=ScalarModel(intercept=7.80185769091568, linear_terms=array([ 6.86991386e-04, -5.16063971e-05,  8.42278729e-04]), square_terms=array([[ 7.64487179e-07, -2.73353872e-08,  4.77961648e-07],
+       [-2.73353872e-08,  1.45372181e-09, -2.56082670e-08],
+       [ 4.77961648e-07, -2.56082670e-08,  4.53272066e-07]]), scale=0.0005672035919482269, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1883,12 +1883,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=68, candidate_x=array([ 4.70569155, 16.96405815, 46.46314072]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-11.301558821142645, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 54, 55, 56, 57, 58, 62, 63, 64, 65, 66, 67]), old_indices_discarded=array([59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=7.056185974377672e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=8.106239946117373, linear_terms=array([-0.03451123, -0.02301713,  0.03291206]), square_terms=array([[ 0.00026391,  0.00018275, -0.00024859],
-       [ 0.00018275,  0.00014313, -0.00016172],
-       [-0.00024859, -0.00016172,  0.0002407 ]]), scale=7.056185974377672e-05, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=68, candidate_x=array([ 4.68199145, 17.38250259, 46.39367054]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-381.70649331838456, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 55, 56, 57, 58, 59, 60, 62, 63, 65, 66, 67]), old_indices_discarded=array([54, 61, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.00028360179597411347, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 55, 56, 57, 58, 59, 60, 62, 63, 66, 67, 68]), model=ScalarModel(intercept=7.832501858210558, linear_terms=array([-0.00934396, -0.00257555, -0.00907166]), square_terms=array([[2.50062911e-05, 7.51022956e-06, 2.43754087e-05],
+       [7.51022956e-06, 2.28737337e-06, 7.24151065e-06],
+       [2.43754087e-05, 7.24151065e-06, 2.40319016e-05]]), scale=0.00028360179597411347, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -1970,12 +1970,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=81, candidate_x=array([ 4.70565894, 16.96410674, 46.463213  ]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-7.878855790285914, accepted=False, new_indices=array([69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), old_indices_used=array([47, 67, 68]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=3.528092987188836e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=8.09711787584544, linear_terms=array([0.00382197, 0.01486028, 0.0062441 ]), square_terms=array([[3.08967374e-06, 1.18500122e-05, 4.97922821e-06],
-       [1.18500122e-05, 4.54565210e-05, 1.91002667e-05],
-       [4.97922821e-06, 1.91002667e-05, 8.02569531e-06]]), scale=3.528092987188836e-05, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=69, candidate_x=array([ 4.68254351, 17.38253273, 46.39430771]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-31.00850079815674, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 55, 56, 57, 58, 59, 60, 62, 63, 66, 67, 68]), old_indices_discarded=array([61, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.00014180089798705673, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=8.169210918272597, linear_terms=array([-0.00038306, -0.00507427,  0.00257826]), square_terms=array([[ 1.03451998e-05,  2.11956146e-06,  1.08038467e-05],
+       [ 2.11956146e-06,  2.71695453e-05, -1.40435993e-05],
+       [ 1.08038467e-05, -1.40435993e-05,  2.11952211e-05]]), scale=0.00014180089798705673, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2057,12 +2057,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=82, candidate_x=array([ 4.70560673, 16.96404321, 46.46324479]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-29.942088486440817, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81]), old_indices_discarded=array([68, 69, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1.764046493594418e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 70, 71, 74, 75, 76, 77, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=8.107261374316284, linear_terms=array([ 0.00122538,  0.00206624, -0.00252862]), square_terms=array([[ 1.06921156e-06,  3.42422311e-06, -1.16687573e-07],
-       [ 3.42422311e-06,  1.19607616e-05,  8.54719078e-07],
-       [-1.16687573e-07,  8.54719078e-07,  1.53200886e-06]]), scale=1.764046493594418e-05, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=82, candidate_x=array([ 4.68235644, 17.38260121, 46.39404813]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-85.81986855232132, accepted=False, new_indices=array([70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), old_indices_used=array([35, 68, 69]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=7.090044899352837e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=8.16956317384026, linear_terms=array([0.00048644, 0.00339551, 0.00475271]), square_terms=array([[6.68334479e-08, 3.76265470e-07, 5.26634881e-07],
+       [3.76265470e-07, 2.20344328e-06, 3.08400236e-06],
+       [5.26634881e-07, 3.08400236e-06, 4.31645810e-06]]), scale=7.090044899352837e-05, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2144,12 +2144,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=83, candidate_x=array([ 4.7056084 , 16.96406461, 46.46327061]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-66.07341500231269, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 70, 71, 74, 75, 76, 77, 78, 79, 80, 81, 82]), old_indices_discarded=array([69, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=8.82023246797209e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]), model=ScalarModel(intercept=8.027414199827243, linear_terms=array([-0.00102897, -0.02419561, -0.03622744]), square_terms=array([[ 2.42552676e-05,  5.38177718e-05, -2.53591858e-05],
-       [ 5.38177718e-05,  2.01438379e-04,  8.33396860e-05],
-       [-2.53591858e-05,  8.33396860e-05,  2.64129042e-04]]), scale=8.82023246797209e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=83, candidate_x=array([ 4.68234188, 17.38243342, 46.39405324]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-38.48969680510501, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), old_indices_discarded=array([69, 70, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=3.5450224496764184e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 71, 72, 73, 74, 75, 76, 77, 78, 80, 81, 83]), model=ScalarModel(intercept=8.147554750595367, linear_terms=array([-0.00159076,  0.00417843,  0.00924053]), square_terms=array([[ 6.83291271e-07, -8.56186462e-07, -2.31058424e-06],
+       [-8.56186462e-07,  3.07588931e-06,  6.47695683e-06],
+       [-2.31058424e-06,  6.47695683e-06,  1.42187064e-05]]), scale=3.5450224496764184e-05, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2231,12 +2231,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=96, candidate_x=array([ 4.70561491, 16.96407982, 46.46326522]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-7.595394058155183, accepted=False, new_indices=array([84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]), old_indices_used=array([47, 82, 83]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=4.410116233986045e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96]), model=ScalarModel(intercept=8.024372977621736, linear_terms=array([-0.00526102,  0.00153115,  0.0022491 ]), square_terms=array([[ 5.44740785e-06, -1.58693656e-06, -2.33103709e-06],
-       [-1.58693656e-06,  4.62306298e-07,  6.79077636e-07],
-       [-2.33103709e-06,  6.79077636e-07,  9.97491140e-07]]), scale=4.410116233986045e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=84, candidate_x=array([ 4.68235345, 17.38245996, 46.3940789 ]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-32.081344014415954, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 71, 72, 73, 74, 75, 76, 77, 78, 80, 81, 83]), old_indices_discarded=array([70, 79, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1.7725112248382092e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96]), model=ScalarModel(intercept=7.9659918794540445, linear_terms=array([ 0.00776068,  0.00257546, -0.00704361]), square_terms=array([[ 2.68382961e-05,  3.03873816e-06, -3.77185131e-05],
+       [ 3.03873816e-06,  1.25203078e-05,  1.75519524e-05],
+       [-3.77185131e-05,  1.75519524e-05,  9.23763419e-05]]), scale=1.7725112248382092e-05, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2318,12 +2318,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=97, candidate_x=array([ 4.70561852, 16.96407392, 46.46325614]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-44.64935808415891, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96]), old_indices_discarded=array([83, 89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=2.2050581169930224e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 84, 85, 86, 87, 88, 91, 92, 94, 95, 96, 97]), model=ScalarModel(intercept=8.01905391324511, linear_terms=array([-0.00175287,  0.00218755,  0.00187918]), square_terms=array([[ 6.41148513e-06, -9.94820770e-07, -2.64505371e-06],
-       [-9.94820770e-07,  9.10289725e-07,  8.66893346e-07],
-       [-2.64505371e-06,  8.66893346e-07,  1.36686821e-06]]), scale=2.2050581169930224e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=97, candidate_x=array([ 4.68233547, 17.38246976, 46.39412262]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-24.03445085837565, accepted=False, new_indices=array([85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96]), old_indices_used=array([35, 83, 84]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=8.862556124191046e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97]), model=ScalarModel(intercept=7.956925176364839, linear_terms=array([-9.71305412e-03,  1.85514467e-03,  8.41055243e-07]), square_terms=array([[ 3.02586749e-05, -5.78537058e-06, -2.62289149e-09],
+       [-5.78537058e-06,  1.10614712e-06,  5.01489718e-10],
+       [-2.62289149e-09,  5.01489718e-10,  2.27358490e-13]]), scale=8.862556124191046e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2405,13 +2405,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=98, candidate_x=array([ 4.70561573, 16.96407361, 46.46325657]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-57.28762384450054, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 84, 85, 86, 87, 88, 91, 92, 94, 95, 96, 97]), old_indices_discarded=array([89, 90, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1.1025290584965112e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
-       109, 110]), model=ScalarModel(intercept=8.338585459300694, linear_terms=array([-0.05489626,  0.07386089,  0.06429901]), square_terms=array([[ 0.00047613, -0.00062578, -0.00054731],
-       [-0.00062578,  0.00094261,  0.00080316],
-       [-0.00054731,  0.00080316,  0.00068762]]), scale=1.1025290584965112e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=98, candidate_x=array([ 4.68235662, 17.38247268, 46.39411086]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-19.024215329728104, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97]), old_indices_discarded=array([84, 90, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=4.431278062095523e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 98]), model=ScalarModel(intercept=7.946525735281458, linear_terms=array([-0.00862113,  0.00513633,  0.0020363 ]), square_terms=array([[ 3.63534002e-05, -2.07381119e-05, -7.23124445e-06],
+       [-2.07381119e-05,  1.18590679e-05,  4.16715133e-06],
+       [-7.23124445e-06,  4.16715133e-06,  1.49966261e-06]]), scale=4.431278062095523e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2493,12 +2492,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=111, candidate_x=array([ 4.70561515, 16.96407436, 46.46325715]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-6.195396287396706, accepted=False, new_indices=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110]), old_indices_used=array([47, 97, 98]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109]), model=ScalarModel(intercept=8.350620664549025, linear_terms=array([-0.00247701, -0.01501306, -0.00433553]), square_terms=array([[1.08883464e-06, 6.60301599e-06, 1.90684379e-06],
-       [6.60301599e-06, 4.00426570e-05, 1.15636691e-05],
-       [1.90684379e-06, 1.15636691e-05, 3.33939985e-06]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=99, candidate_x=array([ 4.68235171, 17.38247218, 46.39411004]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-66.34297076760124, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 98]), old_indices_discarded=array([90, 92, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=2.2156390310477615e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
+       110, 111]), model=ScalarModel(intercept=7.971171404454603, linear_terms=array([ 0.01002922, -0.0552256 , -0.02915647]), square_terms=array([[ 2.61610426e-05, -1.07785362e-04, -5.62842802e-05],
+       [-1.07785362e-04,  6.06829136e-04,  3.20641457e-04],
+       [-5.62842802e-05,  3.20641457e-04,  1.69487179e-04]]), scale=2.2156390310477615e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2580,12 +2580,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=112, candidate_x=array([ 4.70561474, 16.964076  , 46.46325807]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-16.12141212547719, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109]), old_indices_discarded=array([ 97,  98, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47,  99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 112]), model=ScalarModel(intercept=8.321235798781322, linear_terms=array([-0.01138522, -0.08552521, -0.03034865]), square_terms=array([[2.27334709e-05, 1.66963695e-04, 5.98092445e-05],
-       [1.66963695e-04, 1.25141795e-03, 4.44501100e-04],
-       [5.98092445e-05, 4.44501100e-04, 1.58441604e-04]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=112, candidate_x=array([ 4.68234762, 17.3824763 , 46.39411179]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-3.9853524513265657, accepted=False, new_indices=array([100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111]), old_indices_used=array([35, 98, 99]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1.1078195155238807e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111]), model=ScalarModel(intercept=7.953158864050542, linear_terms=array([0.00129181, 0.0007209 , 0.00288726]), square_terms=array([[4.20347064e-07, 2.34393091e-07, 9.38764970e-07],
+       [2.34393091e-07, 1.30702026e-07, 5.23473123e-07],
+       [9.38764970e-07, 5.23473123e-07, 2.09655596e-06]]), scale=1.1078195155238807e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2667,12 +2667,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=113, candidate_x=array([ 4.70561469, 16.96407599, 46.46325811]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-4.523394638219533, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47,  99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 112]), old_indices_discarded=array([ 97,  98, 104, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 101, 102, 103, 105, 106, 107, 108, 109, 112, 113]), model=ScalarModel(intercept=8.300719564203227, linear_terms=array([-0.05191576, -0.12494701, -0.0261863 ]), square_terms=array([[0.00047462, 0.00114204, 0.00023985],
-       [0.00114204, 0.00279795, 0.00058626],
-       [0.00023985, 0.00058626, 0.00012289]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=113, candidate_x=array([ 4.68234747, 17.38247407, 46.39410985]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-124.72975269060092, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111]), old_indices_discarded=array([ 99, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 102, 103, 104, 105, 106, 108, 109, 111, 113]), model=ScalarModel(intercept=7.968038808575076, linear_terms=array([-0.00441212, -0.00134982, -0.00838694]), square_terms=array([[4.51612602e-06, 1.06524155e-06, 8.41507536e-06],
+       [1.06524155e-06, 6.01526003e-07, 2.17469365e-06],
+       [8.41507536e-06, 2.17469365e-06, 1.57829776e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2754,12 +2754,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=114, candidate_x=array([ 4.70561492, 16.96407598, 46.46325795]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.411275742403435, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 101, 102, 103, 105, 106, 107, 108, 109, 112, 113]), old_indices_discarded=array([ 97,  98,  99, 104, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 102, 103, 105, 106, 107, 108, 109, 112, 113, 114]), model=ScalarModel(intercept=8.298889054028262, linear_terms=array([-0.07303588, -0.18962725, -0.0359198 ]), square_terms=array([[0.00094002, 0.00243041, 0.00045976],
-       [0.00243041, 0.00638794, 0.00121604],
-       [0.00045976, 0.00121604, 0.0002323 ]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=114, candidate_x=array([ 4.68234836, 17.38247446, 46.39411171]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-20.00279553367766, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 102, 103, 104, 105, 106, 108, 109, 111, 113]), old_indices_discarded=array([ 99, 107, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 102, 104, 105, 106, 108, 109, 111, 113, 114]), model=ScalarModel(intercept=7.965989202823942, linear_terms=array([-0.00637413, -0.00182018, -0.01041058]), square_terms=array([[1.16978049e-05, 2.40445716e-06, 1.69667530e-05],
+       [2.40445716e-06, 8.05669009e-07, 4.24798314e-06],
+       [1.69667530e-05, 4.24798314e-06, 2.64842168e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2841,12 +2841,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=115, candidate_x=array([ 4.70561489, 16.96407599, 46.46325792]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.2480389552195068, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 102, 103, 105, 106, 107, 108, 109, 112, 113, 114]), old_indices_discarded=array([ 97,  98,  99, 101, 104, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 102, 105, 106, 107, 108, 109, 112, 113, 114, 115]), model=ScalarModel(intercept=8.253871939055436, linear_terms=array([-0.1554071 , -0.14943967, -0.03305483]), square_terms=array([[0.00442347, 0.00417817, 0.00091543],
-       [0.00417817, 0.00421111, 0.00092328],
-       [0.00091543, 0.00092328, 0.00020297]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=115, candidate_x=array([ 4.68234842, 17.38247447, 46.39411168]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-20.24072224820093, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 102, 104, 105, 106, 108, 109, 111, 113, 114]), old_indices_discarded=array([ 99, 103, 107, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 104, 105, 106, 108, 109, 111, 113, 114, 115]), model=ScalarModel(intercept=7.96835442879739, linear_terms=array([-0.00605559, -0.00274328, -0.01067251]), square_terms=array([[1.29695659e-05, 5.32181038e-06, 2.06392958e-05],
+       [5.32181038e-06, 2.21640855e-06, 8.48521432e-06],
+       [2.06392958e-05, 8.48521432e-06, 3.37890890e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -2928,12 +2928,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=116, candidate_x=array([ 4.70561537, 16.96407564, 46.46325803]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-2.424741745918107, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 102, 105, 106, 107, 108, 109, 112, 113, 114, 115]), old_indices_discarded=array([ 97,  98,  99, 101, 103, 104, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 102, 105, 106, 108, 109, 112, 113, 114, 115, 116]), model=ScalarModel(intercept=8.276391875287882, linear_terms=array([-0.04330989, -0.19652703, -0.06141882]), square_terms=array([[0.00036656, 0.00138004, 0.00043188],
-       [0.00138004, 0.00718237, 0.00222123],
-       [0.00043188, 0.00222123, 0.00068836]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=116, candidate_x=array([ 4.68234838, 17.38247454, 46.39411169]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-40.223470992231334, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 104, 105, 106, 108, 109, 111, 113, 114, 115]), old_indices_discarded=array([ 99, 102, 103, 107, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 105, 106, 108, 109, 111, 113, 114, 115, 116]), model=ScalarModel(intercept=7.982845846141978, linear_terms=array([ 0.00018666, -0.00062675, -0.00208488]), square_terms=array([[1.42365281e-05, 2.44192427e-06, 1.36842748e-05],
+       [2.44192427e-06, 7.83064203e-07, 2.76243560e-06],
+       [1.36842748e-05, 2.76243560e-06, 1.41995352e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3015,12 +3015,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=117, candidate_x=array([ 4.70561474, 16.964076  , 46.46325804]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.9671088996282863, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 102, 105, 106, 108, 109, 112, 113, 114, 115, 116]), old_indices_discarded=array([ 97,  98,  99, 101, 103, 104, 107, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 105, 106, 108, 109, 112, 113, 114, 115, 116, 117]), model=ScalarModel(intercept=8.272304320616975, linear_terms=array([-0.0214707 , -0.22170885, -0.07129402]), square_terms=array([[0.00025168, 0.00063019, 0.00019512],
-       [0.00063019, 0.00926462, 0.00294849],
-       [0.00019512, 0.00294849, 0.00094094]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=117, candidate_x=array([ 4.68234781, 17.3824746 , 46.39411179]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-90.69375487033876, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 105, 106, 108, 109, 111, 113, 114, 115, 116]), old_indices_discarded=array([ 99, 102, 103, 104, 107, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 106, 108, 109, 111, 113, 114, 115, 116, 117]), model=ScalarModel(intercept=7.9800824777134265, linear_terms=array([ 0.00219568, -0.0034245 , -0.00362589]), square_terms=array([[ 1.96292052e-05, -3.81765416e-06,  1.22340041e-05],
+       [-3.81765416e-06,  1.04094634e-05,  1.89526035e-06],
+       [ 1.22340041e-05,  1.89526035e-06,  1.46593321e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3102,12 +3102,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=118, candidate_x=array([ 4.70561464, 16.96407601, 46.46325806]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.2809199338412123, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 105, 106, 108, 109, 112, 113, 114, 115, 116, 117]), old_indices_discarded=array([ 97,  98,  99, 101, 102, 103, 104, 107, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 105, 108, 109, 112, 113, 114, 115, 116, 117, 118]), model=ScalarModel(intercept=8.255730261325812, linear_terms=array([ 0.16596183, -0.30916818, -0.03335069]), square_terms=array([[ 0.00640569, -0.01048573, -0.00110487],
-       [-0.01048573,  0.01848595,  0.00198261],
-       [-0.00110487,  0.00198261,  0.00021474]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=118, candidate_x=array([ 4.6823475 , 17.38247495, 46.3941115 ]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-52.79418770741774, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 106, 108, 109, 111, 113, 114, 115, 116, 117]), old_indices_discarded=array([ 99, 102, 103, 104, 105, 107, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 106, 108, 109, 113, 114, 115, 116, 117, 118]), model=ScalarModel(intercept=7.982500408551688, linear_terms=array([-0.00043775, -0.00136318, -0.00369799]), square_terms=array([[ 1.88693491e-05, -3.25666862e-06,  9.61988854e-06],
+       [-3.25666862e-06,  2.26715406e-05,  1.41331526e-05],
+       [ 9.61988854e-06,  1.41331526e-05,  1.89132554e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3189,12 +3189,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=119, candidate_x=array([ 4.70561423, 16.96407598, 46.4632578 ]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.5103600335846652, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 105, 108, 109, 112, 113, 114, 115, 116, 117, 118]), old_indices_discarded=array([ 97,  98,  99, 101, 102, 103, 104, 106, 107, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119]), model=ScalarModel(intercept=8.219426828940188, linear_terms=array([ 0.09170169, -0.27434874,  0.03583534]), square_terms=array([[ 0.00294426, -0.00564056,  0.00040588],
-       [-0.00564056,  0.0147854 , -0.00153793],
-       [ 0.00040588, -0.00153793,  0.00025172]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=119, candidate_x=array([ 4.68234801, 17.38247465, 46.39411177]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-42.94926449140449, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 106, 108, 109, 113, 114, 115, 116, 117, 118]), old_indices_discarded=array([ 99, 102, 103, 104, 105, 107, 110, 111, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 106, 108, 109, 113, 114, 115, 116, 117, 118, 119]), model=ScalarModel(intercept=7.978256870497925, linear_terms=array([-0.00463322, -0.00309187, -0.01011229]), square_terms=array([[4.57465884e-05, 7.08969940e-06, 5.12211243e-05],
+       [7.08969940e-06, 2.62402007e-05, 3.04992389e-05],
+       [5.12211243e-05, 3.04992389e-05, 8.41609013e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3276,12 +3276,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=120, candidate_x=array([ 4.70561435, 16.96407601, 46.46325774]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.9196966412231102, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119]), old_indices_discarded=array([ 97,  98,  99, 101, 102, 103, 104, 105, 106, 107, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 109, 112, 113, 114, 115, 116, 117, 118, 119, 120]), model=ScalarModel(intercept=8.179137589658655, linear_terms=array([ 0.07766055, -0.16630148, -0.03465362]), square_terms=array([[ 0.0034807 , -0.00269329, -0.00167644],
-       [-0.00269329,  0.00595065,  0.00113562],
-       [-0.00167644,  0.00113562,  0.00083026]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=120, candidate_x=array([ 4.6823483 , 17.38247459, 46.39411172]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-46.26381761495657, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 106, 108, 109, 113, 114, 115, 116, 117, 118, 119]), old_indices_discarded=array([ 99, 101, 102, 103, 104, 105, 107, 110, 111, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 106, 108, 109, 113, 114, 115, 116, 117, 118, 119, 120]), model=ScalarModel(intercept=7.991227441351442, linear_terms=array([0.0251074 , 0.01057272, 0.01015283]), square_terms=array([[1.79720390e-04, 7.01199396e-05, 9.25845703e-05],
+       [7.01199396e-05, 9.03425308e-05, 6.29529776e-05],
+       [9.25845703e-05, 6.29529776e-05, 6.34934723e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3363,12 +3363,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=121, candidate_x=array([ 4.70561423, 16.96407595, 46.46325802]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.9090746171125308, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 109, 112, 113, 114, 115, 116, 117, 118, 119, 120]), old_indices_discarded=array([ 97,  98,  99, 101, 102, 103, 104, 105, 106, 107, 108, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 109, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121]), model=ScalarModel(intercept=7.85481526704998, linear_terms=array([ 0.17790452,  0.2820454 , -0.57672785]), square_terms=array([[ 0.00972201,  0.01254853, -0.02583681],
-       [ 0.01254853,  0.01791374, -0.03587294],
-       [-0.02583681, -0.03587294,  0.07436654]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=121, candidate_x=array([ 4.68234702, 17.38247399, 46.39411052]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-11.917272451719347, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 106, 108, 109, 113, 114, 115, 116, 117, 118, 119, 120]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 107, 110, 111, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 108, 109, 113, 114, 115, 116, 117, 118, 119, 120, 121]), model=ScalarModel(intercept=8.020545402155149, linear_terms=array([ 0.02066767, -0.00112339, -0.01913257]), square_terms=array([[ 1.22236024e-04, -3.41627740e-06, -4.28275898e-05],
+       [-3.41627740e-06,  5.25927287e-05,  4.06278706e-05],
+       [-4.28275898e-05,  4.06278706e-05,  1.20355158e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3450,13 +3450,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=122, candidate_x=array([ 4.70561454, 16.96407485, 46.46325878]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.0556646767090903, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 109, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 110,
-       111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=122, candidate_x=array([ 4.68234716, 17.38247437, 46.3941115 ]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-21.65918455396445, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 108, 109, 113, 114, 115, 116, 117, 118, 119, 120, 121]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 110, 111, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 108, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=8.008375407719436, linear_terms=array([-0.08314419, -0.25217334,  0.11702306]), square_terms=array([[ 0.00163403,  0.00492151, -0.00238694],
+       [ 0.00492151,  0.01506857, -0.00732698],
+       [-0.00238694, -0.00732698,  0.00369433]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3538,13 +3537,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=123, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.6065774237517582, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=123, candidate_x=array([ 4.68234806, 17.38247527, 46.39411057]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-0.9277828593620053, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 108, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123]), model=ScalarModel(intercept=8.0146617270923, linear_terms=array([-0.10338026, -0.04297765,  0.05535851]), square_terms=array([[ 0.00191397,  0.00066374, -0.00105818],
+       [ 0.00066374,  0.00141854, -0.00068539],
+       [-0.00105818, -0.00068539,  0.00081762]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3626,13 +3624,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=124, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.9743933863682503, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=124, candidate_x=array([ 4.68234881, 17.38247457, 46.39411048]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-3.3426211322214994, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124]), model=ScalarModel(intercept=8.02552419168421, linear_terms=array([-0.02276338, -0.02839304,  0.03530554]), square_terms=array([[ 0.0003343 ,  0.0001141 , -0.00036954],
+       [ 0.0001141 ,  0.00088343, -0.00060589],
+       [-0.00036954, -0.00060589,  0.00085142]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3714,13 +3712,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=125, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.0605789137991335, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=125, candidate_x=array([ 4.68234838, 17.38247484, 46.39411013]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-8.832368290918426, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125]), model=ScalarModel(intercept=8.046662719231234, linear_terms=array([-0.01966025,  0.01855393, -0.00495825]), square_terms=array([[ 1.85723432e-04, -1.68123527e-04, -6.84389867e-05],
+       [-1.68123527e-04,  1.25579475e-03, -2.25004876e-04],
+       [-6.84389867e-05, -2.25004876e-04,  2.15172763e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3802,13 +3800,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=126, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.2600482451743429, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=126, candidate_x=array([ 4.68234865, 17.38247369, 46.39411104]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-19.468154760666586, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 126]), model=ScalarModel(intercept=8.0318646465825, linear_terms=array([ 0.15303911, -0.09657362, -0.04753304]), square_terms=array([[ 0.00580411, -0.00218947, -0.00182339],
+       [-0.00218947,  0.00152456,  0.00059628],
+       [-0.00182339,  0.00059628,  0.00074025]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3890,13 +3888,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=127, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.8501854379014441, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=127, candidate_x=array([ 4.682347  , 17.38247472, 46.39411097]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-1.9458599272870365, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 126]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 127]), model=ScalarModel(intercept=8.021829709501638, linear_terms=array([ 0.05222329,  0.02930636, -0.03390473]), square_terms=array([[ 0.00068655,  0.000606  , -0.00046092],
+       [ 0.000606  ,  0.00188856, -0.00066282],
+       [-0.00046092, -0.00066282,  0.00046994]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -3978,13 +3976,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=128, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.4056151424637772, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=128, candidate_x=array([ 4.68234714, 17.38247392, 46.39411134]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-2.467481607018985, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 127]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 128]), model=ScalarModel(intercept=8.010994524000402, linear_terms=array([ 0.12424882, -0.02277004, -0.03941254]), square_terms=array([[ 0.00301828, -0.00085483, -0.00098889],
+       [-0.00085483,  0.00101291,  0.00014894],
+       [-0.00098889,  0.00014894,  0.0005203 ]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4066,13 +4064,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=129, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.6833448065581629, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=129, candidate_x=array([ 4.68234695, 17.38247445, 46.3941111 ]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-2.1496897541972957, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 128]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4154,13 +4152,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=130, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.1693341326959497, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=130, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-5.80440127929566, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4242,13 +4240,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=131, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.618367867031028, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=131, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-8.002292328068195, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4330,13 +4328,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=132, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.22494837613365068, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=132, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-1.0706309488355799, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4418,13 +4416,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=133, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.6948332590091754, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=133, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-3.4080012643584885, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4506,13 +4504,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=134, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.5359054845285062, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=134, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-2.6154088225354815, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4594,14 +4592,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=135, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.2906252711290453, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=135, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-6.377193629110847, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4683,14 +4680,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=136, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.8876392695292357, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=136, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-4.384005717479062, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4772,14 +4768,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=137, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.0804596455483921, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135, 136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=137, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-5.337808169662223, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135,
+       136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4861,14 +4857,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=138, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.8927340827736439, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135, 136, 137]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=138, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-4.399388153248258, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135,
+       136, 137]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -4950,14 +4946,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=139, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.5511466528587335, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135, 136, 137, 138]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=139, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-2.690081752538871, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135,
+       136, 137, 138]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5039,14 +5035,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=140, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.4247850194368779, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135, 136, 137, 138, 139]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=140, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-7.067482543679271, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135,
+       136, 137, 138, 139]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5128,14 +5124,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=141, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=0.013536964494243514, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135, 136, 137, 138, 139, 140]), step_length=1.0000000029996547e-06, relative_step_length=1.0000000029996547, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 134, 135, 136, 137, 138, 139, 140, 141]), model=ScalarModel(intercept=8.06149887196976, linear_terms=array([-0.5590243 ,  1.27160275,  3.91847412]), square_terms=array([[ 0.06050115, -0.13762125, -0.42408314],
-       [-0.13762125,  0.3130454 ,  0.96465684],
-       [-0.42408314,  0.96465684,  2.97261298]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=141, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=0.05877707683680392, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135,
+       136, 137, 138, 139, 140]), step_length=1.0000000006025997e-06, relative_step_length=1.0000000006025997, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 137, 138, 139, 140, 141]), model=ScalarModel(intercept=8.08143168138139, linear_terms=array([-0.15319259,  0.06917791,  0.07202839]), square_terms=array([[ 0.00544743, -0.00145075, -0.00301805],
+       [-0.00145075,  0.00325306, -0.00105223],
+       [-0.00301805, -0.00105223,  0.00314256]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5217,14 +5213,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=142, candidate_x=array([ 4.70561441, 16.96407502, 46.46325585]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-0.18005243247469838, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 134, 135, 136, 137, 138, 139, 140, 141]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 135, 136, 137, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.164758935845795, linear_terms=array([0.10994239, 0.02402506, 0.07428419]), square_terms=array([[2.65490205e-03, 8.17471467e-05, 2.57496815e-04],
-       [8.17471467e-05, 2.02779609e-04, 6.25225274e-04],
-       [2.57496815e-04, 6.25225274e-04, 1.92775270e-03]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=142, candidate_x=array([ 4.6823479 , 17.38247411, 46.39411093]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-2.3286652591320904, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 137, 138, 139, 140, 141]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5306,14 +5302,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=143, candidate_x=array([ 4.70561382, 16.9640747 , 46.46325629]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.202503206630941, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 135, 136, 137, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 136, 137, 138, 139, 140, 141, 142, 143]), model=ScalarModel(intercept=8.148860497225302, linear_terms=array([0.187207  , 0.08814569, 0.08171824]), square_terms=array([[0.00728267, 0.00251683, 0.00076182],
-       [0.00251683, 0.00121874, 0.00107815],
-       [0.00076182, 0.00107815, 0.00217323]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=143, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-2.333609752112349, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5395,14 +5391,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=144, candidate_x=array([ 4.7056138 , 16.96407453, 46.46325643]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.2133196257898786, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 136, 137, 138, 139, 140, 141, 142, 143]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 137, 138, 139, 140, 141, 142, 143, 144]), model=ScalarModel(intercept=8.155388091733592, linear_terms=array([0.17017583, 0.07067601, 0.08758009]), square_terms=array([[0.00666352, 0.00206706, 0.00057631],
-       [0.00206706, 0.00079034, 0.00071039],
-       [0.00057631, 0.00071039, 0.00228837]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=144, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.921281913850146, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5484,14 +5480,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=145, candidate_x=array([ 4.70561382, 16.96407458, 46.46325637]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.6412208832639814, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 137, 138, 139, 140, 141, 142, 143, 144]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 138, 139, 140, 141, 142, 143, 144, 145]), model=ScalarModel(intercept=8.154378536523849, linear_terms=array([0.1514612 , 0.0430165 , 0.08027159]), square_terms=array([[6.11099166e-03, 1.44644657e-03, 1.34613783e-06],
-       [1.44644657e-03, 3.93524632e-04, 1.64318773e-04],
-       [1.34613783e-06, 1.64318773e-04, 2.14884486e-03]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=145, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.870947994454552, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5573,14 +5569,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=146, candidate_x=array([ 4.7056138 , 16.96407467, 46.46325635]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.333263220338381, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 138, 139, 140, 141, 142, 143, 144, 145]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=146, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-6.04922915081226, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5662,14 +5658,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=147, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.502273956646292, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=147, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.4123861468494865, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5751,15 +5747,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=148, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.9761623665528454, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=148, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-6.813282686092421, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5841,15 +5836,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=149, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.4968576018768154, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=149, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.7333581547667745, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -5931,15 +5925,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=150, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.388743002134334, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=150, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.460943274880049, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6021,15 +6014,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=151, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.8415624114490758, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=151, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.206044730675779, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6111,15 +6104,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=152, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.3405828490281153, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=152, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.023927126602242, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6201,15 +6194,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=153, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-4.35214351659552, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=153, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-9.984145111333305, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6291,15 +6284,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=154, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.3781078239822704, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=154, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.739154302390087, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6381,15 +6374,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=155, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.1294348680302044, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=155, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.109053624771362, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6471,15 +6464,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=156, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.4788552410275997, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=156, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.382040636660244, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6561,15 +6554,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=157, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.8293094663371214, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=157, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.192035921420993, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6651,15 +6644,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=158, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.225643824594799, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=158, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.0554340867341425, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6741,15 +6734,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=159, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.1972850018569776, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=159, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.316340945856832, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6831,15 +6824,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=160, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.6218489419725652, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=160, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-8.284106646172729, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -6921,16 +6914,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=161, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.6424520718593096, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=161, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-8.306281273437465, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7012,16 +7004,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=162, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.2931400616521334, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=162, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.559101787647588, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7103,16 +7094,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=163, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.5678769400874786, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=163, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.837125538785417, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7194,16 +7184,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=164, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-4.241302198289405, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=164, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-9.718304929566266, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7285,16 +7275,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=165, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.997995258791049, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=165, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-6.837878744282935, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7376,16 +7366,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=166, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.021006739578344, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=166, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.574355925268063, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7467,16 +7457,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=167, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.5920160820768656, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=167, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.642299098917888, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7558,16 +7548,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=168, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.092955399921348, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=168, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.733371767337074, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7649,16 +7639,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=169, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.4660126497864314, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=169, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.644332194652515, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7740,16 +7730,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=170, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.33485035555571, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=170, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.616756174364399, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7831,16 +7821,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=171, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.6134463292353423, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=171, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-6.023446547837783, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -7922,16 +7912,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=172, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-0.24826904914756806, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=172, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-0.4927441097777205, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8013,16 +8003,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=173, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.9661140254579133, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=173, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-9.070187909798488, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8104,17 +8094,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=174, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.539553986164235, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=174, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.504737121260635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8196,17 +8185,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=175, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.041725064886811, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=175, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-2.371428577306904, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8288,17 +8276,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=176, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.3403774420251264, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=176, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.336163155862589, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8380,17 +8367,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=177, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.7318968268456154, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=177, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-6.215109327515015, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8472,17 +8459,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=178, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.2100175877167314, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=178, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.326137665044721, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8564,17 +8551,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=179, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.3818062977215506, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=179, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.1083564942403825, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8656,17 +8643,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=180, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.2481907423851633, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178, 179]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=180, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.144859077433388, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178, 179]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8748,17 +8735,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=181, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.1979708169657926, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178, 179, 180]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=181, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.050534542594225, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178, 179, 180]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8840,17 +8827,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=182, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.976007480516852, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178, 179, 180, 181]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=182, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.489435544148635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178, 179, 180, 181]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -8932,17 +8919,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=183, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.6051729641802894, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178, 179, 180, 181, 182]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=183, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-8.279516471817773, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178, 179, 180, 181, 182]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9024,17 +9011,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=184, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.782764051066316, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 185 entries., 'multistart_info': {'start_parameters': [array([ 4.49799881, 16.44990466, 46.2434204 ]), array([ 4.75078971, 14.41694464, 46.50520192]), array([ 4.6220078 , 21.87938374, 45.04784782])], 'local_optima': [{'solution_x': array([ 4.70561465, 16.9640749 , 46.46325682]), 'solution_criterion': 7.718691297994154, 'states': [State(trustregion=Region(center=array([ 4.49799881, 16.44990466, 46.2434204 ]), radius=4.624342040168151, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=8.145256723269389, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=184, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.033727062188392, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178, 179, 180, 181, 182, 183]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'tranquilo_history': History for least_squares function with 185 entries., 'multistart_info': {'start_parameters': [array([ 4.70567639, 16.96622308, 46.46531825]), array([ 4.73735612, 14.6585077 , 46.46528069]), array([ 4.60300984, 22.22100549, 44.99139068])], 'local_optima': [{'solution_x': array([ 4.68234699, 17.38247439, 46.39411122]), 'solution_criterion': 7.7200111553560635, 'states': [State(trustregion=Region(center=array([ 4.70567639, 16.96622308, 46.46531825]), radius=4.646531825239875, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=8.140218982858876, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.],
        [0., 0., 0.],
-       [0., 0., 0.]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+       [0., 0., 0.]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9116,12 +9103,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=0, candidate_x=array([ 4.49799881, 16.44990466, 46.2434204 ]), index=0, x=array([ 4.49799881, 16.44990466, 46.2434204 ]), fval=8.145256723269389, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.49799881, 16.44990466, 46.2434204 ]), radius=4.624342040168151, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=15.006948940519543, linear_terms=array([0.01323854, 1.10352104, 2.39437648]), square_terms=array([[7.03892333e+00, 8.57793675e-02, 4.17471910e-03],
-       [8.57793675e-02, 4.23198942e-02, 8.86295613e-02],
-       [4.17471910e-03, 8.86295613e-02, 1.91682780e-01]]), scale=array([3.11259995, 3.72720108, 3.72720108]), shift=array([ 5.11259995, 16.44990466, 46.2434204 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=0, candidate_x=array([ 4.70567639, 16.96622308, 46.46531825]), index=0, x=array([ 4.70567639, 16.96622308, 46.46531825]), fval=8.140218982858874, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.70567639, 16.96622308, 46.46531825]), radius=4.646531825239875, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=16.490411097157534, linear_terms=array([-0.03818026,  0.33176217,  2.4502688 ]), square_terms=array([[ 7.36404859e+00,  1.27470230e-02, -2.48061738e-02],
+       [ 1.27470230e-02,  3.38443075e-03,  2.47121766e-02],
+       [-2.48061738e-02,  2.47121766e-02,  1.82734843e-01]]), scale=array([3.22538118, 3.74508596, 3.74508596]), shift=array([ 5.22538118, 16.96622308, 46.46531825])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9203,12 +9190,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=13, candidate_x=array([ 5.14652343, 12.72270358, 42.51621932]), index=0, x=array([ 4.49799881, 16.44990466, 46.2434204 ]), fval=8.145256723269389, rho=-0.060946909275941985, accepted=False, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.49799881, 16.44990466, 46.2434204 ]), radius=2.3121710200840755, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  3,  4,  5,  7,  8,  9, 10, 11, 12, 13]), model=ScalarModel(intercept=14.580432746581428, linear_terms=array([-1.35115423,  1.10781698,  1.4968029 ]), square_terms=array([[ 3.86103295,  0.02862604, -0.02446957],
-       [ 0.02862604,  0.04424158,  0.05807645],
-       [-0.02446957,  0.05807645,  0.07760107]]), scale=2.3121710200840755, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=13, candidate_x=array([ 5.23682197, 13.22113712, 42.72023229]), index=0, x=array([ 4.70567639, 16.96622308, 46.46531825]), fval=8.140218982858874, rho=-0.12699405829214627, accepted=False, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70567639, 16.96622308, 46.46531825]), radius=2.3232659126199375, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13]), model=ScalarModel(intercept=15.299412767135175, linear_terms=array([-1.79046808,  1.42748659,  1.30013449]), square_terms=array([[ 3.81432883, -0.01997852, -0.05904408],
+       [-0.01997852,  0.06821013,  0.06117938],
+       [-0.05904408,  0.06117938,  0.05550258]]), scale=2.3232659126199375, shift=array([ 4.70567639, 16.96622308, 46.46531825])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9290,12 +9277,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=14, candidate_x=array([ 5.06004155, 15.0250227 , 44.33684394]), index=0, x=array([ 4.49799881, 16.44990466, 46.2434204 ]), fval=8.145256723269389, rho=-0.21388051286164697, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  3,  4,  5,  7,  8,  9, 10, 11, 12, 13]), old_indices_discarded=array([2, 6]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.49799881, 16.44990466, 46.2434204 ]), radius=1.1560855100420377, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  3,  4,  5,  7,  8,  9, 10, 11, 13, 14]), model=ScalarModel(intercept=13.338681860248593, linear_terms=array([-1.60670822,  0.9508896 ,  0.29728755]), square_terms=array([[ 0.98966811, -0.02693823, -0.02007844],
-       [-0.02693823,  0.03530776,  0.01051071],
-       [-0.02007844,  0.01051071,  0.00333429]]), scale=1.1560855100420377, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=14, candidate_x=array([ 5.43402295, 15.17155435, 44.84689792]), index=0, x=array([ 4.70567639, 16.96622308, 46.46531825]), fval=8.140218982858874, rho=-0.4964514456620586, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13]), old_indices_discarded=array([ 2, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70567639, 16.96622308, 46.46531825]), radius=1.1616329563099688, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  4,  5,  7,  8,  9, 10, 11, 13, 14]), model=ScalarModel(intercept=13.053662317110621, linear_terms=array([-2.55034397,  0.43395241, -0.23182703]), square_terms=array([[ 1.1067579 , -0.03430609,  0.00962644],
+       [-0.03430609,  0.00735824, -0.00405499],
+       [ 0.00962644, -0.00405499,  0.00237418]]), scale=1.1616329563099688, shift=array([ 4.70567639, 16.96622308, 46.46531825])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9377,12 +9364,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=15, candidate_x=array([ 5.28835652, 15.64432349, 45.99260695]), index=0, x=array([ 4.49799881, 16.44990466, 46.2434204 ]), fval=8.145256723269389, rho=-0.30102023579118353, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  3,  4,  5,  7,  8,  9, 10, 11, 13, 14]), old_indices_discarded=array([ 2, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.49799881, 16.44990466, 46.2434204 ]), radius=0.5780427550210189, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=8.024369229911517, linear_terms=array([-0.15788203, -0.11944546, -0.05215894]), square_terms=array([[ 0.313711  , -0.00880867, -0.00850489],
-       [-0.00880867,  0.00133093,  0.00075035],
-       [-0.00850489,  0.00075035,  0.00050391]]), scale=0.5780427550210189, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=15, candidate_x=array([ 5.85458923, 17.02330601, 46.30366444]), index=0, x=array([ 4.70567639, 16.96622308, 46.46531825]), fval=8.140218982858874, rho=-0.9592394226356723, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  4,  5,  7,  8,  9, 10, 11, 13, 14]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70567639, 16.96622308, 46.46531825]), radius=0.5808164781549844, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=8.005185836487234, linear_terms=array([ 0.36477503, -0.04619487, -0.08305498]), square_terms=array([[ 0.33740382, -0.00962398, -0.01068073],
+       [-0.00962398,  0.00041438,  0.00052782],
+       [-0.01068073,  0.00052782,  0.00073221]]), scale=0.5808164781549844, shift=array([ 4.70567639, 16.96622308, 46.46531825])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9464,12 +9451,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=28, candidate_x=array([ 4.7160682 , 16.96957784, 46.47692321]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=1.748632163549693, accepted=True, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.6100311757687438, relative_step_length=1.0553391950160533, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=1.1560855100420377, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 23, 25, 26, 27, 28]), model=ScalarModel(intercept=7.888984808494836, linear_terms=array([-0.34386436, -0.05194838, -0.02312738]), square_terms=array([[ 1.21657627e+00, -4.24486844e-03, -1.44110029e-03],
-       [-4.24486844e-03,  2.03874438e-04,  8.44861153e-05],
-       [-1.44110029e-03,  8.44861153e-05,  3.99633748e-05]]), scale=1.1560855100420377, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=28, candidate_x=array([ 4.25725028, 17.1474243 , 46.81622412]), index=28, x=array([ 4.25725028, 17.1474243 , 46.81622412]), fval=8.066657471859852, rho=0.3089444772758513, accepted=True, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.5975406126185681, relative_step_length=1.0287941804211709, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.25725028, 17.1474243 , 46.81622412]), radius=1.1616329563099688, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=7.972477076287903, linear_terms=array([-0.30891814, -0.0205982 ,  0.02489372]), square_terms=array([[ 1.24232601e+00, -1.97645441e-03,  7.55419362e-04],
+       [-1.97645441e-03,  3.29059590e-05, -3.46870075e-05],
+       [ 7.55419362e-04, -3.46870075e-05,  4.26055898e-05]]), scale=1.1616329563099688, shift=array([ 4.25725028, 17.1474243 , 46.81622412])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9551,12 +9538,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=29, candidate_x=array([ 5.0320216 , 18.02477148, 46.94434338]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-7.24606288417893, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 23, 25, 26, 27, 28]), old_indices_discarded=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 14, 15, 22, 24]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.5780427550210189, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 28]), model=ScalarModel(intercept=7.86487963078101, linear_terms=array([-0.16654267, -0.09029353,  0.00243157]), square_terms=array([[ 3.03961339e-01, -3.19690397e-03, -2.41147826e-04],
-       [-3.19690397e-03,  5.96151742e-04, -1.14490562e-05],
-       [-2.41147826e-04, -1.14490562e-05,  8.93603588e-07]]), scale=0.5780427550210189, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=29, candidate_x=array([ 4.54026495, 17.88507428, 45.93860604]), index=28, x=array([ 4.25725028, 17.1474243 , 46.81622412]), fval=8.066657471859852, rho=-3.91761543536851, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), old_indices_discarded=array([ 1,  4,  5,  7,  8, 10, 11, 13, 14, 15, 17, 18]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.25725028, 17.1474243 , 46.81622412]), radius=0.5808164781549844, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28]), model=ScalarModel(intercept=8.029837216385943, linear_terms=array([-0.21170276, -0.00943844,  0.02072123]), square_terms=array([[ 3.15125217e-01, -7.20879321e-04, -2.34436529e-03],
+       [-7.20879321e-04,  8.96320181e-06, -1.98856116e-06],
+       [-2.34436529e-03, -1.98856116e-06,  6.54857806e-05]]), scale=0.5808164781549844, shift=array([ 4.25725028, 17.1474243 , 46.81622412])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9638,12 +9625,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=30, candidate_x=array([ 4.9641153 , 17.54464667, 46.46229469]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-1.4644299520714406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 25, 27, 28]), old_indices_discarded=array([15, 24, 26, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.28902137751050944, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 22, 23, 25, 27, 28, 30]), model=ScalarModel(intercept=7.941661553246127, linear_terms=array([-0.07751077, -0.00136076, -0.00569786]), square_terms=array([[ 7.57663178e-02, -6.33754053e-04, -3.46700316e-04],
-       [-6.33754053e-04,  8.19469644e-05,  1.60473808e-05],
-       [-3.46700316e-04,  1.60473808e-05,  6.25046163e-06]]), scale=0.28902137751050944, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=30, candidate_x=array([ 4.61419506, 17.35931809, 46.40359963]), index=30, x=array([ 4.61419506, 17.35931809, 46.40359963]), fval=7.902897717558958, rho=1.8634535269130141, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28]), old_indices_discarded=array([15, 16, 27, 29]), step_length=0.5852926755287875, relative_step_length=1.00770673274977, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.61419506, 17.35931809, 46.40359963]), radius=1.1616329563099688, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([16, 17, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30]), model=ScalarModel(intercept=8.04777209526033, linear_terms=array([ 0.01960014,  0.2358811 , -0.06678174]), square_terms=array([[ 1.2126301 ,  0.06254486, -0.03042362],
+       [ 0.06254486,  0.01034081, -0.00401734],
+       [-0.03042362, -0.00401734,  0.00165776]]), scale=1.1616329563099688, shift=array([ 4.61419506, 17.35931809, 46.40359963])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9725,12 +9712,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=31, candidate_x=array([ 4.97107558, 17.01391205, 46.61691596]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-11.111797991447878, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 22, 23, 25, 27, 28, 30]), old_indices_discarded=array([21, 24, 26, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.14451068875525472, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 19, 22, 23, 25, 28, 30, 31]), model=ScalarModel(intercept=7.978558495501097, linear_terms=array([ 0.02652198, -0.00028642,  0.00624314]), square_terms=array([[ 2.05648441e-02, -5.42557035e-04,  2.51148716e-04],
-       [-5.42557035e-04,  4.09091464e-05, -7.41486358e-06],
-       [ 2.51148716e-04, -7.41486358e-06,  8.64371733e-06]]), scale=0.14451068875525472, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=31, candidate_x=array([ 4.65344865, 16.2390077 , 46.71701767]), index=30, x=array([ 4.61419506, 17.35931809, 46.40359963]), fval=7.902897717558958, rho=-0.9919738918629465, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([16, 17, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30]), old_indices_discarded=array([ 0,  1,  4,  5,  7,  8,  9, 10, 11, 13, 14, 15, 18, 19, 23]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.61419506, 17.35931809, 46.40359963]), radius=0.5808164781549844, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 21, 22, 23, 25, 28, 29, 30]), model=ScalarModel(intercept=8.007846882803051, linear_terms=array([-0.0556707 ,  0.07919637, -0.05762226]), square_terms=array([[ 0.30197476,  0.0146575 , -0.00818423],
+       [ 0.0146575 ,  0.0019494 , -0.0010771 ],
+       [-0.00818423, -0.0010771 ,  0.00061556]]), scale=0.5808164781549844, shift=array([ 4.61419506, 17.35931809, 46.40359963])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9812,12 +9799,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=32, candidate_x=array([ 4.62004715, 17.00587342, 46.37520828]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-4.4393778330120055, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 19, 22, 23, 25, 28, 30, 31]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.07225534437762736, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([22, 28, 31, 32]), model=ScalarModel(intercept=7.849521073607839, linear_terms=array([ 0.27833608,  0.0261772 , -0.30529537]), square_terms=array([[ 0.02270853,  0.00153107, -0.01599417],
-       [ 0.00153107,  0.00013776, -0.00100599],
-       [-0.01599417, -0.00100599,  0.01422192]]), scale=0.07225534437762736, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=32, candidate_x=array([ 4.71868512, 16.8935651 , 46.74203922]), index=30, x=array([ 4.61419506, 17.35931809, 46.40359963]), fval=7.902897717558958, rho=-0.33903410676392864, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 21, 22, 23, 25, 28, 29, 30]), old_indices_discarded=array([14, 15, 20, 24, 26, 27, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.61419506, 17.35931809, 46.40359963]), radius=0.2904082390774922, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 18, 19, 21, 22, 23, 25, 28, 29, 30, 32]), model=ScalarModel(intercept=8.008165668299897, linear_terms=array([-0.02148056,  0.04374795, -0.02926837]), square_terms=array([[ 0.07668834,  0.00382458, -0.00203623],
+       [ 0.00382458,  0.00047944, -0.00025807],
+       [-0.00203623, -0.00025807,  0.00014286]]), scale=0.2904082390774922, shift=array([ 4.61419506, 17.35931809, 46.40359963])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9899,12 +9886,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=33, candidate_x=array([ 4.67868592, 16.95650298, 46.5373587 ]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-0.3501719214659062, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 28, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.03612767218881368, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([22, 28, 32, 33]), model=ScalarModel(intercept=7.849521073607844, linear_terms=array([-0.07066411, -0.03896864,  0.02686239]), square_terms=array([[ 0.00240414,  0.00059596, -0.00085165],
-       [ 0.00059596,  0.00034167, -0.00034783],
-       [-0.00085165, -0.00034783,  0.00056658]]), scale=0.03612767218881368, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=33, candidate_x=array([ 4.67166491, 17.12030492, 46.5633123 ]), index=30, x=array([ 4.61419506, 17.35931809, 46.40359963]), fval=7.902897717558958, rho=-1.4406835601923031, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 18, 19, 21, 22, 23, 25, 28, 29, 30, 32]), old_indices_discarded=array([15, 17, 20, 24, 26, 27, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.61419506, 17.35931809, 46.40359963]), radius=0.1452041195387461, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 18, 19, 21, 22, 23, 28, 29, 30, 32, 33]), model=ScalarModel(intercept=8.013230759011146, linear_terms=array([-0.00958804,  0.02117261, -0.01053588]), square_terms=array([[ 1.91446579e-02,  9.57451186e-04, -5.80541252e-04],
+       [ 9.57451186e-04,  1.08214274e-04, -5.56889234e-05],
+       [-5.80541252e-04, -5.56889234e-05,  3.00777236e-05]]), scale=0.1452041195387461, shift=array([ 4.61419506, 17.35931809, 46.40359963])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -9986,12 +9973,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=34, candidate_x=array([ 4.74894929, 16.98248775, 46.46934849]), index=28, x=array([ 4.7160682 , 16.96957784, 46.47692321]), fval=7.849521073607845, rho=-3.972641796941464, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 28, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.7160682 , 16.96957784, 46.47692321]), radius=0.01806383609440684, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([28, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=7.7722491279935175, linear_terms=array([0.05933982, 0.02513362, 0.06787715]), square_terms=array([[0.0018065 , 0.000499  , 0.00123775],
-       [0.000499  , 0.00014611, 0.00036662],
-       [0.00123775, 0.00036662, 0.00094791]]), scale=0.01806383609440684, shift=array([ 4.7160682 , 16.96957784, 46.47692321])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=34, candidate_x=array([ 4.64974776, 17.23288482, 46.46665088]), index=30, x=array([ 4.61419506, 17.35931809, 46.40359963]), fval=7.902897717558958, rho=-10.042100518414895, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 18, 19, 21, 22, 23, 28, 29, 30, 32, 33]), old_indices_discarded=array([25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.61419506, 17.35931809, 46.40359963]), radius=0.07260205976937305, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([19, 23, 30, 33, 34]), model=ScalarModel(intercept=7.9535751109453, linear_terms=array([-0.10600093, -0.06484184, -0.01057826]), square_terms=array([[0.02315565, 0.00490555, 0.00184373],
+       [0.00490555, 0.00196284, 0.00056369],
+       [0.00184373, 0.00056369, 0.00019232]]), scale=0.07260205976937305, shift=array([ 4.61419506, 17.35931809, 46.40359963])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10073,12 +10060,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=47, candidate_x=array([ 4.70561459, 16.96407505, 46.4632578 ]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=1.3592037741862977, accepted=True, new_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_used=array([28, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.018063836094405265, relative_step_length=0.9999999999999128, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.03612767218881368, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([28, 35, 36, 37, 39, 40, 41, 42, 43, 45, 46, 47]), model=ScalarModel(intercept=7.733977086304757, linear_terms=array([-0.00031022,  0.0001404 ,  0.00211586]), square_terms=array([[1.11066890e-03, 1.32662670e-05, 3.33650815e-05],
-       [1.32662670e-05, 6.83876882e-07, 1.16854914e-06],
-       [3.33650815e-05, 1.16854914e-06, 2.61595057e-06]]), scale=0.03612767218881368, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=35, candidate_x=array([ 4.68234791, 17.38247432, 46.39411084]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=1.6587128458247886, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 30, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.07260205976937305, relative_step_length=1.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.1452041195387461, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 18, 19, 22, 23, 28, 30, 32, 33, 34, 35]), model=ScalarModel(intercept=7.9034674515792265, linear_terms=array([-0.0020679 , -0.05545576, -0.00111844]), square_terms=array([[ 1.95785478e-02, -1.81540373e-03,  5.46280827e-05],
+       [-1.81540373e-03,  4.22105484e-04, -1.10012082e-05],
+       [ 5.46280827e-05, -1.10012082e-05,  6.47099121e-06]]), scale=0.1452041195387461, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10160,12 +10147,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=48, candidate_x=array([ 4.7094481 , 16.96169381, 46.42740856]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-198.231940320406, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 35, 36, 37, 39, 40, 41, 42, 43, 45, 46, 47]), old_indices_discarded=array([32, 33, 34, 38, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.01806383609440684, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([28, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 47]), model=ScalarModel(intercept=7.726839188660502, linear_terms=array([0.00879896, 0.00526178, 0.00582683]), square_terms=array([[5.51747809e-04, 1.10445807e-04, 1.13316640e-04],
-       [1.10445807e-04, 3.27458616e-05, 3.33962352e-05],
-       [1.13316640e-04, 3.33962352e-05, 3.41224344e-05]]), scale=0.01806383609440684, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=36, candidate_x=array([ 4.68987942, 17.52745381, 46.39707325]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-7.520985100519123, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 18, 19, 22, 23, 28, 30, 32, 33, 34, 35]), old_indices_discarded=array([21, 29]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.07260205976937305, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([19, 23, 30, 33, 34, 35, 36]), model=ScalarModel(intercept=7.975131384361624, linear_terms=array([ 0.10245776, -0.00847563,  0.01481371]), square_terms=array([[ 9.61461860e-03, -3.05900830e-04,  5.31345067e-04],
+       [-3.05900830e-04,  1.17098733e-04, -1.85801690e-05],
+       [ 5.31345067e-04, -1.85801690e-05,  4.31818780e-05]]), scale=0.07260205976937305, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10247,12 +10234,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=49, candidate_x=array([ 4.69366858, 16.9549441 , 46.45324676]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-30.45775065724263, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 47]), old_indices_discarded=array([33, 34, 37, 46, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.00903191804720342, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([28, 35, 36, 38, 39, 40, 41, 42, 44, 45, 47, 49]), model=ScalarModel(intercept=7.804413499313456, linear_terms=array([-0.03091731, -0.02194317, -0.02545883]), square_terms=array([[8.40899570e-05, 7.03887114e-05, 7.94924297e-05],
-       [7.03887114e-05, 8.76474355e-05, 1.00200482e-04],
-       [7.94924297e-05, 1.00200482e-04, 1.15689861e-04]]), scale=0.00903191804720342, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=37, candidate_x=array([ 4.61001961, 17.38408728, 46.38802192]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-3.52091474572316, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([19, 23, 30, 33, 34, 35, 36]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.036301029884686524, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([30, 34, 35, 36, 37]), model=ScalarModel(intercept=7.736319471622591, linear_terms=array([-0.13924985,  0.09569219,  0.32447151]), square_terms=array([[ 0.00207511, -0.0019659 , -0.00635419],
+       [-0.0019659 ,  0.00241474,  0.00784056],
+       [-0.00635419,  0.00784056,  0.02582089]]), scale=0.036301029884686524, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10334,12 +10321,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=50, candidate_x=array([ 4.71175707, 16.96835254, 46.46831236]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-4.664413684425712, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 35, 36, 38, 39, 40, 41, 42, 44, 45, 47, 49]), old_indices_discarded=array([37, 43, 46, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.00451595902360171, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([28, 35, 38, 39, 41, 42, 44, 47, 49, 50]), model=ScalarModel(intercept=7.819759695884859, linear_terms=array([-0.0184351 , -0.00690095, -0.01815452]), square_terms=array([[3.22753081e-05, 1.18663247e-05, 3.36580092e-05],
-       [1.18663247e-05, 1.84364654e-05, 3.39520031e-05],
-       [3.36580092e-05, 3.39520031e-05, 7.21619318e-05]]), scale=0.00451595902360171, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=38, candidate_x=array([ 4.69156463, 17.37787165, 46.35930233]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-0.6007840565845133, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([30, 34, 35, 36, 37]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.018150514942343262, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([30, 35, 37, 38]), model=ScalarModel(intercept=7.7249497274541925, linear_terms=array([-0.07424609,  0.02850284, -0.12615741]), square_terms=array([[ 0.00056555, -0.00017033,  0.00138401],
+       [-0.00017033,  0.00054861,  0.00033325],
+       [ 0.00138401,  0.00033325,  0.00586802]]), scale=0.018150514942343262, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10421,12 +10408,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=51, candidate_x=array([ 4.70872878, 16.96522582, 46.46631908]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-9.750702226133074, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([28, 35, 38, 39, 41, 42, 44, 47, 49, 50]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.002257979511800855, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([38, 44, 47, 50, 51]), model=ScalarModel(intercept=7.711709399926576, linear_terms=array([-0.04491751, -0.19266887,  0.31333725]), square_terms=array([[ 0.00074431,  0.00211792, -0.00257367],
-       [ 0.00211792,  0.00796962, -0.01179223],
-       [-0.00257367, -0.01179223,  0.01919665]]), scale=0.002257979511800855, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=39, candidate_x=array([ 4.69077163, 17.37833807, 46.40964704]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-1.7832014670167953, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([30, 35, 37, 38]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.009075257471171631, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), model=ScalarModel(intercept=8.235548908421132, linear_terms=array([-0.0775429 ,  0.03941567,  0.02310366]), square_terms=array([[ 0.00085966, -0.00051305, -0.0001847 ],
+       [-0.00051305,  0.00031468,  0.0001103 ],
+       [-0.0001847 ,  0.0001103 ,  0.00013357]]), scale=0.009075257471171631, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10508,12 +10495,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=52, candidate_x=array([ 4.70605376, 16.96501762, 46.46125352]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.670182060224612, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([38, 44, 47, 50, 51]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.0011289897559004275, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([44, 47, 51, 52]), model=ScalarModel(intercept=7.724404600954944, linear_terms=array([ 0.15706182,  0.25063012, -0.16051657]), square_terms=array([[ 0.00559721,  0.00893029, -0.00582589],
-       [ 0.00893029,  0.01429041, -0.00937304],
-       [-0.00582589, -0.00937304,  0.00620876]]), scale=0.0011289897559004275, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=52, candidate_x=array([ 4.69044162, 17.37887834, 46.39213068]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-6.580762395292093, accepted=False, new_indices=array([40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]), old_indices_used=array([35, 38, 39]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.0045376287355858155, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52]), model=ScalarModel(intercept=8.256636552922478, linear_terms=array([ 0.00561754, -0.00241631, -0.00535758]), square_terms=array([[ 3.60721443e-05, -6.02291934e-06, -1.26800788e-05],
+       [-6.02291934e-06,  1.19018933e-06,  2.52528763e-06],
+       [-1.26800788e-05,  2.52528763e-06,  5.38340499e-06]]), scale=0.0045376287355858155, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10595,12 +10582,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=53, candidate_x=array([ 4.70496849, 16.96342162, 46.46391371]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.1476151112107937, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([44, 47, 51, 52]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.0005644948779502137, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=8.032744143893979, linear_terms=array([-0.02352272,  0.01076142, -0.05482852]), square_terms=array([[ 1.60641111e-04, -4.64426692e-06,  2.64479134e-04],
-       [-4.64426692e-06,  6.08149184e-05, -1.26242645e-04],
-       [ 2.64479134e-04, -1.26242645e-04,  6.86685396e-04]]), scale=0.0005644948779502137, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=53, candidate_x=array([ 4.67926273, 17.38385896, 46.39713648]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-44.386134325605916, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 52]), old_indices_discarded=array([39, 43, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.0022688143677929077, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 40, 42, 44, 45, 46, 47, 49, 50, 51, 52, 53]), model=ScalarModel(intercept=8.231039150006058, linear_terms=array([ 0.00910109, -0.00965067, -0.01320036]), square_terms=array([[ 3.41638836e-05, -2.54257345e-05, -3.55308250e-05],
+       [-2.54257345e-05,  1.97403092e-05,  2.76469541e-05],
+       [-3.55308250e-05,  2.76469541e-05,  3.90654416e-05]]), scale=0.0022688143677929077, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10682,12 +10669,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=66, candidate_x=array([ 4.70582045, 16.96398708, 46.46377601]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-5.348742433713813, accepted=False, new_indices=array([54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]), old_indices_used=array([47, 52, 53]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.00028224743897510687, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 54, 55, 56, 57, 58, 59, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=8.002354293534912, linear_terms=array([-0.01609135, -0.00394256,  0.01908672]), square_terms=array([[ 4.98645298e-05,  1.25322573e-05, -6.07331796e-05],
-       [ 1.25322573e-05,  3.15154476e-06, -1.52731335e-05],
-       [-6.07331796e-05, -1.52731335e-05,  7.40179510e-05]]), scale=0.00028224743897510687, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=54, candidate_x=array([ 4.68125936, 17.38365099, 46.39571646]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-16.968936842314275, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 40, 42, 44, 45, 46, 47, 49, 50, 51, 52, 53]), old_indices_discarded=array([41, 43, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.0011344071838964539, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=7.811900251844641, linear_terms=array([-0.0342071 ,  0.03065072,  0.04523505]), square_terms=array([[ 0.00027609, -0.00024653, -0.00037284],
+       [-0.00024653,  0.00023121,  0.00034213],
+       [-0.00037284,  0.00034213,  0.0005113 ]]), scale=0.0011344071838964539, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10769,12 +10756,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=67, candidate_x=array([ 4.70579084, 16.96412186, 46.46304238]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-3.39343113052912, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 54, 55, 56, 57, 58, 59, 62, 63, 64, 65, 66]), old_indices_discarded=array([53, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=0.00014112371948755344, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 54, 55, 56, 57, 58, 62, 63, 64, 65, 66, 67]), model=ScalarModel(intercept=7.940414587718634, linear_terms=array([-0.02084255,  0.00402229,  0.03057654]), square_terms=array([[ 9.17349633e-05, -1.44372171e-05, -1.33718159e-04],
-       [-1.44372171e-05,  4.64572232e-06,  2.26408140e-05],
-       [-1.33718159e-04,  2.26408140e-05,  1.96000407e-04]]), scale=0.00014112371948755344, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=67, candidate_x=array([ 4.6829256 , 17.38192261, 46.39330538]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-1.3188906121302586, accepted=False, new_indices=array([55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66]), old_indices_used=array([35, 53, 54]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.0005672035919482269, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 55, 56, 57, 58, 59, 60, 62, 63, 65, 66, 67]), model=ScalarModel(intercept=7.80185769091568, linear_terms=array([ 6.86991386e-04, -5.16063971e-05,  8.42278729e-04]), square_terms=array([[ 7.64487179e-07, -2.73353872e-08,  4.77961648e-07],
+       [-2.73353872e-08,  1.45372181e-09, -2.56082670e-08],
+       [ 4.77961648e-07, -2.56082670e-08,  4.53272066e-07]]), scale=0.0005672035919482269, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10856,12 +10843,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=68, candidate_x=array([ 4.70569155, 16.96405815, 46.46314072]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-11.301558821142645, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 54, 55, 56, 57, 58, 62, 63, 64, 65, 66, 67]), old_indices_discarded=array([59, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=7.056185974377672e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=8.106239946117373, linear_terms=array([-0.03451123, -0.02301713,  0.03291206]), square_terms=array([[ 0.00026391,  0.00018275, -0.00024859],
-       [ 0.00018275,  0.00014313, -0.00016172],
-       [-0.00024859, -0.00016172,  0.0002407 ]]), scale=7.056185974377672e-05, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=68, candidate_x=array([ 4.68199145, 17.38250259, 46.39367054]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-381.70649331838456, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 55, 56, 57, 58, 59, 60, 62, 63, 65, 66, 67]), old_indices_discarded=array([54, 61, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.00028360179597411347, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 55, 56, 57, 58, 59, 60, 62, 63, 66, 67, 68]), model=ScalarModel(intercept=7.832501858210558, linear_terms=array([-0.00934396, -0.00257555, -0.00907166]), square_terms=array([[2.50062911e-05, 7.51022956e-06, 2.43754087e-05],
+       [7.51022956e-06, 2.28737337e-06, 7.24151065e-06],
+       [2.43754087e-05, 7.24151065e-06, 2.40319016e-05]]), scale=0.00028360179597411347, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -10943,12 +10930,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=81, candidate_x=array([ 4.70565894, 16.96410674, 46.463213  ]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-7.878855790285914, accepted=False, new_indices=array([69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]), old_indices_used=array([47, 67, 68]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=3.528092987188836e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=8.09711787584544, linear_terms=array([0.00382197, 0.01486028, 0.0062441 ]), square_terms=array([[3.08967374e-06, 1.18500122e-05, 4.97922821e-06],
-       [1.18500122e-05, 4.54565210e-05, 1.91002667e-05],
-       [4.97922821e-06, 1.91002667e-05, 8.02569531e-06]]), scale=3.528092987188836e-05, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=69, candidate_x=array([ 4.68254351, 17.38253273, 46.39430771]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-31.00850079815674, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 55, 56, 57, 58, 59, 60, 62, 63, 66, 67, 68]), old_indices_discarded=array([61, 64, 65]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=0.00014180089798705673, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=8.169210918272597, linear_terms=array([-0.00038306, -0.00507427,  0.00257826]), square_terms=array([[ 1.03451998e-05,  2.11956146e-06,  1.08038467e-05],
+       [ 2.11956146e-06,  2.71695453e-05, -1.40435993e-05],
+       [ 1.08038467e-05, -1.40435993e-05,  2.11952211e-05]]), scale=0.00014180089798705673, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11030,12 +11017,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=82, candidate_x=array([ 4.70560673, 16.96404321, 46.46324479]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-29.942088486440817, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81]), old_indices_discarded=array([68, 69, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1.764046493594418e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 70, 71, 74, 75, 76, 77, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=8.107261374316284, linear_terms=array([ 0.00122538,  0.00206624, -0.00252862]), square_terms=array([[ 1.06921156e-06,  3.42422311e-06, -1.16687573e-07],
-       [ 3.42422311e-06,  1.19607616e-05,  8.54719078e-07],
-       [-1.16687573e-07,  8.54719078e-07,  1.53200886e-06]]), scale=1.764046493594418e-05, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=82, candidate_x=array([ 4.68235644, 17.38260121, 46.39404813]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-85.81986855232132, accepted=False, new_indices=array([70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), old_indices_used=array([35, 68, 69]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=7.090044899352837e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=8.16956317384026, linear_terms=array([0.00048644, 0.00339551, 0.00475271]), square_terms=array([[6.68334479e-08, 3.76265470e-07, 5.26634881e-07],
+       [3.76265470e-07, 2.20344328e-06, 3.08400236e-06],
+       [5.26634881e-07, 3.08400236e-06, 4.31645810e-06]]), scale=7.090044899352837e-05, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11117,12 +11104,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=83, candidate_x=array([ 4.7056084 , 16.96406461, 46.46327061]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-66.07341500231269, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 70, 71, 74, 75, 76, 77, 78, 79, 80, 81, 82]), old_indices_discarded=array([69, 72, 73]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=8.82023246797209e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]), model=ScalarModel(intercept=8.027414199827243, linear_terms=array([-0.00102897, -0.02419561, -0.03622744]), square_terms=array([[ 2.42552676e-05,  5.38177718e-05, -2.53591858e-05],
-       [ 5.38177718e-05,  2.01438379e-04,  8.33396860e-05],
-       [-2.53591858e-05,  8.33396860e-05,  2.64129042e-04]]), scale=8.82023246797209e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=83, candidate_x=array([ 4.68234188, 17.38243342, 46.39405324]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-38.48969680510501, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81]), old_indices_discarded=array([69, 70, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=3.5450224496764184e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 71, 72, 73, 74, 75, 76, 77, 78, 80, 81, 83]), model=ScalarModel(intercept=8.147554750595367, linear_terms=array([-0.00159076,  0.00417843,  0.00924053]), square_terms=array([[ 6.83291271e-07, -8.56186462e-07, -2.31058424e-06],
+       [-8.56186462e-07,  3.07588931e-06,  6.47695683e-06],
+       [-2.31058424e-06,  6.47695683e-06,  1.42187064e-05]]), scale=3.5450224496764184e-05, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11204,12 +11191,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=96, candidate_x=array([ 4.70561491, 16.96407982, 46.46326522]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-7.595394058155183, accepted=False, new_indices=array([84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]), old_indices_used=array([47, 82, 83]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=4.410116233986045e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96]), model=ScalarModel(intercept=8.024372977621736, linear_terms=array([-0.00526102,  0.00153115,  0.0022491 ]), square_terms=array([[ 5.44740785e-06, -1.58693656e-06, -2.33103709e-06],
-       [-1.58693656e-06,  4.62306298e-07,  6.79077636e-07],
-       [-2.33103709e-06,  6.79077636e-07,  9.97491140e-07]]), scale=4.410116233986045e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=84, candidate_x=array([ 4.68235345, 17.38245996, 46.3940789 ]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-32.081344014415954, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 71, 72, 73, 74, 75, 76, 77, 78, 80, 81, 83]), old_indices_discarded=array([70, 79, 82]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1.7725112248382092e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96]), model=ScalarModel(intercept=7.9659918794540445, linear_terms=array([ 0.00776068,  0.00257546, -0.00704361]), square_terms=array([[ 2.68382961e-05,  3.03873816e-06, -3.77185131e-05],
+       [ 3.03873816e-06,  1.25203078e-05,  1.75519524e-05],
+       [-3.77185131e-05,  1.75519524e-05,  9.23763419e-05]]), scale=1.7725112248382092e-05, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11291,12 +11278,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=97, candidate_x=array([ 4.70561852, 16.96407392, 46.46325614]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-44.64935808415891, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96]), old_indices_discarded=array([83, 89, 90]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=2.2050581169930224e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 84, 85, 86, 87, 88, 91, 92, 94, 95, 96, 97]), model=ScalarModel(intercept=8.01905391324511, linear_terms=array([-0.00175287,  0.00218755,  0.00187918]), square_terms=array([[ 6.41148513e-06, -9.94820770e-07, -2.64505371e-06],
-       [-9.94820770e-07,  9.10289725e-07,  8.66893346e-07],
-       [-2.64505371e-06,  8.66893346e-07,  1.36686821e-06]]), scale=2.2050581169930224e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=97, candidate_x=array([ 4.68233547, 17.38246976, 46.39412262]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-24.03445085837565, accepted=False, new_indices=array([85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96]), old_indices_used=array([35, 83, 84]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=8.862556124191046e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97]), model=ScalarModel(intercept=7.956925176364839, linear_terms=array([-9.71305412e-03,  1.85514467e-03,  8.41055243e-07]), square_terms=array([[ 3.02586749e-05, -5.78537058e-06, -2.62289149e-09],
+       [-5.78537058e-06,  1.10614712e-06,  5.01489718e-10],
+       [-2.62289149e-09,  5.01489718e-10,  2.27358490e-13]]), scale=8.862556124191046e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11378,13 +11365,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=98, candidate_x=array([ 4.70561573, 16.96407361, 46.46325657]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-57.28762384450054, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 84, 85, 86, 87, 88, 91, 92, 94, 95, 96, 97]), old_indices_discarded=array([89, 90, 93]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1.1025290584965112e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
-       109, 110]), model=ScalarModel(intercept=8.338585459300694, linear_terms=array([-0.05489626,  0.07386089,  0.06429901]), square_terms=array([[ 0.00047613, -0.00062578, -0.00054731],
-       [-0.00062578,  0.00094261,  0.00080316],
-       [-0.00054731,  0.00080316,  0.00068762]]), scale=1.1025290584965112e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=98, candidate_x=array([ 4.68235662, 17.38247268, 46.39411086]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-19.024215329728104, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 85, 86, 87, 88, 89, 91, 92, 93, 94, 96, 97]), old_indices_discarded=array([84, 90, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=4.431278062095523e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([35, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 98]), model=ScalarModel(intercept=7.946525735281458, linear_terms=array([-0.00862113,  0.00513633,  0.0020363 ]), square_terms=array([[ 3.63534002e-05, -2.07381119e-05, -7.23124445e-06],
+       [-2.07381119e-05,  1.18590679e-05,  4.16715133e-06],
+       [-7.23124445e-06,  4.16715133e-06,  1.49966261e-06]]), scale=4.431278062095523e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11466,12 +11452,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=111, candidate_x=array([ 4.70561515, 16.96407436, 46.46325715]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-6.195396287396706, accepted=False, new_indices=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110]), old_indices_used=array([47, 97, 98]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109]), model=ScalarModel(intercept=8.350620664549025, linear_terms=array([-0.00247701, -0.01501306, -0.00433553]), square_terms=array([[1.08883464e-06, 6.60301599e-06, 1.90684379e-06],
-       [6.60301599e-06, 4.00426570e-05, 1.15636691e-05],
-       [1.90684379e-06, 1.15636691e-05, 3.33939985e-06]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=99, candidate_x=array([ 4.68235171, 17.38247218, 46.39411004]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-66.34297076760124, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([35, 85, 86, 87, 88, 89, 91, 93, 94, 96, 97, 98]), old_indices_discarded=array([90, 92, 95]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=2.2156390310477615e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
+       110, 111]), model=ScalarModel(intercept=7.971171404454603, linear_terms=array([ 0.01002922, -0.0552256 , -0.02915647]), square_terms=array([[ 2.61610426e-05, -1.07785362e-04, -5.62842802e-05],
+       [-1.07785362e-04,  6.06829136e-04,  3.20641457e-04],
+       [-5.62842802e-05,  3.20641457e-04,  1.69487179e-04]]), scale=2.2156390310477615e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11553,12 +11540,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=112, candidate_x=array([ 4.70561474, 16.964076  , 46.46325807]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-16.12141212547719, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109]), old_indices_discarded=array([ 97,  98, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47,  99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 112]), model=ScalarModel(intercept=8.321235798781322, linear_terms=array([-0.01138522, -0.08552521, -0.03034865]), square_terms=array([[2.27334709e-05, 1.66963695e-04, 5.98092445e-05],
-       [1.66963695e-04, 1.25141795e-03, 4.44501100e-04],
-       [5.98092445e-05, 4.44501100e-04, 1.58441604e-04]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=112, candidate_x=array([ 4.68234762, 17.3824763 , 46.39411179]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-3.9853524513265657, accepted=False, new_indices=array([100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111]), old_indices_used=array([35, 98, 99]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1.1078195155238807e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111]), model=ScalarModel(intercept=7.953158864050542, linear_terms=array([0.00129181, 0.0007209 , 0.00288726]), square_terms=array([[4.20347064e-07, 2.34393091e-07, 9.38764970e-07],
+       [2.34393091e-07, 1.30702026e-07, 5.23473123e-07],
+       [9.38764970e-07, 5.23473123e-07, 2.09655596e-06]]), scale=1.1078195155238807e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11640,12 +11627,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=113, candidate_x=array([ 4.70561469, 16.96407599, 46.46325811]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-4.523394638219533, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47,  99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 112]), old_indices_discarded=array([ 97,  98, 104, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 101, 102, 103, 105, 106, 107, 108, 109, 112, 113]), model=ScalarModel(intercept=8.300719564203227, linear_terms=array([-0.05191576, -0.12494701, -0.0261863 ]), square_terms=array([[0.00047462, 0.00114204, 0.00023985],
-       [0.00114204, 0.00279795, 0.00058626],
-       [0.00023985, 0.00058626, 0.00012289]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=113, candidate_x=array([ 4.68234747, 17.38247407, 46.39410985]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-124.72975269060092, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111]), old_indices_discarded=array([ 99, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 102, 103, 104, 105, 106, 108, 109, 111, 113]), model=ScalarModel(intercept=7.968038808575076, linear_terms=array([-0.00441212, -0.00134982, -0.00838694]), square_terms=array([[4.51612602e-06, 1.06524155e-06, 8.41507536e-06],
+       [1.06524155e-06, 6.01526003e-07, 2.17469365e-06],
+       [8.41507536e-06, 2.17469365e-06, 1.57829776e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11727,12 +11714,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=114, candidate_x=array([ 4.70561492, 16.96407598, 46.46325795]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.411275742403435, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 101, 102, 103, 105, 106, 107, 108, 109, 112, 113]), old_indices_discarded=array([ 97,  98,  99, 104, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 102, 103, 105, 106, 107, 108, 109, 112, 113, 114]), model=ScalarModel(intercept=8.298889054028262, linear_terms=array([-0.07303588, -0.18962725, -0.0359198 ]), square_terms=array([[0.00094002, 0.00243041, 0.00045976],
-       [0.00243041, 0.00638794, 0.00121604],
-       [0.00045976, 0.00121604, 0.0002323 ]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=114, candidate_x=array([ 4.68234836, 17.38247446, 46.39411171]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-20.00279553367766, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 102, 103, 104, 105, 106, 108, 109, 111, 113]), old_indices_discarded=array([ 99, 107, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 102, 104, 105, 106, 108, 109, 111, 113, 114]), model=ScalarModel(intercept=7.965989202823942, linear_terms=array([-0.00637413, -0.00182018, -0.01041058]), square_terms=array([[1.16978049e-05, 2.40445716e-06, 1.69667530e-05],
+       [2.40445716e-06, 8.05669009e-07, 4.24798314e-06],
+       [1.69667530e-05, 4.24798314e-06, 2.64842168e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11814,12 +11801,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=115, candidate_x=array([ 4.70561489, 16.96407599, 46.46325792]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.2480389552195068, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 102, 103, 105, 106, 107, 108, 109, 112, 113, 114]), old_indices_discarded=array([ 97,  98,  99, 101, 104, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 102, 105, 106, 107, 108, 109, 112, 113, 114, 115]), model=ScalarModel(intercept=8.253871939055436, linear_terms=array([-0.1554071 , -0.14943967, -0.03305483]), square_terms=array([[0.00442347, 0.00417817, 0.00091543],
-       [0.00417817, 0.00421111, 0.00092328],
-       [0.00091543, 0.00092328, 0.00020297]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=115, candidate_x=array([ 4.68234842, 17.38247447, 46.39411168]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-20.24072224820093, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 102, 104, 105, 106, 108, 109, 111, 113, 114]), old_indices_discarded=array([ 99, 103, 107, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 104, 105, 106, 108, 109, 111, 113, 114, 115]), model=ScalarModel(intercept=7.96835442879739, linear_terms=array([-0.00605559, -0.00274328, -0.01067251]), square_terms=array([[1.29695659e-05, 5.32181038e-06, 2.06392958e-05],
+       [5.32181038e-06, 2.21640855e-06, 8.48521432e-06],
+       [2.06392958e-05, 8.48521432e-06, 3.37890890e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11901,12 +11888,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=116, candidate_x=array([ 4.70561537, 16.96407564, 46.46325803]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-2.424741745918107, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 102, 105, 106, 107, 108, 109, 112, 113, 114, 115]), old_indices_discarded=array([ 97,  98,  99, 101, 103, 104, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 102, 105, 106, 108, 109, 112, 113, 114, 115, 116]), model=ScalarModel(intercept=8.276391875287882, linear_terms=array([-0.04330989, -0.19652703, -0.06141882]), square_terms=array([[0.00036656, 0.00138004, 0.00043188],
-       [0.00138004, 0.00718237, 0.00222123],
-       [0.00043188, 0.00222123, 0.00068836]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=116, candidate_x=array([ 4.68234838, 17.38247454, 46.39411169]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-40.223470992231334, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 104, 105, 106, 108, 109, 111, 113, 114, 115]), old_indices_discarded=array([ 99, 102, 103, 107, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 105, 106, 108, 109, 111, 113, 114, 115, 116]), model=ScalarModel(intercept=7.982845846141978, linear_terms=array([ 0.00018666, -0.00062675, -0.00208488]), square_terms=array([[1.42365281e-05, 2.44192427e-06, 1.36842748e-05],
+       [2.44192427e-06, 7.83064203e-07, 2.76243560e-06],
+       [1.36842748e-05, 2.76243560e-06, 1.41995352e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -11988,12 +11975,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=117, candidate_x=array([ 4.70561474, 16.964076  , 46.46325804]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.9671088996282863, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 102, 105, 106, 108, 109, 112, 113, 114, 115, 116]), old_indices_discarded=array([ 97,  98,  99, 101, 103, 104, 107, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 105, 106, 108, 109, 112, 113, 114, 115, 116, 117]), model=ScalarModel(intercept=8.272304320616975, linear_terms=array([-0.0214707 , -0.22170885, -0.07129402]), square_terms=array([[0.00025168, 0.00063019, 0.00019512],
-       [0.00063019, 0.00926462, 0.00294849],
-       [0.00019512, 0.00294849, 0.00094094]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=117, candidate_x=array([ 4.68234781, 17.3824746 , 46.39411179]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-90.69375487033876, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 105, 106, 108, 109, 111, 113, 114, 115, 116]), old_indices_discarded=array([ 99, 102, 103, 104, 107, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 106, 108, 109, 111, 113, 114, 115, 116, 117]), model=ScalarModel(intercept=7.9800824777134265, linear_terms=array([ 0.00219568, -0.0034245 , -0.00362589]), square_terms=array([[ 1.96292052e-05, -3.81765416e-06,  1.22340041e-05],
+       [-3.81765416e-06,  1.04094634e-05,  1.89526035e-06],
+       [ 1.22340041e-05,  1.89526035e-06,  1.46593321e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12075,12 +12062,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=118, candidate_x=array([ 4.70561464, 16.96407601, 46.46325806]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.2809199338412123, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 105, 106, 108, 109, 112, 113, 114, 115, 116, 117]), old_indices_discarded=array([ 97,  98,  99, 101, 102, 103, 104, 107, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 105, 108, 109, 112, 113, 114, 115, 116, 117, 118]), model=ScalarModel(intercept=8.255730261325812, linear_terms=array([ 0.16596183, -0.30916818, -0.03335069]), square_terms=array([[ 0.00640569, -0.01048573, -0.00110487],
-       [-0.01048573,  0.01848595,  0.00198261],
-       [-0.00110487,  0.00198261,  0.00021474]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=118, candidate_x=array([ 4.6823475 , 17.38247495, 46.3941115 ]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-52.79418770741774, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 106, 108, 109, 111, 113, 114, 115, 116, 117]), old_indices_discarded=array([ 99, 102, 103, 104, 105, 107, 110, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 101, 106, 108, 109, 113, 114, 115, 116, 117, 118]), model=ScalarModel(intercept=7.982500408551688, linear_terms=array([-0.00043775, -0.00136318, -0.00369799]), square_terms=array([[ 1.88693491e-05, -3.25666862e-06,  9.61988854e-06],
+       [-3.25666862e-06,  2.26715406e-05,  1.41331526e-05],
+       [ 9.61988854e-06,  1.41331526e-05,  1.89132554e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12162,12 +12149,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=119, candidate_x=array([ 4.70561423, 16.96407598, 46.4632578 ]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.5103600335846652, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 105, 108, 109, 112, 113, 114, 115, 116, 117, 118]), old_indices_discarded=array([ 97,  98,  99, 101, 102, 103, 104, 106, 107, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119]), model=ScalarModel(intercept=8.219426828940188, linear_terms=array([ 0.09170169, -0.27434874,  0.03583534]), square_terms=array([[ 0.00294426, -0.00564056,  0.00040588],
-       [-0.00564056,  0.0147854 , -0.00153793],
-       [ 0.00040588, -0.00153793,  0.00025172]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=119, candidate_x=array([ 4.68234801, 17.38247465, 46.39411177]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-42.94926449140449, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 101, 106, 108, 109, 113, 114, 115, 116, 117, 118]), old_indices_discarded=array([ 99, 102, 103, 104, 105, 107, 110, 111, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 100, 106, 108, 109, 113, 114, 115, 116, 117, 118, 119]), model=ScalarModel(intercept=7.978256870497925, linear_terms=array([-0.00463322, -0.00309187, -0.01011229]), square_terms=array([[4.57465884e-05, 7.08969940e-06, 5.12211243e-05],
+       [7.08969940e-06, 2.62402007e-05, 3.04992389e-05],
+       [5.12211243e-05, 3.04992389e-05, 8.41609013e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12249,12 +12236,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=120, candidate_x=array([ 4.70561435, 16.96407601, 46.46325774]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.9196966412231102, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 108, 109, 112, 113, 114, 115, 116, 117, 118, 119]), old_indices_discarded=array([ 97,  98,  99, 101, 102, 103, 104, 105, 106, 107, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 100, 109, 112, 113, 114, 115, 116, 117, 118, 119, 120]), model=ScalarModel(intercept=8.179137589658655, linear_terms=array([ 0.07766055, -0.16630148, -0.03465362]), square_terms=array([[ 0.0034807 , -0.00269329, -0.00167644],
-       [-0.00269329,  0.00595065,  0.00113562],
-       [-0.00167644,  0.00113562,  0.00083026]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=120, candidate_x=array([ 4.6823483 , 17.38247459, 46.39411172]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-46.26381761495657, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 100, 106, 108, 109, 113, 114, 115, 116, 117, 118, 119]), old_indices_discarded=array([ 99, 101, 102, 103, 104, 105, 107, 110, 111, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 106, 108, 109, 113, 114, 115, 116, 117, 118, 119, 120]), model=ScalarModel(intercept=7.991227441351442, linear_terms=array([0.0251074 , 0.01057272, 0.01015283]), square_terms=array([[1.79720390e-04, 7.01199396e-05, 9.25845703e-05],
+       [7.01199396e-05, 9.03425308e-05, 6.29529776e-05],
+       [9.25845703e-05, 6.29529776e-05, 6.34934723e-05]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12336,12 +12323,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=121, candidate_x=array([ 4.70561423, 16.96407595, 46.46325802]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.9090746171125308, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 100, 109, 112, 113, 114, 115, 116, 117, 118, 119, 120]), old_indices_discarded=array([ 97,  98,  99, 101, 102, 103, 104, 105, 106, 107, 108, 110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 109, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121]), model=ScalarModel(intercept=7.85481526704998, linear_terms=array([ 0.17790452,  0.2820454 , -0.57672785]), square_terms=array([[ 0.00972201,  0.01254853, -0.02583681],
-       [ 0.01254853,  0.01791374, -0.03587294],
-       [-0.02583681, -0.03587294,  0.07436654]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=121, candidate_x=array([ 4.68234702, 17.38247399, 46.39411052]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-11.917272451719347, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 106, 108, 109, 113, 114, 115, 116, 117, 118, 119, 120]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 107, 110, 111, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 108, 109, 113, 114, 115, 116, 117, 118, 119, 120, 121]), model=ScalarModel(intercept=8.020545402155149, linear_terms=array([ 0.02066767, -0.00112339, -0.01913257]), square_terms=array([[ 1.22236024e-04, -3.41627740e-06, -4.28275898e-05],
+       [-3.41627740e-06,  5.25927287e-05,  4.06278706e-05],
+       [-4.28275898e-05,  4.06278706e-05,  1.20355158e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12423,13 +12410,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=122, candidate_x=array([ 4.70561454, 16.96407485, 46.46325878]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.0556646767090903, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 109, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 110,
-       111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=122, candidate_x=array([ 4.68234716, 17.38247437, 46.3941115 ]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-21.65918455396445, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 108, 109, 113, 114, 115, 116, 117, 118, 119, 120, 121]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 110, 111, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 108, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=8.008375407719436, linear_terms=array([-0.08314419, -0.25217334,  0.11702306]), square_terms=array([[ 0.00163403,  0.00492151, -0.00238694],
+       [ 0.00492151,  0.01506857, -0.00732698],
+       [-0.00238694, -0.00732698,  0.00369433]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12511,13 +12497,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=123, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.6065774237517582, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=123, candidate_x=array([ 4.68234806, 17.38247527, 46.39411057]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-0.9277828593620053, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 108, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 109, 110, 111, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123]), model=ScalarModel(intercept=8.0146617270923, linear_terms=array([-0.10338026, -0.04297765,  0.05535851]), square_terms=array([[ 0.00191397,  0.00066374, -0.00105818],
+       [ 0.00066374,  0.00141854, -0.00068539],
+       [-0.00105818, -0.00068539,  0.00081762]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12599,13 +12584,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=124, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.9743933863682503, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=124, candidate_x=array([ 4.68234881, 17.38247457, 46.39411048]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-3.3426211322214994, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124]), model=ScalarModel(intercept=8.02552419168421, linear_terms=array([-0.02276338, -0.02839304,  0.03530554]), square_terms=array([[ 0.0003343 ,  0.0001141 , -0.00036954],
+       [ 0.0001141 ,  0.00088343, -0.00060589],
+       [-0.00036954, -0.00060589,  0.00085142]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12687,13 +12672,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=125, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.0605789137991335, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=125, candidate_x=array([ 4.68234838, 17.38247484, 46.39411013]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-8.832368290918426, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125]), model=ScalarModel(intercept=8.046662719231234, linear_terms=array([-0.01966025,  0.01855393, -0.00495825]), square_terms=array([[ 1.85723432e-04, -1.68123527e-04, -6.84389867e-05],
+       [-1.68123527e-04,  1.25579475e-03, -2.25004876e-04],
+       [-6.84389867e-05, -2.25004876e-04,  2.15172763e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12775,13 +12760,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=126, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.2600482451743429, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=126, candidate_x=array([ 4.68234865, 17.38247369, 46.39411104]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-19.468154760666586, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 126]), model=ScalarModel(intercept=8.0318646465825, linear_terms=array([ 0.15303911, -0.09657362, -0.04753304]), square_terms=array([[ 0.00580411, -0.00218947, -0.00182339],
+       [-0.00218947,  0.00152456,  0.00059628],
+       [-0.00182339,  0.00059628,  0.00074025]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12863,13 +12848,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=127, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.8501854379014441, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=127, candidate_x=array([ 4.682347  , 17.38247472, 46.39411097]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-1.9458599272870365, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 126]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 127]), model=ScalarModel(intercept=8.021829709501638, linear_terms=array([ 0.05222329,  0.02930636, -0.03390473]), square_terms=array([[ 0.00068655,  0.000606  , -0.00046092],
+       [ 0.000606  ,  0.00188856, -0.00066282],
+       [-0.00046092, -0.00066282,  0.00046994]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -12951,13 +12936,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=128, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.4056151424637772, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=128, candidate_x=array([ 4.68234714, 17.38247392, 46.39411134]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-2.467481607018985, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 127]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 128]), model=ScalarModel(intercept=8.010994524000402, linear_terms=array([ 0.12424882, -0.02277004, -0.03941254]), square_terms=array([[ 0.00301828, -0.00085483, -0.00098889],
+       [-0.00085483,  0.00101291,  0.00014894],
+       [-0.00098889,  0.00014894,  0.0005203 ]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13039,13 +13024,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=129, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.6833448065581629, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=129, candidate_x=array([ 4.68234695, 17.38247445, 46.3941111 ]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-2.1496897541972957, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 128]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13127,13 +13112,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=130, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.1693341326959497, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=130, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-5.80440127929566, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13215,13 +13200,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=131, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.618367867031028, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=131, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-8.002292328068195, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13303,13 +13288,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=132, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.22494837613365068, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=132, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-1.0706309488355799, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13391,13 +13376,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=133, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.6948332590091754, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=133, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-3.4080012643584885, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13479,13 +13464,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=134, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.5359054845285062, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=134, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-2.6154088225354815, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13567,14 +13552,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=135, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.2906252711290453, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=135, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-6.377193629110847, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13656,14 +13640,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=136, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.8876392695292357, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=136, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-4.384005717479062, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13745,14 +13728,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=137, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.0804596455483921, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135, 136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=137, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-5.337808169662223, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135,
+       136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13834,14 +13817,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=138, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.8927340827736439, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135, 136, 137]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=138, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-4.399388153248258, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135,
+       136, 137]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -13923,14 +13906,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=139, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-0.5511466528587335, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135, 136, 137, 138]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=139, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-2.690081752538871, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135,
+       136, 137, 138]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14012,14 +13995,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=140, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=47, x=array([ 4.70561459, 16.96407505, 46.4632578 ]), fval=7.724404600954942, rho=-1.4247850194368779, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135, 136, 137, 138, 139]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561459, 16.96407505, 46.4632578 ]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=7.857223080960438, linear_terms=array([0.00928832, 0.10499757, 0.43538396]), square_terms=array([[0.000728  , 0.00052424, 0.00334264],
-       [0.00052424, 0.00256195, 0.00976513],
-       [0.00334264, 0.00976513, 0.04409581]]), scale=1e-06, shift=array([ 4.70561459, 16.96407505, 46.4632578 ])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=140, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=35, x=array([ 4.68234791, 17.38247432, 46.39411084]), fval=7.7249497274541925, rho=-7.067482543679271, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135,
+       136, 137, 138, 139]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234791, 17.38247432, 46.39411084]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), model=ScalarModel(intercept=8.023673562035768, linear_terms=array([ 0.0769078 , -0.0079456 , -0.03517091]), square_terms=array([[ 1.46335971e-03,  5.75145705e-05, -5.93966023e-04],
+       [ 5.75145705e-05,  1.17865064e-03, -3.61317673e-04],
+       [-5.93966023e-04, -3.61317673e-04,  4.72644920e-04]]), scale=1e-06, shift=array([ 4.68234791, 17.38247432, 46.39411084])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14101,14 +14084,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=141, candidate_x=array([ 4.70561465, 16.9640749 , 46.46325682]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=0.013536964494243514, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 47, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([ 97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-       110, 111, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
-       134, 135, 136, 137, 138, 139, 140]), step_length=1.0000000029996547e-06, relative_step_length=1.0000000029996547, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 134, 135, 136, 137, 138, 139, 140, 141]), model=ScalarModel(intercept=8.06149887196976, linear_terms=array([-0.5590243 ,  1.27160275,  3.91847412]), square_terms=array([[ 0.06050115, -0.13762125, -0.42408314],
-       [-0.13762125,  0.3130454 ,  0.96465684],
-       [-0.42408314,  0.96465684,  2.97261298]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=141, candidate_x=array([ 4.68234699, 17.38247439, 46.39411122]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=0.05877707683680392, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 35, 114, 115, 116, 117, 119, 120, 121, 123, 124, 125, 129]), old_indices_discarded=array([ 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 118, 122, 126, 127, 128, 130, 131, 132, 133, 134, 135,
+       136, 137, 138, 139, 140]), step_length=1.0000000006025997e-06, relative_step_length=1.0000000006025997, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 137, 138, 139, 140, 141]), model=ScalarModel(intercept=8.08143168138139, linear_terms=array([-0.15319259,  0.06917791,  0.07202839]), square_terms=array([[ 0.00544743, -0.00145075, -0.00301805],
+       [-0.00145075,  0.00325306, -0.00105223],
+       [-0.00301805, -0.00105223,  0.00314256]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14190,14 +14173,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=142, candidate_x=array([ 4.70561441, 16.96407502, 46.46325585]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-0.18005243247469838, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 134, 135, 136, 137, 138, 139, 140, 141]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 135, 136, 137, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.164758935845795, linear_terms=array([0.10994239, 0.02402506, 0.07428419]), square_terms=array([[2.65490205e-03, 8.17471467e-05, 2.57496815e-04],
-       [8.17471467e-05, 2.02779609e-04, 6.25225274e-04],
-       [2.57496815e-04, 6.25225274e-04, 1.92775270e-03]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=142, candidate_x=array([ 4.6823479 , 17.38247411, 46.39411093]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-2.3286652591320904, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 137, 138, 139, 140, 141]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14279,14 +14262,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=143, candidate_x=array([ 4.70561382, 16.9640747 , 46.46325629]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.202503206630941, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 135, 136, 137, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 136, 137, 138, 139, 140, 141, 142, 143]), model=ScalarModel(intercept=8.148860497225302, linear_terms=array([0.187207  , 0.08814569, 0.08171824]), square_terms=array([[0.00728267, 0.00251683, 0.00076182],
-       [0.00251683, 0.00121874, 0.00107815],
-       [0.00076182, 0.00107815, 0.00217323]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=143, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-2.333609752112349, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14368,14 +14351,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=144, candidate_x=array([ 4.7056138 , 16.96407453, 46.46325643]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.2133196257898786, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 136, 137, 138, 139, 140, 141, 142, 143]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 137, 138, 139, 140, 141, 142, 143, 144]), model=ScalarModel(intercept=8.155388091733592, linear_terms=array([0.17017583, 0.07067601, 0.08758009]), square_terms=array([[0.00666352, 0.00206706, 0.00057631],
-       [0.00206706, 0.00079034, 0.00071039],
-       [0.00057631, 0.00071039, 0.00228837]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=144, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.921281913850146, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14457,14 +14440,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=145, candidate_x=array([ 4.70561382, 16.96407458, 46.46325637]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.6412208832639814, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 137, 138, 139, 140, 141, 142, 143, 144]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 138, 139, 140, 141, 142, 143, 144, 145]), model=ScalarModel(intercept=8.154378536523849, linear_terms=array([0.1514612 , 0.0430165 , 0.08027159]), square_terms=array([[6.11099166e-03, 1.44644657e-03, 1.34613783e-06],
-       [1.44644657e-03, 3.93524632e-04, 1.64318773e-04],
-       [1.34613783e-06, 1.64318773e-04, 2.14884486e-03]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=145, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.870947994454552, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14546,14 +14529,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=146, candidate_x=array([ 4.7056138 , 16.96407467, 46.46325635]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.333263220338381, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 138, 139, 140, 141, 142, 143, 144, 145]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=146, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-6.04922915081226, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14635,14 +14618,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=147, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.502273956646292, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=147, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.4123861468494865, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14724,15 +14707,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=148, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.9761623665528454, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=148, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-6.813282686092421, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14814,15 +14796,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=149, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.4968576018768154, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=149, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.7333581547667745, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14904,15 +14885,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=150, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.388743002134334, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=150, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.460943274880049, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -14994,15 +14974,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=151, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.8415624114490758, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=151, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.206044730675779, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15084,15 +15064,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=152, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.3405828490281153, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=152, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.023927126602242, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15174,15 +15154,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=153, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-4.35214351659552, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=153, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-9.984145111333305, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15264,15 +15244,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=154, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.3781078239822704, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=154, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.739154302390087, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15354,15 +15334,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=155, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.1294348680302044, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=155, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.109053624771362, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15444,15 +15424,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=156, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.4788552410275997, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=156, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.382040636660244, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15534,15 +15514,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=157, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.8293094663371214, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=157, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.192035921420993, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15624,15 +15604,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=158, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.225643824594799, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=158, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.0554340867341425, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15714,15 +15694,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=159, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.1972850018569776, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=159, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.316340945856832, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15804,15 +15784,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=160, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.6218489419725652, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=160, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-8.284106646172729, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15894,16 +15874,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=161, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.6424520718593096, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=161, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-8.306281273437465, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -15985,16 +15964,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=162, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.2931400616521334, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=162, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.559101787647588, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16076,16 +16054,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=163, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.5678769400874786, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=163, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.837125538785417, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16167,16 +16144,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=164, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-4.241302198289405, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=164, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-9.718304929566266, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16258,16 +16235,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=165, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.997995258791049, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=165, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-6.837878744282935, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16349,16 +16326,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=166, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.021006739578344, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=166, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.574355925268063, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16440,16 +16417,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=167, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.5920160820768656, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=167, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.642299098917888, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16531,16 +16508,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=168, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.092955399921348, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=168, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.733371767337074, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16622,16 +16599,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=169, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.4660126497864314, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=169, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.644332194652515, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16713,16 +16690,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=170, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.33485035555571, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=170, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.616756174364399, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16804,16 +16781,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=171, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.6134463292353423, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=171, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-6.023446547837783, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16895,16 +16872,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=172, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-0.24826904914756806, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=172, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-0.4927441097777205, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -16986,16 +16963,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=173, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.9661140254579133, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=173, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-9.070187909798488, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17077,17 +17054,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=174, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.539553986164235, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=174, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.504737121260635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17169,17 +17145,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=175, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.041725064886811, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=175, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-2.371428577306904, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17261,17 +17236,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=176, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.3403774420251264, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=176, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.336163155862589, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17353,17 +17327,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=177, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.7318968268456154, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=177, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-6.215109327515015, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17445,17 +17419,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=178, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.2100175877167314, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=178, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-7.326137665044721, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17537,17 +17511,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=179, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.3818062977215506, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=179, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-3.1083564942403825, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17629,17 +17603,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=180, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.2481907423851633, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178, 179]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=180, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.144859077433388, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178, 179]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17721,17 +17695,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=181, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-2.1979708169657926, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178, 179, 180]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=181, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-5.050534542594225, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178, 179, 180]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17813,17 +17787,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=182, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.976007480516852, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178, 179, 180, 181]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=182, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.489435544148635, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178, 179, 180, 181]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17905,17 +17879,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=183, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-3.6051729641802894, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178, 179, 180, 181, 182]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.70561465, 16.9640749 , 46.46325682]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), model=ScalarModel(intercept=8.164766594958499, linear_terms=array([0.12274657, 0.04158108, 0.08752848]), square_terms=array([[0.00430494, 0.00124779, 0.00020766],
-       [0.00124779, 0.00039842, 0.00019978],
-       [0.00020766, 0.00019978, 0.00213258]]), scale=1e-06, shift=array([ 4.70561465, 16.9640749 , 46.46325682])), vector_model=VectorModel(intercepts=array([0.02981886, 0.07291964, 0.08030304, 0.12370277, 0.1653629 ,
-       0.22134331, 0.29071661, 0.63424491, 0.74923248, 1.08347698,
-       1.10107736, 1.47544397, 0.84130638, 0.78666266, 0.70333061,
-       0.58861055, 0.51169329]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=183, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-8.279516471817773, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
+       112, 113, 114, 115, 116, 117, 119, 120, 123, 124, 125, 126, 131,
+       132, 133, 134, 135, 136, 137, 143, 144, 145, 146, 147, 148, 149,
+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
+       163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,
+       176, 177, 178, 179, 180, 181, 182]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.68234699, 17.38247439, 46.39411122]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), model=ScalarModel(intercept=8.057629697616091, linear_terms=array([0.05796091, 0.02962913, 0.00854899]), square_terms=array([[ 0.00279336, -0.00187279,  0.00083096],
+       [-0.00187279,  0.00348785, -0.00149469],
+       [ 0.00083096, -0.00149469,  0.0010799 ]]), scale=1e-06, shift=array([ 4.68234699, 17.38247439, 46.39411122])), vector_model=VectorModel(intercepts=array([0.03178805, 0.07741475, 0.0872077 , 0.13302435, 0.17623484,
+       0.23429958, 0.30576365, 0.65554438, 0.77144606, 1.1110646 ,
+       1.13483082, 1.52000952, 0.76918844, 0.72816377, 0.65064274,
+       0.54716606, 0.47567473]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -17997,17 +17971,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.624342040168151, shift=array([ 4.49799881, 16.44990466, 46.2434204 ])), candidate_index=184, candidate_x=array([ 4.70561395, 16.96407456, 46.46325618]), index=141, x=array([ 4.70561465, 16.9640749 , 46.46325682]), fval=7.718691297994154, rho=-1.782764051066316, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([105, 109, 111, 123, 139, 140, 141, 142, 143, 144, 145, 146]), old_indices_discarded=array([ 47,  97,  98,  99, 100, 101, 102, 103, 104, 106, 107, 108, 110,
-       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,
-       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
-       147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-       160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172,
-       173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 185 entries., 'history': {'params': [{'CRRA': 4.497998813498684, 'BeqShift': 16.449904656848112, 'BeqFac': 46.24342040168151}, {'CRRA': 2.725481740266993, 'BeqShift': 12.72270357616253, 'BeqFac': 49.60460564415341}, {'CRRA': 8.225199894184266, 'BeqShift': 14.340937894776207, 'BeqFac': 49.79459449356107}, {'CRRA': 8.14508780260925, 'BeqShift': 20.177105737533694, 'BeqFac': 46.63833846775536}, {'CRRA': 5.697213871559821, 'BeqShift': 20.177105737533694, 'BeqFac': 42.68401645432884}, {'CRRA': 4.320475839845306, 'BeqShift': 19.917861322269456, 'BeqFac': 49.97062148236709}, {'CRRA': 7.867262047690456, 'BeqShift': 20.177105737533694, 'BeqFac': 49.47643682518483}, {'CRRA': 2.0097973845108674, 'BeqShift': 20.177105737533694, 'BeqFac': 47.743021342008234}, {'CRRA': 2.0, 'BeqShift': 15.507088453629825, 'BeqFac': 49.86590436822275}, {'CRRA': 7.616691453453694, 'BeqShift': 17.392134059472802, 'BeqFac': 42.51621932099593}, {'CRRA': 5.002785189877985, 'BeqShift': 12.72270357616253, 'BeqFac': 42.688963288278025}, {'CRRA': 2.1708146709693863, 'BeqShift': 20.177105737533694, 'BeqFac': 43.12295749735683}, {'CRRA': 8.225199894184266, 'BeqShift': 12.865535130923446, 'BeqFac': 48.13645283403052}, {'CRRA': 5.146523432091769, 'BeqShift': 12.72270357616253, 'BeqFac': 42.51621932099593}, {'CRRA': 5.060041550766472, 'BeqShift': 15.02502269564325, 'BeqFac': 44.33684394345911}, {'CRRA': 5.288356518436867, 'BeqShift': 15.644323489431292, 'BeqFac': 45.992606950133634}, {'CRRA': 4.973918628791782, 'BeqShift': 16.669085644476336, 'BeqFac': 45.99930270321213}, {'CRRA': 4.07411602465922, 'BeqShift': 16.245824065044296, 'BeqFac': 46.57928930468828}, {'CRRA': 3.934197231421301, 'BeqShift': 16.568931307516102, 'BeqFac': 46.197661696388536}, {'CRRA': 4.275126151735581, 'BeqShift': 16.93526685687272, 'BeqFac': 46.46451933442022}, {'CRRA': 5.012634337789958, 'BeqShift': 16.297233483174914, 'BeqFac': 46.457838722957895}, {'CRRA': 4.224885890886242, 'BeqShift': 16.459320390681384, 'BeqFac': 45.73405402853664}, {'CRRA': 4.8088540306397425, 'BeqShift': 16.83227945703964, 'BeqFac': 46.545566016885445}, {'CRRA': 4.4801398100080085, 'BeqShift': 16.931761606874883, 'BeqFac': 45.924628365492545}, {'CRRA': 4.273905540044453, 'BeqShift': 15.96827929087305, 'BeqFac': 46.01548918777204}, {'CRRA': 4.5112755773962, 'BeqShift': 16.417757891807785, 'BeqFac': 46.82041584331874}, {'CRRA': 4.572450844546997, 'BeqShift': 15.914924247657249, 'BeqFac': 46.44929969516934}, {'CRRA': 4.745599114964927, 'BeqShift': 16.249113886627253, 'BeqFac': 45.76122688872201}, {'CRRA': 4.7160682025182865, 'BeqShift': 16.969577836161843, 'BeqFac': 46.4769232146932}, {'CRRA': 5.032021596867327, 'BeqShift': 18.024771482538164, 'BeqFac': 46.94434338261181}, {'CRRA': 4.9641153021135285, 'BeqShift': 17.54464666638368, 'BeqFac': 46.46229468549674}, {'CRRA': 4.971075579038686, 'BeqShift': 17.013912048130152, 'BeqFac': 46.616915964497856}, {'CRRA': 4.62004715205243, 'BeqShift': 17.005873423830113, 'BeqFac': 46.37520828290948}, {'CRRA': 4.678685916308889, 'BeqShift': 16.956502975810015, 'BeqFac': 46.537358696785}, {'CRRA': 4.748949294503104, 'BeqShift': 16.9824877534617, 'BeqFac': 46.4693484854585}, {'CRRA': 4.705630902110383, 'BeqShift': 16.9554110914229, 'BeqFac': 46.48100589495773}, {'CRRA': 4.708304859403353, 'BeqShift': 16.9858814311731, 'BeqFac': 46.47739804922683}, {'CRRA': 4.731725886370099, 'BeqShift': 16.969216029655755, 'BeqFac': 46.48592367062023}, {'CRRA': 4.710904975162749, 'BeqShift': 16.960694010462166, 'BeqFac': 46.462066536105496}, {'CRRA': 4.715678991920344, 'BeqShift': 16.95290618163614, 'BeqFac': 46.46998011560622}, {'CRRA': 4.720388240449541, 'BeqShift': 16.958425040131196, 'BeqFac': 46.49046037050293}, {'CRRA': 4.703517491103345, 'BeqShift': 16.976485494778238, 'BeqFac': 46.46592021192742}, {'CRRA': 4.723920604127898, 'BeqShift': 16.976719858708243, 'BeqFac': 46.462307008709644}, {'CRRA': 4.71096714346186, 'BeqShift': 16.976668909552714, 'BeqFac': 46.49273454130148}, {'CRRA': 4.703693983846474, 'BeqShift': 16.965930282627255, 'BeqFac': 46.4642789970456}, {'CRRA': 4.731234098548995, 'BeqShift': 16.96212175568702, 'BeqFac': 46.47054319619916}, {'CRRA': 4.725174446526864, 'BeqShift': 16.984239484374143, 'BeqFac': 46.48225374620521}, {'CRRA': 4.705614590804665, 'BeqShift': 16.964075045284286, 'BeqFac': 46.46325780327809}, {'CRRA': 4.709448101086718, 'BeqShift': 16.961693812595087, 'BeqFac': 46.42740855552399}, {'CRRA': 4.693668584323472, 'BeqShift': 16.954944101802006, 'BeqFac': 46.45324676063907}, {'CRRA': 4.71175706874194, 'BeqShift': 16.968352539139037, 'BeqFac': 46.46831236109496}, {'CRRA': 4.7087287792678625, 'BeqShift': 16.965225821463903, 'BeqFac': 46.46631908247198}, {'CRRA': 4.706053763619456, 'BeqShift': 16.965017621612436, 'BeqFac': 46.46125352071815}, {'CRRA': 4.704968490109904, 'BeqShift': 16.963421623490543, 'BeqFac': 46.46391370854611}, {'CRRA': 4.705685458178461, 'BeqShift': 16.96410685203744, 'BeqFac': 46.46381692814162}, {'CRRA': 4.7056920618036555, 'BeqShift': 16.96449860587302, 'BeqFac': 46.463622836344015}, {'CRRA': 4.705933026638426, 'BeqShift': 16.963646682322725, 'BeqFac': 46.46344153748877}, {'CRRA': 4.70516852607237, 'BeqShift': 16.964366009105156, 'BeqFac': 46.46344494166747}, {'CRRA': 4.705180330276757, 'BeqShift': 16.96427173711657, 'BeqFac': 46.46356010211078}, {'CRRA': 4.70590683979315, 'BeqShift': 16.963677844538697, 'BeqFac': 46.46298307329489}, {'CRRA': 4.705472880079565, 'BeqShift': 16.963642883575474, 'BeqFac': 46.462923425056516}, {'CRRA': 4.705896769287223, 'BeqShift': 16.963741888312878, 'BeqFac': 46.46289998180917}, {'CRRA': 4.705214492966383, 'BeqShift': 16.96447315256638, 'BeqFac': 46.46324848775461}, {'CRRA': 4.705278793263317, 'BeqShift': 16.96438206804684, 'BeqFac': 46.46292369027067}, {'CRRA': 4.705094570694985, 'BeqShift': 16.964128030248066, 'BeqFac': 46.46347093738229}, {'CRRA': 4.705170040824431, 'BeqShift': 16.964420380023633, 'BeqFac': 46.46329991859723}, {'CRRA': 4.705820451134196, 'BeqShift': 16.96398707605927, 'BeqFac': 46.46377600922885}, {'CRRA': 4.705790840039498, 'BeqShift': 16.96412186495598, 'BeqFac': 46.46304237871502}, {'CRRA': 4.7056915464835445, 'BeqShift': 16.964058154158145, 'BeqFac': 46.46314072032652}, {'CRRA': 4.705608722453343, 'BeqShift': 16.96403827503594, 'BeqFac': 46.463197865916285}, {'CRRA': 4.705581177419813, 'BeqShift': 16.964096841673364, 'BeqFac': 46.46319960156689}, {'CRRA': 4.705666717010895, 'BeqShift': 16.964028871649106, 'BeqFac': 46.46324640897737}, {'CRRA': 4.705593409092042, 'BeqShift': 16.96403976632823, 'BeqFac': 46.46331512435938}, {'CRRA': 4.705601580742571, 'BeqShift': 16.96400569389777, 'BeqFac': 46.46325748780468}, {'CRRA': 4.705633688489278, 'BeqShift': 16.964122662269986, 'BeqFac': 46.46330624785638}, {'CRRA': 4.705644128788719, 'BeqShift': 16.964124142311668, 'BeqFac': 46.463216621177}, {'CRRA': 4.705587921439082, 'BeqShift': 16.964140296575437, 'BeqFac': 46.46325464258308}, {'CRRA': 4.705562749051342, 'BeqShift': 16.9640973991998, 'BeqFac': 46.4633001318869}, {'CRRA': 4.705681957194596, 'BeqShift': 16.964095874961604, 'BeqFac': 46.46325518219031}, {'CRRA': 4.705655900947669, 'BeqShift': 16.964056155239803, 'BeqFac': 46.463311799707185}, {'CRRA': 4.705547310467153, 'BeqShift': 16.964056471976267, 'BeqFac': 46.463247441595605}, {'CRRA': 4.705658943755349, 'BeqShift': 16.964106741391557, 'BeqFac': 46.463213002066055}, {'CRRA': 4.705606726888067, 'BeqShift': 16.964043208162348, 'BeqFac': 46.463244791725714}, {'CRRA': 4.705608399126551, 'BeqShift': 16.96406461468349, 'BeqFac': 46.46327061154465}, {'CRRA': 4.705621120000961, 'BeqShift': 16.964080952384247, 'BeqFac': 46.46325832506978}, {'CRRA': 4.7056123899525755, 'BeqShift': 16.964082760240917, 'BeqFac': 46.463254138282174}, {'CRRA': 4.705611382056321, 'BeqShift': 16.964076496890975, 'BeqFac': 46.46324971666738}, {'CRRA': 4.705606117635732, 'BeqShift': 16.96407721369465, 'BeqFac': 46.46325894341207}, {'CRRA': 4.7056146552167135, 'BeqShift': 16.96406656037704, 'BeqFac': 46.46326021132729}, {'CRRA': 4.705619661779661, 'BeqShift': 16.964074113165704, 'BeqFac': 46.46326495959477}, {'CRRA': 4.705620403463393, 'BeqShift': 16.964076732769083, 'BeqFac': 46.46325138752448}, {'CRRA': 4.705615255958174, 'BeqShift': 16.964068632482103, 'BeqFac': 46.463251784138635}, {'CRRA': 4.705611126568003, 'BeqShift': 16.964075470529778, 'BeqFac': 46.46326590357015}, {'CRRA': 4.705607491396165, 'BeqShift': 16.964070174137603, 'BeqFac': 46.46325588838129}, {'CRRA': 4.705622216590535, 'BeqShift': 16.964070620063513, 'BeqFac': 46.46325805089023}, {'CRRA': 4.70561373970782, 'BeqShift': 16.964082319647556, 'BeqFac': 46.46326271813047}, {'CRRA': 4.7056149050921805, 'BeqShift': 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{'solution_x': array([ 4.82330528, 21.52272366, 36.05707678]), 'solution_criterion': 7.777182247944023, 'states': [State(trustregion=Region(center=array([ 4.75078971, 14.41694464, 46.50520192]), radius=4.650520192108982, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=8.539217691760555, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.646531825239875, shift=array([ 4.70567639, 16.96622308, 46.46531825])), candidate_index=184, candidate_x=array([ 4.6823461 , 17.38247392, 46.39411109]), index=141, x=array([ 4.68234699, 17.38247439, 46.39411122]), fval=7.7200111553560635, rho=-4.033727062188392, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([118, 121, 122, 127, 128, 129, 130, 138, 139, 140, 141, 142]), old_indices_discarded=array([ 35, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
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+       150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,
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17.382473916774426, 'BeqFac': 46.39411109000904}], 'criterion': [8.140218982858876, 22.370828647495845, 24.900784674195588, 25.529136656721835, 8.330794754723549, 8.011990449804088, 25.529136656721835, 26.902965189728192, 28.283764159321276, 24.687053499961213, 8.510344063324736, 28.283764159321276, 23.54611401082387, 8.491491287950968, 9.28096102259703, 9.99197124397522, 8.066946801033449, 8.00533348481103, 8.200923238243508, 7.850300978331116, 8.162739014986265, 7.9087497261220525, 8.102967791208021, 7.841883789232617, 7.959333216932288, 7.847778180681784, 7.883249270254372, 7.971474700493697, 8.066657471859852, 8.343589985637175, 7.902897717558958, 8.141521643551082, 7.938140261779154, 7.982704412286408, 8.153801534417667, 7.7249497274541925, 8.14129260014515, 8.072392172824758, 7.9316786031099324, 7.985675067813611, 8.307050560829015, 8.307614651071198, 8.312122414901815, 8.30847320729214, 8.308637353220327, 8.308350257818564, 8.307607826811116, 8.310810637885758, 8.307816199142684, 8.307168492771904, 8.311836822951761, 8.310675891063827, 8.312077067983921, 8.085011205587413, 8.041735817351269, 7.80927169683411, 7.809274723556541, 7.809268998351818, 7.809152152436264, 7.809133654051072, 7.809272616240788, 7.80913413738995, 7.80945411448954, 7.809173540233162, 7.809254932574984, 7.8091579335572705, 7.809161530095363, 7.809277867825939, 8.14009730531154, 8.135714822388614, 8.212885567939475, 8.212879033832097, 8.212938845820409, 8.212867934094248, 8.21287723120987, 8.212902999339526, 8.212937400747036, 8.212885321710827, 8.21284650249054, 8.212938916215451, 8.212925675861689, 8.212834428864952, 8.212899512117858, 7.950419550925067, 8.053993101909251, 7.983218016024817, 7.983214402553283, 7.983212606215708, 7.983210748798216, 7.983215281703747, 7.98321816464199, 7.983217586383812, 7.9832161452917525, 7.983213385895906, 7.983211684757234, 7.983219403948549, 7.9832138805507, 7.983211935054066, 7.912773626985095, 8.402306758402728, 7.975172115335465, 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8.230223971586216, 8.188683846053754, 7.94297614744859, 7.996376035759811, 8.05329657154966, 8.202349505598388, 8.266150593432574, 8.267612481719526, 8.21835379856386, 8.104830496522696, 8.360701795878128, 8.170806324291185, 8.02158093002115, 7.9601339915472895, 8.032064235746452, 8.092120369392902, 8.222154731568352, 8.117113927377938, 7.752495888197335, 8.317973921877698, 7.9510650569732615, 7.876350363186919, 8.071803964394052, 8.129749520721182, 8.202995365790775, 7.924933199652802, 8.059192018850913, 8.052973563360885, 8.015982454385007, 8.265847980809395, 7.985939338706451], 'runtime': [0.0, 1.5322700169926975, 1.7228302310104482, 1.9208646310144104, 2.1332449990150053, 2.361938640999142, 2.590823261998594, 2.891958430001978, 3.1140725759905763, 3.340094521001447, 3.611819473997457, 3.8394388669985346, 4.089107859006617, 106.00706634900416, 107.15606766199926, 108.87616579601308, 110.52282074399409, 110.73537979699904, 110.93869999601156, 111.14044919100706, 111.3500367430097, 111.57059045799542, 111.79390888899798, 112.06702691101236, 112.34688970199204, 112.63267803500639, 112.86494285200024, 113.1287798040139, 114.55466434301343, 115.68195928900968, 116.79288036099751, 117.93154299500748, 119.0491025139927, 120.16628614600631, 121.32941477998975, 122.58392835099949, 123.71397000801517, 124.86803827100084, 126.03855085599935, 127.26059040700784, 128.73114981499384, 128.95051324600354, 129.14707206899766, 129.36354031600058, 129.57636769599048, 129.78265580101288, 130.02725651900982, 130.25321358200745, 130.4791061040014, 130.71467895701062, 130.92645657999674, 131.19377070799237, 132.60481945201172, 133.7063623490103, 134.9575633490167, 136.40434595299303, 136.61054131001583, 136.8220401740109, 137.04561336900224, 137.28777280001668, 137.50862265200703, 137.73497709000367, 137.942509763001, 138.14999196500867, 138.3573253430077, 138.5587816509942, 138.79298727901187, 140.10094936800306, 141.23441704799188, 142.40413849698962, 143.92890803300543, 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231.73016448601265, 232.8206941219978, 234.02564773600898, 235.1096605240018, 236.2237832940009, 237.32346751799923, 238.41078376799123, 239.5271930109884, 240.6436982729938, 241.76513809201424, 242.87015024700668, 243.9742569969967, 245.08989404101158, 246.31563435398857], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 20, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 24, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 28, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 32, 33, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107]}, 'multistart_info': {...}}, {'solution_x': array([ 4.54795424, 20.0343194 , 40.45579176]), 'solution_criterion': 7.7732925722040385, 'states': [State(trustregion=Region(center=array([ 4.73735612, 14.6585077 , 46.46528069]), radius=4.6465280693896815, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=8.531678369900794, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.],
        [0., 0., 0.],
-       [0., 0., 0.]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       [0., 0., 0.]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18089,12 +18063,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=0, candidate_x=array([ 4.75078971, 14.41694464, 46.50520192]), index=0, x=array([ 4.75078971, 14.41694464, 46.50520192]), fval=8.539217691760557, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.75078971, 14.41694464, 46.50520192]), radius=4.650520192108982, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=17.650803059681383, linear_terms=array([ 0.65210821, -0.87257426,  2.20657749]), square_terms=array([[ 7.44379823e+00, -3.50607546e-02, -6.02608319e-03],
-       [-3.50607546e-02,  2.17470025e-02, -5.45960326e-02],
-       [-6.02608319e-03, -5.45960326e-02,  1.38659401e-01]]), scale=array([3.24954514, 3.74830057, 3.74830057]), shift=array([ 5.24954514, 14.41694464, 46.50520192])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=0, candidate_x=array([ 4.73735612, 14.6585077 , 46.46528069]), index=0, x=array([ 4.73735612, 14.6585077 , 46.46528069]), fval=8.531678369900796, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.73735612, 14.6585077 , 46.46528069]), radius=4.6465280693896815, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=17.621642663531812, linear_terms=array([ 0.53999   , -0.93989797,  2.29675914]), square_terms=array([[ 7.40284396e+00, -3.87999076e-02, -6.42658205e-03],
+       [-3.87999076e-02,  2.53237502e-02, -6.13323499e-02],
+       [-6.42658205e-03, -6.13323499e-02,  1.50402536e-01]]), scale=array([3.24121953, 3.74508293, 3.74508293]), shift=array([ 5.24121953, 14.6585077 , 46.46528069])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18176,12 +18150,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=13, candidate_x=array([ 4.97754615, 18.1652452 , 42.75690136]), index=13, x=array([ 4.97754615, 18.1652452 , 42.75690136]), fval=8.344651102932957, rho=0.06577596565014257, accepted=True, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=5.305745258261155, relative_step_length=1.1408928547958914, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.97754615, 18.1652452 , 42.75690136]), radius=2.325260096054491, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), model=ScalarModel(intercept=13.67353306947416, linear_terms=array([ 0.38316026, -0.28822751,  1.26708255]), square_terms=array([[ 3.85050190e+00, -8.96342961e-03,  1.33540902e-02],
-       [-8.96342961e-03,  3.05524145e-03, -1.33970101e-02],
-       [ 1.33540902e-02, -1.33970101e-02,  5.89519079e-02]]), scale=2.325260096054491, shift=array([ 4.97754615, 18.1652452 , 42.75690136])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=13, candidate_x=array([ 5.0189675 , 18.40359063, 42.72019776]), index=13, x=array([ 5.0189675 , 18.40359063, 42.72019776]), fval=8.390998557253404, rho=0.04522927554721717, accepted=True, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=5.303828550094531, relative_step_length=1.1414605638638025, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 5.0189675 , 18.40359063, 42.72019776]), radius=2.3232640346948408, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), model=ScalarModel(intercept=13.53319029928541, linear_terms=array([ 0.36340857, -0.34065981,  1.32376146]), square_terms=array([[ 3.83835387, -0.01229648,  0.01315061],
+       [-0.01229648,  0.00432027, -0.01671685],
+       [ 0.01315061, -0.01671685,  0.06501946]]), scale=2.3232640346948408, shift=array([ 5.0189675 , 18.40359063, 42.72019776])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18263,12 +18237,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=14, candidate_x=array([ 4.80932165, 18.67937733, 40.49326713]), index=14, x=array([ 4.80932165, 18.67937733, 40.49326713]), fval=8.21254642610547, rho=0.10297367673957906, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), old_indices_discarded=array([1, 2]), step_length=2.3273743186830953, relative_step_length=1.0009092413498997, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.80932165, 18.67937733, 40.49326713]), radius=4.650520192108982, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  3,  4,  5,  6,  7,  9, 10, 11, 12, 13, 14]), model=ScalarModel(intercept=11.686104925427994, linear_terms=array([ 1.87530742, -0.40639921,  1.95155468]), square_terms=array([[ 7.71326483e+00, -5.79738912e-02,  1.34662245e-01],
-       [-5.79738912e-02,  7.17324151e-03, -3.39337338e-02],
-       [ 1.34662245e-01, -3.39337338e-02,  1.63597302e-01]]), scale=array([3.27881111, 3.74830057, 3.74830057]), shift=array([ 5.27881111, 18.67937733, 40.49326713])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=14, candidate_x=array([ 4.86173073, 18.98093407, 40.47259038]), index=14, x=array([ 4.86173073, 18.98093407, 40.47259038]), fval=8.240081242797332, rho=0.11216726922270909, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), old_indices_discarded=array([1, 2]), step_length=2.3258950483681593, relative_step_length=1.0011324643406982, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.86173073, 18.98093407, 40.47259038]), radius=4.6465280693896815, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13, 14]), model=ScalarModel(intercept=11.357889722839692, linear_terms=array([ 0.56540966, -0.0873625 ,  2.36221211]), square_terms=array([[ 7.74101730e+00, -2.67795813e-02,  1.31359401e-02],
+       [-2.67795813e-02,  4.59740247e-04, -8.83474730e-03],
+       [ 1.31359401e-02, -8.83474730e-03,  2.46937963e-01]]), scale=array([3.30340683, 3.74508293, 3.74508293]), shift=array([ 5.30340683, 18.98093407, 40.47259038])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18350,12 +18324,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=15, candidate_x=array([ 4.56352887, 22.42767789, 36.74496657]), index=15, x=array([ 4.56352887, 22.42767789, 36.74496657]), fval=8.052287356221209, rho=0.07177736756439829, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  3,  4,  5,  6,  7,  9, 10, 11, 12, 13, 14]), old_indices_discarded=array([1, 2, 8]), step_length=5.306592913751121, relative_step_length=1.1410751259085736, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.56352887, 22.42767789, 36.74496657]), radius=2.325260096054491, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 4,  9, 11, 13, 14, 15]), model=ScalarModel(intercept=6.517661873543402, linear_terms=array([-1.277884  , -7.81670453, -4.56062779]), square_terms=array([[ 3.7484396 , -0.034336  ,  0.05173428],
-       [-0.034336  ,  4.92705615,  2.85745246],
-       [ 0.05173428,  2.85745246,  1.65880244]]), scale=2.325260096054491, shift=array([ 4.56352887, 22.42767789, 36.74496657])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=15, candidate_x=array([ 5.07915712, 22.726017  , 36.72750745]), index=14, x=array([ 4.86173073, 18.98093407, 40.47259038]), fval=8.240081242797332, rho=-0.03277413447800563, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13, 14]), old_indices_discarded=array([ 1,  2, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.86173073, 18.98093407, 40.47259038]), radius=2.3232640346948408, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  3,  4,  5,  6,  7,  9, 10, 11, 13, 14, 15]), model=ScalarModel(intercept=11.341042852378065, linear_terms=array([0.18662771, 0.09578451, 1.25479933]), square_terms=array([[ 3.84623835e+00, -1.94700843e-02,  4.38943415e-02],
+       [-1.94700843e-02,  5.63403116e-04,  5.17956219e-03],
+       [ 4.38943415e-02,  5.17956219e-03,  6.99803878e-02]]), scale=2.3232640346948408, shift=array([ 4.86173073, 18.98093407, 40.47259038])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18437,12 +18411,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=16, candidate_x=array([ 5.26109527, 24.62034659, 37.08028674]), index=15, x=array([ 4.56352887, 22.42767789, 36.74496657]), fval=8.052287356221209, rho=-0.16432593238813983, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 4,  9, 11, 13, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.56352887, 22.42767789, 36.74496657]), radius=1.1626300480272456, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), model=ScalarModel(intercept=8.57783936363707, linear_terms=array([-0.35641001,  0.35038103,  0.29856929]), square_terms=array([[1.25168888, 0.02419222, 0.03533201],
-       [0.02419222, 0.00822381, 0.00738837],
-       [0.03533201, 0.00738837, 0.00682487]]), scale=1.1626300480272456, shift=array([ 4.56352887, 22.42767789, 36.74496657])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=16, candidate_x=array([ 4.79512248, 18.80095182, 38.13560716]), index=14, x=array([ 4.86173073, 18.98093407, 40.47259038]), fval=8.240081242797332, rho=-0.12555854101863442, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  3,  4,  5,  6,  7,  9, 10, 11, 13, 14, 15]), old_indices_discarded=array([ 8, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.86173073, 18.98093407, 40.47259038]), radius=1.1616320173474204, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 4,  9, 11, 13, 14, 15, 16]), model=ScalarModel(intercept=10.992615712589451, linear_terms=array([-0.39685921, -3.20130053, -0.80631979]), square_terms=array([[0.93681522, 0.0034882 , 0.00574657],
+       [0.0034882 , 0.47149626, 0.11811585],
+       [0.00574657, 0.11811585, 0.0297366 ]]), scale=1.1616320173474204, shift=array([ 4.86173073, 18.98093407, 40.47259038])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18524,12 +18498,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=29, candidate_x=array([ 4.83391154, 21.55139266, 35.99116682]), index=29, x=array([ 4.83391154, 21.55139266, 35.99116682]), fval=7.929456546242375, rho=0.24136972412526866, accepted=True, new_indices=array([17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]), old_indices_used=array([14, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=1.1870958892825352, relative_step_length=1.0210435308263393, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83391154, 21.55139266, 35.99116682]), radius=2.325260096054491, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([15, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29]), model=ScalarModel(intercept=7.9006854701318385, linear_terms=array([0.06540457, 1.18319519, 0.78600667]), square_terms=array([[4.89627106, 0.11613869, 0.0141607 ],
-       [0.11613869, 0.09228281, 0.05977891],
-       [0.0141607 , 0.05977891, 0.03964013]]), scale=2.325260096054491, shift=array([ 4.83391154, 21.55139266, 35.99116682])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=17, candidate_x=array([ 4.99460701, 20.1047873 , 40.73467194]), index=17, x=array([ 4.99460701, 20.1047873 , 40.73467194]), fval=8.04200686154724, rho=0.06450599135516881, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 4,  9, 11, 13, 14, 15, 16]), old_indices_discarded=array([], dtype=int64), step_length=1.1616320173474206, relative_step_length=1.0000000000000002, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.99460701, 20.1047873 , 40.73467194]), radius=0.5808160086737102, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=8.196818557772174, linear_terms=array([ 0.51665457, -0.01697494,  0.04818148]), square_terms=array([[ 3.20955678e-01, -1.91500463e-02, -3.54993917e-04],
+       [-1.91500463e-02,  1.33311866e-03,  7.43615986e-05],
+       [-3.54993917e-04,  7.43615986e-05,  1.78452371e-04]]), scale=0.5808160086737102, shift=array([ 4.99460701, 20.1047873 , 40.73467194])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18611,12 +18585,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=30, candidate_x=array([ 4.84863986, 19.61454822, 34.7028612 ]), index=29, x=array([ 4.83391154, 21.55139266, 35.99116682]), fval=7.929456546242375, rho=-0.02075680093446248, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29]), old_indices_discarded=array([ 4,  9, 11, 13, 14, 16, 19, 25]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83391154, 21.55139266, 35.99116682]), radius=1.1626300480272456, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29]), model=ScalarModel(intercept=7.953167465299659, linear_terms=array([0.17157003, 0.38824272, 0.40276844]), square_terms=array([[1.22361868, 0.03485472, 0.00786884],
-       [0.03485472, 0.01041676, 0.00999802],
-       [0.00786884, 0.00999802, 0.01033748]]), scale=1.1626300480272456, shift=array([ 4.83391154, 21.55139266, 35.99116682])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=30, candidate_x=array([ 4.46574856, 19.98364351, 40.52735537]), index=30, x=array([ 4.46574856, 19.98364351, 40.52735537]), fval=7.962336599726309, rho=0.2245815571751142, accepted=True, new_indices=array([18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_used=array([13, 14, 17]), old_indices_discarded=array([], dtype=int64), step_length=0.5808160086737101, relative_step_length=0.9999999999999998, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.46574856, 19.98364351, 40.52735537]), radius=1.1616320173474204, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([14, 17, 18, 19, 22, 23, 24, 25, 27, 28, 29, 30]), model=ScalarModel(intercept=7.822680115318414, linear_terms=array([ 0.48036445, -0.12301546,  0.10955979]), square_terms=array([[ 1.31392816e+00, -1.08510611e-01,  6.42437927e-03],
+       [-1.08510611e-01,  1.06086185e-02, -1.15950918e-03],
+       [ 6.42437927e-03, -1.15950918e-03,  7.83404133e-04]]), scale=1.1616320173474204, shift=array([ 4.46574856, 19.98364351, 40.52735537])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18698,12 +18672,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=31, candidate_x=array([ 4.74024164, 20.74590066, 35.14931869]), index=29, x=array([ 4.83391154, 21.55139266, 35.99116682]), fval=7.929456546242375, rho=-0.9883424412910544, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29]), old_indices_discarded=array([14, 16, 19, 20, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83391154, 21.55139266, 35.99116682]), radius=0.5813150240136228, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([15, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31]), model=ScalarModel(intercept=8.283644742286226, linear_terms=array([ 0.39885051, -0.22618848,  0.1350466 ]), square_terms=array([[ 0.32930077, -0.02693564,  0.00103506],
-       [-0.02693564,  0.00480513, -0.0016364 ],
-       [ 0.00103506, -0.0016364 ,  0.00114466]]), scale=0.5813150240136228, shift=array([ 4.83391154, 21.55139266, 35.99116682])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=31, candidate_x=array([ 4.13879884, 20.69671635, 39.63518825]), index=30, x=array([ 4.46574856, 19.98364351, 40.52735537]), fval=7.962336599726309, rho=-3.3871874141223883, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 18, 19, 22, 23, 24, 25, 27, 28, 29, 30]), old_indices_discarded=array([ 4, 11, 13, 15, 16, 20, 21, 26]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.46574856, 19.98364351, 40.52735537]), radius=0.5808160086737102, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([14, 17, 18, 19, 20, 21, 23, 25, 26, 28, 29, 30]), model=ScalarModel(intercept=7.8750158183943535, linear_terms=array([ 0.18290525, -0.03966602,  0.04962236]), square_terms=array([[ 3.18189399e-01, -2.44943855e-02,  2.15005791e-03],
+       [-2.44943855e-02,  2.21832600e-03, -3.31693854e-04],
+       [ 2.15005791e-03, -3.31693854e-04,  2.36647088e-04]]), scale=0.5808160086737102, shift=array([ 4.46574856, 19.98364351, 40.52735537])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18785,12 +18759,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=32, candidate_x=array([ 4.46792034, 21.9905737 , 35.70681603]), index=29, x=array([ 4.83391154, 21.55139266, 35.99116682]), fval=7.929456546242375, rho=-0.38258314201469573, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31]), old_indices_discarded=array([18, 19, 20, 30]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83391154, 21.55139266, 35.99116682]), radius=0.2906575120068114, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([15, 17, 21, 22, 24, 25, 28, 29, 31, 32]), model=ScalarModel(intercept=8.27309064245344, linear_terms=array([ 0.2203074 , -0.09209883,  0.05033552]), square_terms=array([[ 0.07751409, -0.00772011,  0.0019533 ],
-       [-0.00772011,  0.00117773, -0.00039966],
-       [ 0.0019533 , -0.00039966,  0.00018747]]), scale=0.2906575120068114, shift=array([ 4.83391154, 21.55139266, 35.99116682])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=32, candidate_x=array([ 4.20423026, 20.25346659, 40.0505172 ]), index=30, x=array([ 4.46574856, 19.98364351, 40.52735537]), fval=7.962336599726309, rho=-3.3747099580360915, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([14, 17, 18, 19, 20, 21, 23, 25, 26, 28, 29, 30]), old_indices_discarded=array([13, 16, 22, 24, 27, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.46574856, 19.98364351, 40.52735537]), radius=0.2904080043368551, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([17, 18, 19, 20, 21, 23, 25, 26, 28, 29, 30, 32]), model=ScalarModel(intercept=7.975775842312034, linear_terms=array([ 0.04160741,  0.11726395, -0.04856694]), square_terms=array([[ 0.07709858,  0.00346284, -0.00185717],
+       [ 0.00346284,  0.00103692, -0.00041189],
+       [-0.00185717, -0.00041189,  0.00022013]]), scale=0.2904080043368551, shift=array([ 4.46574856, 19.98364351, 40.52735537])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18872,12 +18846,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=33, candidate_x=array([ 4.66057623, 21.74104789, 35.85527144]), index=29, x=array([ 4.83391154, 21.55139266, 35.99116682]), fval=7.929456546242375, rho=-1.508111903062971, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([15, 17, 21, 22, 24, 25, 28, 29, 31, 32]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83391154, 21.55139266, 35.99116682]), radius=0.1453287560034057, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([22, 25, 29, 32, 33]), model=ScalarModel(intercept=8.014191151333087, linear_terms=array([-0.14253123,  0.07943288,  0.26582941]), square_terms=array([[ 0.01574696, -0.00121154, -0.0020692 ],
-       [-0.00121154,  0.00114369,  0.00341495],
-       [-0.0020692 ,  0.00341495,  0.01106896]]), scale=0.1453287560034057, shift=array([ 4.83391154, 21.55139266, 35.99116682])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=33, candidate_x=array([ 4.4120249 , 19.71950113, 40.63656965]), index=30, x=array([ 4.46574856, 19.98364351, 40.52735537]), fval=7.962336599726309, rho=-2.408526841450073, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([17, 18, 19, 20, 21, 23, 25, 26, 28, 29, 30, 32]), old_indices_discarded=array([14, 22, 24, 27, 31]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.46574856, 19.98364351, 40.52735537]), radius=0.14520400216842755, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([17, 19, 20, 21, 23, 26, 28, 30, 32, 33]), model=ScalarModel(intercept=8.098260675983976, linear_terms=array([-0.05415487, -0.02440153, -0.03415609]), square_terms=array([[ 0.01705534, -0.00096788, -0.00045023],
+       [-0.00096788,  0.00024206,  0.00012217],
+       [-0.00045023,  0.00012217,  0.00010367]]), scale=0.14520400216842755, shift=array([ 4.46574856, 19.98364351, 40.52735537])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -18959,12 +18933,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=34, candidate_x=array([ 4.90065205, 21.51823074, 35.86640128]), index=29, x=array([ 4.83391154, 21.55139266, 35.99116682]), fval=7.929456546242375, rho=-0.8104596658606387, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([22, 25, 29, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.83391154, 21.55139266, 35.99116682]), radius=0.07266437800170285, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([29, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=7.9036946959661405, linear_terms=array([ 0.01674871,  0.03922312, -0.08984769]), square_terms=array([[ 0.00455916,  0.0005764 , -0.001633  ],
-       [ 0.0005764 ,  0.00022076, -0.00047225],
-       [-0.001633  , -0.00047225,  0.00123082]]), scale=0.07266437800170285, shift=array([ 4.83391154, 21.55139266, 35.99116682])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=34, candidate_x=array([ 4.5694414 , 20.04424997, 40.61093263]), index=30, x=array([ 4.46574856, 19.98364351, 40.52735537]), fval=7.962336599726309, rho=-1.7155586440005917, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([17, 19, 20, 21, 23, 26, 28, 30, 32, 33]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.46574856, 19.98364351, 40.52735537]), radius=0.07260200108421377, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([30, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=7.925391743016029, linear_terms=array([-0.01046604, -0.04476979,  0.06225747]), square_terms=array([[ 0.00606362, -0.00100946,  0.00158893],
+       [-0.00100946,  0.00038477, -0.00050384],
+       [ 0.00158893, -0.00050384,  0.00077257]]), scale=0.07260200108421377, shift=array([ 4.46574856, 19.98364351, 40.52735537])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19046,12 +19020,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=47, candidate_x=array([ 4.82317079, 21.52250883, 36.05720457]), index=47, x=array([ 4.82317079, 21.52250883, 36.05720457]), fval=7.879941176547239, rho=0.5015667092838586, accepted=True, new_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_used=array([29, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.07287403056726817, relative_step_length=1.002885217920126, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82317079, 21.52250883, 36.05720457]), radius=0.1453287560034057, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([29, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47]), model=ScalarModel(intercept=7.883885069122536, linear_terms=array([ 0.07378839, -0.00167758,  0.00170096]), square_terms=array([[ 2.04811395e-02, -5.67453840e-05,  6.42643019e-05],
-       [-5.67453840e-05,  7.28776765e-07,  2.50683413e-07],
-       [ 6.42643019e-05,  2.50683413e-07,  1.86388105e-06]]), scale=0.1453287560034057, shift=array([ 4.82317079, 21.52250883, 36.05720457])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=47, candidate_x=array([ 4.47689007, 20.02674487, 40.46743013]), index=47, x=array([ 4.47689007, 20.02674487, 40.46743013]), fval=7.8974176203791435, rho=0.8195254297622456, accepted=True, new_indices=array([35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]), old_indices_used=array([30, 33, 34]), old_indices_discarded=array([], dtype=int64), step_length=0.0746518261217632, relative_step_length=1.0282337264391894, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.47689007, 20.02674487, 40.46743013]), radius=0.14520400216842755, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([30, 35, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=7.902116571196435, linear_terms=array([-0.07517237, -0.00175366,  0.00474786]), square_terms=array([[ 1.87972075e-02, -7.62719120e-05,  1.27526617e-04],
+       [-7.62719120e-05,  1.16077161e-06, -1.52037336e-06],
+       [ 1.27526617e-04, -1.52037336e-06,  3.55447516e-06]]), scale=0.14520400216842755, shift=array([ 4.47689007, 20.02674487, 40.46743013])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19133,12 +19107,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=48, candidate_x=array([ 4.67957681, 21.50665962, 36.07301657]), index=47, x=array([ 4.82317079, 21.52250883, 36.05720457]), fval=7.879941176547239, rho=-8.098236357261534, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([29, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47]), old_indices_discarded=array([22, 25, 32, 33, 34, 40, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82317079, 21.52250883, 36.05720457]), radius=0.07266437800170285, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([29, 35, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=7.879475803734024, linear_terms=array([ 0.03772024,  0.00152311, -0.00402758]), square_terms=array([[ 5.19755651e-03,  7.77834300e-05, -1.60456369e-04],
-       [ 7.77834300e-05,  4.24631678e-06, -7.35945087e-06],
-       [-1.60456369e-04, -7.35945087e-06,  1.31480778e-05]]), scale=0.07266437800170285, shift=array([ 4.82317079, 21.52250883, 36.05720457])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=48, candidate_x=array([ 4.61845778, 20.04594408, 40.44146522]), index=47, x=array([ 4.47689007, 20.02674487, 40.46743013]), fval=7.8974176203791435, rho=-7.350216467162828, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([30, 35, 36, 37, 38, 39, 41, 43, 44, 45, 46, 47]), old_indices_discarded=array([17, 19, 20, 21, 23, 26, 28, 32, 33, 34, 40, 42]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.47689007, 20.02674487, 40.46743013]), radius=0.07260200108421377, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([30, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47]), model=ScalarModel(intercept=7.900183028292221, linear_terms=array([-0.03955292, -0.00220656,  0.00429301]), square_terms=array([[ 4.62492409e-03, -4.40756867e-05,  6.76090031e-05],
+       [-4.40756867e-05,  1.21473022e-06, -1.96185203e-06],
+       [ 6.76090031e-05, -1.96185203e-06,  3.53020588e-06]]), scale=0.07260200108421377, shift=array([ 4.47689007, 20.02674487, 40.46743013])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19220,12 +19194,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=49, candidate_x=array([ 4.75059254, 21.52523384, 36.05945932]), index=47, x=array([ 4.82317079, 21.52250883, 36.05720457]), fval=7.879941176547239, rho=-0.5183379249515261, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([29, 35, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47]), old_indices_discarded=array([33, 34, 41, 42, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82317079, 21.52250883, 36.05720457]), radius=0.036332189000851424, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([29, 35, 36, 37, 39, 40, 43, 44, 45, 46, 47, 49]), model=ScalarModel(intercept=7.890206169486809, linear_terms=array([ 0.01321359, -0.00145856,  0.00155507]), square_terms=array([[ 1.14211716e-03, -1.05566176e-05, -8.95447201e-06],
-       [-1.05566176e-05,  1.75012580e-06, -5.07952886e-06],
-       [-8.95447201e-06, -5.07952886e-06,  1.81095317e-05]]), scale=0.036332189000851424, shift=array([ 4.82317079, 21.52250883, 36.05720457])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=49, candidate_x=array([ 4.54810503, 20.0344787 , 40.455612  ]), index=49, x=array([ 4.54810503, 20.0344787 , 40.455612  ]), fval=7.895353314082556, rho=0.05501662636305402, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([30, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47]), old_indices_discarded=array([33, 34, 35, 40, 48]), step_length=0.0726020010842145, relative_step_length=1.00000000000001, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54810503, 20.0344787 , 40.455612  ]), radius=0.03630100054210689, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([30, 34, 36, 37, 38, 41, 42, 45, 46, 47, 48, 49]), model=ScalarModel(intercept=8.053474787129238, linear_terms=array([ 0.05269853,  0.04041517, -0.01070785]), square_terms=array([[ 3.24102882e-03,  8.80008479e-04, -2.18594542e-04],
+       [ 8.80008479e-04,  2.98823350e-04, -6.90036458e-05],
+       [-2.18594542e-04, -6.90036458e-05,  4.04428180e-05]]), scale=0.03630100054210689, shift=array([ 4.54810503, 20.0344787 , 40.455612  ])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19307,12 +19281,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=50, candidate_x=array([ 4.78741613, 21.52515831, 36.05132131]), index=47, x=array([ 4.82317079, 21.52250883, 36.05720457]), fval=7.879941176547239, rho=-37.526290228226614, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([29, 35, 36, 37, 39, 40, 43, 44, 45, 46, 47, 49]), old_indices_discarded=array([38, 41, 42, 48]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82317079, 21.52250883, 36.05720457]), radius=0.018166094500425712, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([29, 35, 37, 40, 43, 47, 49, 50]), model=ScalarModel(intercept=7.951936719462296, linear_terms=array([-0.01394112, -0.00032375,  0.00754414]), square_terms=array([[ 1.15931945e-04,  4.50527165e-06, -1.05836102e-05],
-       [ 4.50527165e-06,  7.93370633e-06, -2.30704529e-05],
-       [-1.05836102e-05, -2.30704529e-05,  9.17108358e-05]]), scale=0.018166094500425712, shift=array([ 4.82317079, 21.52250883, 36.05720457])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=50, candidate_x=array([ 4.52457003, 20.00863284, 40.46540272]), index=49, x=array([ 4.54810503, 20.0344787 , 40.455612  ]), fval=7.895353314082556, rho=-6.421038321070497, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([30, 34, 36, 37, 38, 41, 42, 45, 46, 47, 48, 49]), old_indices_discarded=array([39, 40, 43, 44]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54810503, 20.0344787 , 40.455612  ]), radius=0.018150500271053444, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([42, 45, 46, 47, 48, 49, 50]), model=ScalarModel(intercept=8.107171200041613, linear_terms=array([ 0.04770602,  0.00660652, -0.03405234]), square_terms=array([[ 1.25023165e-03,  5.91636099e-05, -5.22002658e-04],
+       [ 5.91636099e-05,  1.25327249e-05, -2.66728376e-05],
+       [-5.22002658e-04, -2.66728376e-05,  2.42770229e-04]]), scale=0.018150500271053444, shift=array([ 4.54810503, 20.0344787 , 40.455612  ])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19394,12 +19368,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=51, candidate_x=array([ 4.83913269, 21.52280876, 36.04853654]), index=47, x=array([ 4.82317079, 21.52250883, 36.05720457]), fval=7.879941176547239, rho=-26.373586462951504, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([29, 35, 37, 40, 43, 47, 49, 50]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82317079, 21.52250883, 36.05720457]), radius=0.009083047250212856, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([40, 47, 50, 51]), model=ScalarModel(intercept=7.879941176547236, linear_terms=array([-0.02018705,  0.33337256, -0.44563768]), square_terms=array([[ 5.30241440e-05, -3.92722031e-04,  5.67351673e-04],
-       [-3.92722031e-04,  1.93946944e-02, -2.59279288e-02],
-       [ 5.67351673e-04, -2.59279288e-02,  3.47426605e-02]]), scale=0.009083047250212856, shift=array([ 4.82317079, 21.52250883, 36.05720457])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=51, candidate_x=array([ 4.53246884, 20.03367043, 40.46479333]), index=49, x=array([ 4.54810503, 20.0344787 , 40.455612  ]), fval=7.895353314082556, rho=-5.563306614860443, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([42, 45, 46, 47, 48, 49, 50]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54810503, 20.0344787 , 40.455612  ]), radius=0.009075250135526722, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), model=ScalarModel(intercept=8.08842957117924, linear_terms=array([-0.04374547, -0.03048344,  0.01980434]), square_terms=array([[ 1.64277851e-04,  1.45012664e-04, -9.95754018e-05],
+       [ 1.45012664e-04,  1.71996614e-04, -1.04814280e-04],
+       [-9.95754018e-05, -1.04814280e-04,  7.53980189e-05]]), scale=0.009075250135526722, shift=array([ 4.54810503, 20.0344787 , 40.455612  ])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19481,12 +19455,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=52, candidate_x=array([ 4.82222293, 21.51864016, 36.0653677 ]), index=47, x=array([ 4.82317079, 21.52250883, 36.05720457]), fval=7.879941176547239, rho=-0.5637650627768772, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([40, 47, 50, 51]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82317079, 21.52250883, 36.05720457]), radius=0.004541523625106428, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), model=ScalarModel(intercept=8.21222178187958, linear_terms=array([ 0.01289879,  0.004231  , -0.01359205]), square_terms=array([[ 1.04943250e-04, -2.45113726e-06, -3.12177384e-05],
-       [-2.45113726e-06,  9.88263228e-06, -1.71193655e-05],
-       [-3.12177384e-05, -1.71193655e-05,  4.36418526e-05]]), scale=0.004541523625106428, shift=array([ 4.82317079, 21.52250883, 36.05720457])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=64, candidate_x=array([ 4.55512722, 20.03925026, 40.45240557]), index=49, x=array([ 4.54810503, 20.0344787 , 40.455612  ]), fval=7.895353314082556, rho=-3.802534324582633, accepted=False, new_indices=array([52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]), old_indices_used=array([49, 50, 51]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54810503, 20.0344787 , 40.455612  ]), radius=0.004537625067763361, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([49, 52, 53, 54, 55, 56, 57, 58, 59, 62, 63, 64]), model=ScalarModel(intercept=8.092949984076341, linear_terms=array([-0.00250844, -0.00270127,  0.00458848]), square_terms=array([[ 1.42343905e-05, -3.00940691e-06,  5.22934660e-06],
+       [-3.00940691e-06,  1.58818059e-06, -2.70264645e-06],
+       [ 5.22934660e-06, -2.70264645e-06,  4.61235222e-06]]), scale=0.004537625067763361, shift=array([ 4.54810503, 20.0344787 , 40.455612  ])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19568,12 +19542,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=65, candidate_x=array([ 4.82007908, 21.52145349, 36.06035941]), index=47, x=array([ 4.82317079, 21.52250883, 36.05720457]), fval=7.879941176547239, rho=-18.781530529539378, accepted=False, new_indices=array([53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]), old_indices_used=array([47, 51, 52]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82317079, 21.52250883, 36.05720457]), radius=0.002270761812553214, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65]), model=ScalarModel(intercept=8.209938412401922, linear_terms=array([-0.00086086,  0.00116155,  0.00054634]), square_terms=array([[2.35499262e-06, 4.36996529e-07, 2.10532813e-07],
-       [4.36996529e-07, 2.16221227e-07, 1.04624596e-07],
-       [2.10532813e-07, 1.04624596e-07, 5.27725691e-08]]), scale=0.002270761812553214, shift=array([ 4.82317079, 21.52250883, 36.05720457])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=65, candidate_x=array([ 4.55004065, 20.03656242, 40.45207249]), index=49, x=array([ 4.54810503, 20.0344787 , 40.455612  ]), fval=7.895353314082556, rho=-49.414150985134675, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 52, 53, 54, 55, 56, 57, 58, 59, 62, 63, 64]), old_indices_discarded=array([51, 60, 61]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54810503, 20.0344787 , 40.455612  ]), radius=0.0022688125338816804, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([49, 52, 53, 54, 55, 56, 57, 58, 59, 62, 63, 65]), model=ScalarModel(intercept=8.101810233683992, linear_terms=array([ 0.00030672,  0.00289596, -0.00259533]), square_terms=array([[ 6.08829966e-06,  4.34999746e-06, -3.71631326e-06],
+       [ 4.34999746e-06,  6.90416114e-06, -5.74948746e-06],
+       [-3.71631326e-06, -5.74948746e-06,  4.79698074e-06]]), scale=0.0022688125338816804, shift=array([ 4.54810503, 20.0344787 , 40.455612  ])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19655,12 +19629,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=66, candidate_x=array([ 4.82443512, 21.52080102, 36.05640129]), index=47, x=array([ 4.82317079, 21.52250883, 36.05720457]), fval=7.879941176547239, rho=-75.09062163433572, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65]), old_indices_discarded=array([52, 53, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82317079, 21.52250883, 36.05720457]), radius=0.001135380906276607, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 54, 55, 57, 58, 60, 61, 62, 63, 64, 65, 66]), model=ScalarModel(intercept=8.190342304634058, linear_terms=array([-0.00390451,  0.00354355,  0.00036841]), square_terms=array([[ 1.23702374e-06, -1.30688822e-06, -1.62137485e-07],
-       [-1.30688822e-06,  1.91195680e-06,  2.05466792e-07],
-       [-1.62137485e-07,  2.05466792e-07,  4.95959065e-08]]), scale=0.001135380906276607, shift=array([ 4.82317079, 21.52250883, 36.05720457])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=66, candidate_x=array([ 4.54800832, 20.03271887, 40.4570407 ]), index=49, x=array([ 4.54810503, 20.0344787 , 40.455612  ]), fval=7.895353314082556, rho=-16.567699509062333, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 52, 53, 54, 55, 56, 57, 58, 59, 62, 63, 65]), old_indices_discarded=array([60, 61, 64]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54810503, 20.0344787 , 40.455612  ]), radius=0.0011344062669408402, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([49, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]), model=ScalarModel(intercept=8.053761997460354, linear_terms=array([ 0.00527287,  0.02140746, -0.02223704]), square_terms=array([[ 6.31816876e-05,  1.75667115e-05, -7.22528396e-05],
+       [ 1.75667115e-05,  8.00018077e-05, -7.34072987e-05],
+       [-7.22528396e-05, -7.34072987e-05,  1.21034851e-04]]), scale=0.0011344062669408402, shift=array([ 4.54810503, 20.0344787 , 40.455612  ])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19742,12 +19716,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=67, candidate_x=array([ 4.82401168, 21.5217503 , 36.05712312]), index=47, x=array([ 4.82317079, 21.52250883, 36.05720457]), fval=7.879941176547239, rho=-45.159106704858246, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 54, 55, 57, 58, 60, 61, 62, 63, 64, 65, 66]), old_indices_discarded=array([53, 56, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82317079, 21.52250883, 36.05720457]), radius=0.0005676904531383035, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79]), model=ScalarModel(intercept=8.181621792197582, linear_terms=array([-0.0248101 ,  0.01919921,  0.04027442]), square_terms=array([[ 1.58740953e-04, -1.19396821e-04, -1.72545568e-04],
-       [-1.19396821e-04,  9.37076888e-05,  1.32059282e-04],
-       [-1.72545568e-04,  1.32059282e-04,  2.47238655e-04]]), scale=0.0005676904531383035, shift=array([ 4.82317079, 21.52250883, 36.05720457])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=79, candidate_x=array([ 4.54789945, 20.03372151, 40.45643132]), index=49, x=array([ 4.54810503, 20.0344787 , 40.455612  ]), fval=7.895353314082556, rho=-5.589096352594169, accepted=False, new_indices=array([67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]), old_indices_used=array([49, 65, 66]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54810503, 20.0344787 , 40.455612  ]), radius=0.0005672031334704201, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([49, 67, 68, 70, 71, 72, 73, 74, 76, 77, 78, 79]), model=ScalarModel(intercept=8.054890520840994, linear_terms=array([-0.00015236, -0.00015895,  0.00031207]), square_terms=array([[ 3.22131959e-07, -2.56498401e-08,  4.92324599e-08],
+       [-2.56498401e-08,  4.92819029e-09, -9.41380908e-09],
+       [ 4.92324599e-08, -9.41380908e-09,  1.80067706e-08]]), scale=0.0005672031334704201, shift=array([ 4.54810503, 20.0344787 , 40.455612  ])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19829,12 +19803,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=80, candidate_x=array([ 4.82343976, 21.52229199, 36.05675412]), index=47, x=array([ 4.82317079, 21.52250883, 36.05720457]), fval=7.879941176547239, rho=-6.6863082618888745, accepted=False, new_indices=array([68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79]), old_indices_used=array([47, 66, 67]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82317079, 21.52250883, 36.05720457]), radius=0.00028384522656915175, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 68, 69, 70, 71, 72, 75, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=8.179681723770933, linear_terms=array([-0.00848254, -0.01422289,  0.00804882]), square_terms=array([[ 1.10341792e-05,  1.94015285e-05, -1.09731944e-05],
-       [ 1.94015285e-05,  3.41893987e-05, -1.93368371e-05],
-       [-1.09731944e-05, -1.93368371e-05,  1.09365414e-05]]), scale=0.00028384522656915175, shift=array([ 4.82317079, 21.52250883, 36.05720457])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=80, candidate_x=array([ 4.54833127, 20.03471485, 40.45514836]), index=49, x=array([ 4.54810503, 20.0344787 , 40.455612  ]), fval=7.895353314082556, rho=-680.839186412877, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 67, 68, 70, 71, 72, 73, 74, 76, 77, 78, 79]), old_indices_discarded=array([66, 69, 75]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54810503, 20.0344787 , 40.455612  ]), radius=0.00028360156673521006, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([49, 67, 68, 70, 72, 73, 74, 76, 77, 78, 79, 80]), model=ScalarModel(intercept=8.0606504292287, linear_terms=array([ 0.00163836,  0.0017222 , -0.00194526]), square_terms=array([[ 1.42852417e-06,  8.68590235e-07, -1.71510865e-06],
+       [ 8.68590235e-07,  6.00874052e-07, -9.50688241e-07],
+       [-1.71510865e-06, -9.50688241e-07,  2.37568838e-06]]), scale=0.00028360156673521006, shift=array([ 4.54810503, 20.0344787 , 40.455612  ])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -19916,12 +19890,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=81, candidate_x=array([ 4.82330528, 21.52272366, 36.05707678]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=5.591022060643097, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([47, 68, 69, 70, 71, 72, 75, 76, 77, 78, 79, 80]), old_indices_discarded=array([67, 73, 74]), step_length=0.0002838452265687179, relative_step_length=0.9999999999984716, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=0.0005676904531383035, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 69, 70, 73, 74, 75, 76, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=8.11157047285112, linear_terms=array([-0.02151551, -0.07599694,  0.04516709]), square_terms=array([[ 6.93181957e-05,  2.53699788e-04, -1.51934412e-04],
-       [ 2.53699788e-04,  9.29798154e-04, -5.56832029e-04],
-       [-1.51934412e-04, -5.56832029e-04,  3.34196437e-04]]), scale=0.0005676904531383035, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=81, candidate_x=array([ 4.54795424, 20.0343194 , 40.45579176]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=39.76485577085202, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([49, 67, 68, 70, 72, 73, 74, 76, 77, 78, 79, 80]), old_indices_discarded=array([69, 71, 75]), step_length=0.0002836015667377904, relative_step_length=1.0000000000090985, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=0.0005672031334704201, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([49, 67, 69, 70, 71, 72, 75, 77, 78, 79, 80, 81]), model=ScalarModel(intercept=8.03610400379156, linear_terms=array([ 0.00267448, -0.00308386, -0.00208632]), square_terms=array([[ 3.22409665e-05,  4.55569186e-05, -4.87182836e-05],
+       [ 4.55569186e-05,  7.88743880e-05, -7.54251679e-05],
+       [-4.87182836e-05, -7.54251679e-05,  7.67579944e-05]]), scale=0.0005672031334704201, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20003,12 +19977,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=82, candidate_x=array([ 4.8234089 , 21.52322283, 36.05682706]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.973289900708472, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 69, 70, 73, 74, 75, 76, 77, 78, 79, 80, 81]), old_indices_discarded=array([66, 67, 68, 71, 72]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=0.00028384522656915175, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 69, 70, 73, 74, 75, 76, 77, 78, 80, 81, 82]), model=ScalarModel(intercept=8.16299950096525, linear_terms=array([0.00710925, 0.02228778, 0.01295234]), square_terms=array([[9.92435228e-06, 3.27050802e-05, 1.10319604e-05],
-       [3.27050802e-05, 1.15841838e-04, 2.29923202e-05],
-       [1.10319604e-05, 2.29923202e-05, 3.61930890e-05]]), scale=0.00028384522656915175, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=82, candidate_x=array([ 4.54762007, 20.03470713, 40.45605189]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-100.72649762774934, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 67, 69, 70, 71, 72, 75, 77, 78, 79, 80, 81]), old_indices_discarded=array([66, 68, 73, 74, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=0.00028360156673521006, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([49, 69, 70, 71, 72, 75, 77, 78, 79, 80, 81, 82]), model=ScalarModel(intercept=8.047936448754953, linear_terms=array([-0.00376373,  0.00584969,  0.00534439]), square_terms=array([[ 6.64977140e-06,  6.56389702e-06, -1.59798898e-05],
+       [ 6.56389702e-06,  2.98251268e-05, -2.32990024e-05],
+       [-1.59798898e-05, -2.32990024e-05,  4.15537599e-05]]), scale=0.00028360156673521006, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20090,12 +20064,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=83, candidate_x=array([ 4.82323684, 21.52248108, 36.05694624]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-20.418600156211497, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 69, 70, 73, 74, 75, 76, 77, 78, 80, 81, 82]), old_indices_discarded=array([67, 68, 71, 72, 79]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=0.00014192261328457587, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 69, 70, 73, 74, 75, 77, 80, 81, 82, 83]), model=ScalarModel(intercept=8.172306936352223, linear_terms=array([ 0.00718096,  0.00840867, -0.00805722]), square_terms=array([[ 8.37271386e-06,  9.80783583e-06, -8.91459782e-06],
-       [ 9.80783583e-06,  1.84069845e-05, -1.49071820e-05],
-       [-8.91459782e-06, -1.49071820e-05,  1.39860180e-05]]), scale=0.00014192261328457587, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=83, candidate_x=array([ 4.54807601, 20.03412982, 40.45561902]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-53.2510564023457, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 69, 70, 71, 72, 75, 77, 78, 79, 80, 81, 82]), old_indices_discarded=array([67, 68, 73, 74, 76]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=0.00014180078336760503, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([49, 81, 82, 83]), model=ScalarModel(intercept=7.773292572204038, linear_terms=array([-1.09298259, -0.06301484, -1.06703545]), square_terms=array([[0.25506964, 0.01341666, 0.2512259 ],
+       [0.01341666, 0.00079108, 0.01309426],
+       [0.2512259 , 0.01309426, 0.24766807]]), scale=0.00014180078336760503, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20177,12 +20151,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=84, candidate_x=array([ 4.8232304 , 21.52263709, 36.05716069]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-19.649996123358612, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 69, 70, 73, 74, 75, 77, 80, 81, 82, 83]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=7.096130664228794e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([47, 74, 81, 83, 84]), model=ScalarModel(intercept=7.881865656401156, linear_terms=array([ 0.01891263, -0.08443516, -0.07655381]), square_terms=array([[ 0.00013566, -0.00027629, -0.00019859],
-       [-0.00027629,  0.00115909,  0.00102696],
-       [-0.00019859,  0.00102696,  0.00096729]]), scale=7.096130664228794e-05, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=84, candidate_x=array([ 4.54808082, 20.03436366, 40.45583787]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-0.20431773719708382, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 81, 82, 83]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=7.090039168380251e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([49, 81, 83, 84]), model=ScalarModel(intercept=7.773292572204032, linear_terms=array([ 0.15131341, -0.07862676,  0.01006957]), square_terms=array([[ 0.00586837, -0.00266274,  0.00061619],
+       [-0.00266274,  0.00127435, -0.00025977],
+       [ 0.00061619, -0.00025977,  0.00016019]]), scale=7.090039168380251e-05, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20264,12 +20238,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=85, candidate_x=array([ 4.82328964, 21.52277082, 36.05712744]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-3.645406006418879, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([47, 74, 81, 83, 84]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=3.548065332114397e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), model=ScalarModel(intercept=8.2768442505726, linear_terms=array([ 0.02829895,  0.01321173, -0.04145655]), square_terms=array([[ 1.38983584e-04,  3.29415111e-05, -2.19408105e-04],
-       [ 3.29415111e-05,  3.84892969e-05, -3.65676171e-05],
-       [-2.19408105e-04, -3.65676171e-05,  3.54141054e-04]]), scale=3.548065332114397e-05, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=85, candidate_x=array([ 4.54788905, 20.03434728, 40.45579185]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-2.075951516648934, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([49, 81, 83, 84]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=3.545019584190126e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([81, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), model=ScalarModel(intercept=8.188796748983204, linear_terms=array([-0.03703136, -0.00228161, -0.01380209]), square_terms=array([[ 2.29712330e-04, -6.48139985e-06,  8.34210040e-05],
+       [-6.48139985e-06,  2.35838329e-04,  1.84698356e-05],
+       [ 8.34210040e-05,  1.84698356e-05,  3.26779483e-05]]), scale=3.545019584190126e-05, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20351,12 +20325,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=98, candidate_x=array([ 4.82328642, 21.52271425, 36.05710532]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-10.651029072129532, accepted=False, new_indices=array([86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), old_indices_used=array([81, 84, 85]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1.7740326660571984e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([81, 86, 87, 88, 89, 90, 91, 92, 95, 96, 97, 98]), model=ScalarModel(intercept=8.279157705758113, linear_terms=array([-0.00032621,  0.00358878,  0.00421786]), square_terms=array([[ 1.33809527e-08, -1.57000523e-07, -1.84521348e-07],
-       [-1.57000523e-07,  1.86029680e-06,  2.18639064e-06],
-       [-1.84521348e-07,  2.18639064e-06,  2.56964588e-06]]), scale=1.7740326660571984e-05, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=98, candidate_x=array([ 4.54798747, 20.03432162, 40.4558039 ]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-12.118772198739151, accepted=False, new_indices=array([86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97]), old_indices_used=array([81, 84, 85]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1.772509792095063e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([81, 86, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98]), model=ScalarModel(intercept=8.16639694473572, linear_terms=array([-0.0377616 ,  0.05466721,  0.00873161]), square_terms=array([[ 2.52044525e-04, -3.65187092e-04, -5.83287550e-05],
+       [-3.65187092e-04,  5.29119573e-04,  8.45125325e-05],
+       [-5.83287550e-05,  8.45125325e-05,  1.34985899e-05]]), scale=1.772509792095063e-05, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20438,12 +20412,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=99, candidate_x=array([ 4.82330636, 21.52271223, 36.05706325]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-95.97585300760615, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([81, 86, 87, 88, 89, 90, 91, 92, 95, 96, 97, 98]), old_indices_discarded=array([85, 93, 94]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=8.870163330285992e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([81, 86, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99]), model=ScalarModel(intercept=8.276757482153636, linear_terms=array([-0.00213864, -0.0018736 , -0.00296729]), square_terms=array([[6.58437597e-07, 7.41341023e-07, 1.11784125e-06],
-       [7.41341023e-07, 6.58112771e-06, 8.35636235e-06],
-       [1.11784125e-06, 8.35636235e-06, 1.06646633e-05]]), scale=8.870163330285992e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=99, candidate_x=array([ 4.54796345, 20.03430434, 40.45579017]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-6.857404223132469, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([81, 86, 87, 88, 89, 90, 91, 92, 94, 95, 97, 98]), old_indices_discarded=array([85, 93, 96]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=8.862548960475314e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([81, 86, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99]), model=ScalarModel(intercept=8.18970337083217, linear_terms=array([-0.01188931,  0.01526962,  0.0027824 ]), square_terms=array([[ 4.08672148e-05, -6.03469782e-05, -9.83702502e-06],
+       [-6.03469782e-05,  9.13838314e-05,  1.46019283e-05],
+       [-9.83702502e-06,  1.46019283e-05,  2.37038358e-06]]), scale=8.862548960475314e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20525,13 +20499,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=100, candidate_x=array([ 4.82330991, 21.52272767, 36.05708318]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-40.421430100497616, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([81, 86, 87, 88, 89, 90, 91, 92, 95, 96, 98, 99]), old_indices_discarded=array([93, 94, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=4.435081665142996e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,
-       111, 112]), model=ScalarModel(intercept=8.065063674188076, linear_terms=array([-0.00831488, -0.03125778, -0.04027869]), square_terms=array([[ 1.50770831e-04, -7.87375759e-05, -4.43742587e-05],
-       [-7.87375759e-05,  2.48264154e-04,  2.71815213e-04],
-       [-4.43742587e-05,  2.71815213e-04,  3.11511360e-04]]), scale=4.435081665142996e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=100, candidate_x=array([ 4.54795922, 20.03431212, 40.45579089]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-7.554679895850257, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([81, 86, 87, 88, 89, 90, 91, 92, 94, 95, 98, 99]), old_indices_discarded=array([93, 96, 97]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=4.431274480237657e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,
+       111, 112]), model=ScalarModel(intercept=8.02225277668465, linear_terms=array([ 0.00975676, -0.02291425, -0.00125046]), square_terms=array([[ 1.05915477e-04, -1.84828396e-04, -1.46289760e-05],
+       [-1.84828396e-04,  3.30242294e-04,  2.53969928e-05],
+       [-1.46289760e-05,  2.53969928e-05,  2.02281429e-06]]), scale=4.431274480237657e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20613,12 +20587,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=113, candidate_x=array([ 4.82330575, 21.52272608, 36.05708046]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-6.115080044728515, accepted=False, new_indices=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112]), old_indices_used=array([ 81,  99, 100]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=2.217540832571498e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 113]), model=ScalarModel(intercept=8.061475587088557, linear_terms=array([-0.00131982, -0.0045598 ,  0.00539376]), square_terms=array([[ 2.66211428e-07,  9.21899175e-07, -1.09050740e-06],
-       [ 9.21899175e-07,  3.19259432e-06, -3.77649512e-06],
-       [-1.09050740e-06, -3.77649512e-06,  4.46718687e-06]]), scale=2.217540832571498e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=113, candidate_x=array([ 4.54795262, 20.03432352, 40.45579189]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-11.093027338390817, accepted=False, new_indices=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112]), old_indices_used=array([ 81,  99, 100]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=2.2156372401188286e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 101, 102, 103, 104, 105, 106, 109, 110, 111, 112, 113]), model=ScalarModel(intercept=8.022431528379546, linear_terms=array([-0.00226875, -0.00035832,  0.00566528]), square_terms=array([[ 9.46354119e-07,  1.49743417e-07, -2.36753126e-06],
+       [ 1.49743417e-07,  2.36942553e-08, -3.74620075e-07],
+       [-2.36753126e-06, -3.74620075e-07,  5.92296313e-06]]), scale=2.2156372401188286e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20700,12 +20674,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=114, candidate_x=array([ 4.82330569, 21.52272506, 36.05707511]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-64.33325095194103, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 113]), old_indices_discarded=array([100, 107, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1.108770416285749e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 103, 104, 105, 106, 108, 109, 110, 111, 113, 114]), model=ScalarModel(intercept=8.078258469369693, linear_terms=array([ 0.00096103,  0.00323213, -0.00308825]), square_terms=array([[ 1.45135393e-06,  2.91024059e-06, -2.33056099e-06],
-       [ 2.91024059e-06,  6.12146233e-06, -5.01192570e-06],
-       [-2.33056099e-06, -5.01192570e-06,  4.14368035e-06]]), scale=1.108770416285749e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=114, candidate_x=array([ 4.54795506, 20.03431953, 40.45578971]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-65.03877057978109, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 101, 102, 103, 104, 105, 106, 109, 110, 111, 112, 113]), old_indices_discarded=array([100, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1.1078186200594143e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 103, 104, 105, 106, 109, 110, 111, 112, 113, 114]), model=ScalarModel(intercept=8.035712741817115, linear_terms=array([ 0.00094691,  0.00063589, -0.00342348]), square_terms=array([[ 1.47141127e-06,  8.99349292e-07, -2.68935616e-06],
+       [ 8.99349292e-07,  5.50340966e-07, -1.66327535e-06],
+       [-2.68935616e-06, -1.66327535e-06,  5.50529633e-06]]), scale=1.1078186200594143e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20787,12 +20761,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=115, candidate_x=array([ 4.82330503, 21.52272288, 36.05707753]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-60.2939086014513, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 103, 104, 105, 106, 108, 109, 110, 111, 113, 114]), old_indices_discarded=array([101, 107, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 104, 105, 106, 108, 109, 110, 111, 113, 114, 115]), model=ScalarModel(intercept=8.074850357818761, linear_terms=array([ 0.00015076,  0.00363642, -0.00212575]), square_terms=array([[ 2.22141771e-06,  2.08607301e-06, -2.76106861e-06],
-       [ 2.08607301e-06,  4.19368223e-06, -3.79062602e-06],
-       [-2.76106861e-06, -3.79062602e-06,  4.07742940e-06]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=115, candidate_x=array([ 4.54795395, 20.0343192 , 40.45579282]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-64.11569287242392, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 103, 104, 105, 106, 109, 110, 111, 112, 113, 114]), old_indices_discarded=array([101, 107, 108]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 103, 104, 105, 106, 109, 110, 111, 113, 114, 115]), model=ScalarModel(intercept=8.031218135165165, linear_terms=array([ 0.00036071, -0.00091776, -0.00359654]), square_terms=array([[ 1.55019373e-06,  1.35080520e-06, -1.77710262e-06],
+       [ 1.35080520e-06,  1.47331342e-06, -7.51721732e-07],
+       [-1.77710262e-06, -7.51721732e-07,  4.25451486e-06]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20874,12 +20848,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=116, candidate_x=array([ 4.82330524, 21.52272279, 36.05707728]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-37.085229084547166, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 104, 105, 106, 108, 109, 110, 111, 113, 114, 115]), old_indices_discarded=array([101, 103, 107, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 104, 105, 108, 109, 110, 111, 113, 114, 115, 116]), model=ScalarModel(intercept=8.060184522537769, linear_terms=array([ 0.00040799,  0.0117221 , -0.00137685]), square_terms=array([[ 2.37702547e-06,  2.01309637e-06, -3.03442433e-06],
-       [ 2.01309637e-06,  2.25046358e-05, -4.09573495e-06],
-       [-3.03442433e-06, -4.09573495e-06,  3.98684745e-06]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=116, candidate_x=array([ 4.54795414, 20.03431964, 40.45579273]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-33.20588559289968, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 103, 104, 105, 106, 109, 110, 111, 113, 114, 115]), old_indices_discarded=array([101, 107, 108, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 103, 105, 106, 109, 110, 111, 113, 114, 115, 116]), model=ScalarModel(intercept=8.019091969145485, linear_terms=array([ 0.00168395, -0.00167253, -0.00474279]), square_terms=array([[ 2.12352954e-06,  1.14496507e-06, -2.98522352e-06],
+       [ 1.14496507e-06,  2.23352631e-06, -1.25091250e-07],
+       [-2.98522352e-06, -1.25091250e-07,  5.56832621e-06]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -20961,12 +20935,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=117, candidate_x=array([ 4.82330524, 21.52272266, 36.05707689]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-56.11799816537842, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 104, 105, 108, 109, 110, 111, 113, 114, 115, 116]), old_indices_discarded=array([101, 103, 106, 107, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 104, 105, 109, 110, 111, 113, 114, 115, 116, 117]), model=ScalarModel(intercept=8.09392964176465, linear_terms=array([ 0.00195521,  0.00338509, -0.0057841 ]), square_terms=array([[ 2.55764763e-06,  3.37873758e-06, -6.44689424e-06],
-       [ 3.37873758e-06,  5.69368876e-06, -8.59207813e-06],
-       [-6.44689424e-06, -8.59207813e-06,  1.64005975e-05]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=117, candidate_x=array([ 4.54795392, 20.03431971, 40.45579266]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-110.80268549132667, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 103, 105, 106, 109, 110, 111, 113, 114, 115, 116]), old_indices_discarded=array([101, 104, 107, 108, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 103, 105, 106, 109, 110, 111, 114, 115, 116, 117]), model=ScalarModel(intercept=8.04600021123036, linear_terms=array([ 0.00141876,  0.00263811, -0.00283092]), square_terms=array([[ 1.56621269e-06,  5.99993075e-06, -2.26404948e-06],
+       [ 5.99993075e-06,  2.58491562e-05, -8.28945897e-06],
+       [-2.26404948e-06, -8.28945897e-06,  3.42523782e-06]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21048,12 +21022,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=118, candidate_x=array([ 4.82330499, 21.52272318, 36.05707761]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-98.2846809610794, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 104, 105, 109, 110, 111, 113, 114, 115, 116, 117]), old_indices_discarded=array([101, 103, 106, 107, 108, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 104, 109, 110, 111, 113, 114, 115, 116, 117, 118]), model=ScalarModel(intercept=8.12866992904854, linear_terms=array([ 0.0077402 , -0.00057494, -0.00704305]), square_terms=array([[ 3.87889018e-05, -1.60976596e-07, -3.10582606e-05],
-       [-1.60976596e-07,  2.30598361e-06, -4.07812083e-07],
-       [-3.10582606e-05, -4.07812083e-07,  2.52327240e-05]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=118, candidate_x=array([ 4.54795389, 20.03431877, 40.45579246]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-145.3171732310735, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 103, 105, 106, 109, 110, 111, 114, 115, 116, 117]), old_indices_discarded=array([101, 104, 107, 108, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 103, 105, 109, 110, 111, 114, 115, 116, 117, 118]), model=ScalarModel(intercept=8.06873637309918, linear_terms=array([-0.00242536, -0.00162619, -0.00076894]), square_terms=array([[ 2.02282491e-06,  4.98186278e-06, -9.76782208e-07],
+       [ 4.98186278e-06,  2.82751616e-05, -6.65719797e-06],
+       [-9.76782208e-07, -6.65719797e-06,  1.90074555e-06]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21135,12 +21109,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=119, candidate_x=array([ 4.8233045 , 21.52272367, 36.0570774 ]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-43.946667719855895, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 104, 109, 110, 111, 113, 114, 115, 116, 117, 118]), old_indices_discarded=array([101, 103, 105, 106, 107, 108, 112]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 104, 109, 110, 111, 114, 115, 116, 117, 118, 119]), model=ScalarModel(intercept=8.142471088121155, linear_terms=array([ 0.00831431,  0.00599277, -0.00625675]), square_terms=array([[ 4.53506167e-05,  2.73486816e-05, -2.79743798e-05],
-       [ 2.73486816e-05,  1.94896303e-05, -1.75094351e-05],
-       [-2.79743798e-05, -1.75094351e-05,  1.77416446e-05]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=119, candidate_x=array([ 4.54795504, 20.03431993, 40.45579202]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-132.18366564717007, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 103, 105, 109, 110, 111, 114, 115, 116, 117, 118]), old_indices_discarded=array([101, 104, 106, 107, 108, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 103, 109, 110, 111, 114, 115, 116, 117, 118, 119]), model=ScalarModel(intercept=8.083330798516064, linear_terms=array([ 0.00636085,  0.00609641, -0.0024775 ]), square_terms=array([[ 2.98335713e-05,  3.79969764e-05, -9.98163799e-06],
+       [ 3.79969764e-05,  4.93383203e-05, -1.26924323e-05],
+       [-9.98163799e-06, -1.26924323e-05,  3.53936891e-06]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21222,12 +21196,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=120, candidate_x=array([ 4.8233046 , 21.52272315, 36.05707731]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-31.850818643468653, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 104, 109, 110, 111, 114, 115, 116, 117, 118, 119]), old_indices_discarded=array([101, 103, 105, 106, 107, 108, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 104, 110, 111, 114, 115, 116, 117, 118, 119, 120]), model=ScalarModel(intercept=8.150617471864782, linear_terms=array([ 0.0030992 ,  0.02026705, -0.00047965]), square_terms=array([[ 1.36754653e-05,  4.31089303e-05, -2.97898073e-06],
-       [ 4.31089303e-05,  1.66640526e-04, -7.73574846e-06],
-       [-2.97898073e-06, -7.73574846e-06,  1.20664047e-06]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=120, candidate_x=array([ 4.54795353, 20.03431875, 40.45579205]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-36.89607644331655, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 103, 109, 110, 111, 114, 115, 116, 117, 118, 119]), old_indices_discarded=array([101, 104, 105, 106, 107, 108, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 103, 110, 111, 114, 115, 116, 117, 118, 119, 120]), model=ScalarModel(intercept=8.09014403912027, linear_terms=array([-0.00137196,  0.01166663, -0.00126287]), square_terms=array([[ 3.81670968e-06, -1.62014317e-05,  1.78916470e-08],
+       [-1.62014317e-05,  1.60351951e-04, -9.05175053e-06],
+       [ 1.78916470e-08, -9.05175053e-06,  1.04045788e-06]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21309,12 +21283,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=121, candidate_x=array([ 4.82330513, 21.52272267, 36.05707679]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-13.662812516586788, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 104, 110, 111, 114, 115, 116, 117, 118, 119, 120]), old_indices_discarded=array([101, 103, 105, 106, 107, 108, 109, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 110, 111, 114, 115, 116, 117, 118, 119, 120, 121]), model=ScalarModel(intercept=8.14900518865651, linear_terms=array([-0.01907111,  0.03625781,  0.00780469]), square_terms=array([[ 8.14548164e-05, -1.82870274e-04, -3.73190280e-05],
-       [-1.82870274e-04,  4.69927379e-04,  8.60482858e-05],
-       [-3.73190280e-05,  8.60482858e-05,  1.76633373e-05]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=121, candidate_x=array([ 4.54795434, 20.03431841, 40.45579186]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-20.75948303894331, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 103, 110, 111, 114, 115, 116, 117, 118, 119, 120]), old_indices_discarded=array([101, 104, 105, 106, 107, 108, 109, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 103, 110, 111, 114, 115, 116, 117, 118, 119, 120, 121]), model=ScalarModel(intercept=8.09191924895214, linear_terms=array([0.00549137, 0.01568606, 0.00167972]), square_terms=array([[2.82985256e-05, 7.60308213e-05, 8.99595079e-06],
+       [7.60308213e-05, 2.35923904e-04, 2.87690208e-05],
+       [8.99595079e-06, 2.87690208e-05, 4.35248272e-06]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21396,12 +21370,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=122, candidate_x=array([ 4.8233057 , 21.52272277, 36.05707662]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-6.463678394060518, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 110, 111, 114, 115, 116, 117, 118, 119, 120, 121]), old_indices_discarded=array([101, 103, 104, 105, 106, 107, 108, 109, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=8.143266140077635, linear_terms=array([-0.05326211,  0.03969836,  0.02874272]), square_terms=array([[ 0.00062959, -0.00049481, -0.0003539 ],
-       [-0.00049481,  0.00046557,  0.00027984],
-       [-0.0003539 ,  0.00027984,  0.00019985]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=122, candidate_x=array([ 4.54795393, 20.03431845, 40.45579168]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-14.105349594382545, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 103, 110, 111, 114, 115, 116, 117, 118, 119, 120, 121]), old_indices_discarded=array([101, 102, 104, 105, 106, 107, 108, 109, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 110, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122]), model=ScalarModel(intercept=8.088669174351967, linear_terms=array([-0.00057248,  0.02768032,  0.00430916]), square_terms=array([[ 5.58066635e-05, -1.01896932e-04, -2.42083793e-05],
+       [-1.01896932e-04,  7.20934079e-04,  1.36906156e-04],
+       [-2.42083793e-05,  1.36906156e-04,  2.73707112e-05]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21483,12 +21457,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=123, candidate_x=array([ 4.82330596, 21.52272307, 36.05707634]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.765053971828672, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([101, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 102, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123]), model=ScalarModel(intercept=8.149857192280214, linear_terms=array([-0.0339903 ,  0.02649437,  0.0122707 ]), square_terms=array([[ 2.76992413e-04, -2.26626704e-04, -8.74275619e-05],
-       [-2.26626704e-04,  2.57512180e-04,  1.06410945e-04],
-       [-8.74275619e-05,  1.06410945e-04,  4.98401860e-05]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=123, candidate_x=array([ 4.54795424, 20.0343184 , 40.45579163]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-12.835172781412307, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 110, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123]), model=ScalarModel(intercept=8.08988480486271, linear_terms=array([0.00613459, 0.00821288, 0.00474186]), square_terms=array([[ 2.86992825e-05, -4.18269876e-05,  6.38943144e-06],
+       [-4.18269876e-05,  3.71520289e-04,  5.91212329e-05],
+       [ 6.38943144e-06,  5.91212329e-05,  4.46765226e-05]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21570,12 +21544,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=124, candidate_x=array([ 4.82330604, 21.52272309, 36.05707649]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-8.801652692697434, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 102, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123]), old_indices_discarded=array([101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124]), model=ScalarModel(intercept=8.147113753005963, linear_terms=array([-0.0581846 ,  0.00380169, -0.02472274]), square_terms=array([[0.00246752, 0.00122995, 0.00210448],
-       [0.00122995, 0.00092392, 0.00126096],
-       [0.00210448, 0.00126096, 0.00195737]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=124, candidate_x=array([ 4.54795368, 20.03431868, 40.45579135]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-30.500639542791244, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 111, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124]), model=ScalarModel(intercept=8.096111280906365, linear_terms=array([-0.02420393,  0.01846992, -0.00943493]), square_terms=array([[ 0.0012874 , -0.00102379,  0.00040927],
+       [-0.00102379,  0.00087928, -0.00034386],
+       [ 0.00040927, -0.00034386,  0.00014587]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21657,12 +21631,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=125, candidate_x=array([ 4.82330621, 21.52272353, 36.0570771 ]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-6.899772029802872, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125]), model=ScalarModel(intercept=8.050866130897193, linear_terms=array([ 0.06026122, -0.08257186,  0.16476298]), square_terms=array([[ 3.18192817e-03, -2.60245391e-05,  4.43047247e-03],
-       [-2.60245391e-05,  1.30863063e-03, -1.08579834e-03],
-       [ 4.43047247e-03, -1.08579834e-03,  7.33000406e-03]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=125, candidate_x=array([ 4.54795483, 20.03431871, 40.45579219]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-12.042404648091702, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125]), model=ScalarModel(intercept=8.027205872674104, linear_terms=array([-0.00343239, -0.05890815,  0.10865714]), square_terms=array([[ 0.00016102, -0.00010568,  0.00013372],
+       [-0.00010568,  0.00081879, -0.00131121],
+       [ 0.00013372, -0.00131121,  0.00241572]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21744,13 +21718,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=126, candidate_x=array([ 4.82330507, 21.522724  , 36.05707586]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-2.233823137443214, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=126, candidate_x=array([ 4.54795423, 20.03431983, 40.45579086]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-3.0287947526053065, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.088200510869859, linear_terms=array([-0.03899899,  0.00743562,  0.06168015]), square_terms=array([[ 4.61456515e-04, -2.06896046e-04, -3.49739456e-04],
+       [-2.06896046e-04,  1.88350818e-04,  4.51594561e-05],
+       [-3.49739456e-04,  4.51594561e-05,  1.09567188e-03]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21832,13 +21806,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=127, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.558541138945838, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=127, candidate_x=array([ 4.54795475, 20.03431928, 40.45579091]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-5.971664274099751, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127]), model=ScalarModel(intercept=8.096377096649354, linear_terms=array([-0.03747592, -0.0021704 ,  0.01515033]), square_terms=array([[ 3.56781841e-04, -1.03169001e-04, -9.77029419e-06],
+       [-1.03169001e-04,  2.05619280e-04, -8.24800805e-05],
+       [-9.77029419e-06, -8.24800805e-05,  4.04973810e-04]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -21920,13 +21894,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=128, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.720389687614612, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=128, candidate_x=array([ 4.54795517, 20.03431945, 40.4557914 ]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-10.88208251356158, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22008,13 +21982,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=129, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-6.094903217924709, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=129, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-7.514288676636365, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22096,13 +22070,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=130, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-8.1394820681089, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=130, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-9.872813241583962, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22184,13 +22158,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=131, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-1.5641133802635288, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=131, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-2.084527797196965, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22272,13 +22246,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=132, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-3.642292528552843, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=132, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-4.504368852923181, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22360,13 +22334,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=133, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.741855741538225, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=133, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-7.20135767829392, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22448,13 +22422,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=134, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-2.5691886493819083, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=134, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-3.592607675234265, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22536,13 +22510,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=135, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.286872811404443, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=135, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-9.020406755154898, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22624,13 +22598,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=136, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-0.7750082179713318, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=136, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-0.9415875990356607, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22712,13 +22686,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=137, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-6.469674330983498, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=137, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-8.000762223502424, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22800,13 +22774,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=138, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.070602302284241, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=138, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-8.596264656070053, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22888,14 +22862,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=139, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-2.4296170750883372, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=139, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-2.8917992166900532, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -22977,14 +22951,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=140, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-6.133592691315585, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=140, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-7.477682128695116, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23066,14 +23040,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=141, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.76684390619074, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=141, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-9.555288238347394, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23155,14 +23129,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=142, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-4.873541835636981, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140, 141]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=142, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-5.9468311472220865, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140, 141]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23244,14 +23218,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=143, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-3.0768906597704437, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140, 141, 142]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=143, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-3.6387887288403524, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140, 141, 142]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23333,14 +23307,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=144, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-2.440830497238748, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140, 141, 142, 143]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=144, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-3.0862557832094537, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140, 141, 142, 143]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23422,14 +23396,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=145, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-2.259062344876628, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140, 141, 142, 143, 144]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=145, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-2.9448402891029795, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140, 141, 142, 143, 144]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23511,14 +23485,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=146, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-8.91609289318393, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140, 141, 142, 143, 144, 145]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=146, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-10.915393521511527, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140, 141, 142, 143, 144, 145]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23600,14 +23574,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=147, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-3.413836328055013, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140, 141, 142, 143, 144, 145, 146]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=147, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-4.184706174504453, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140, 141, 142, 143, 144, 145, 146]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23689,14 +23663,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=148, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.353159482624392, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140, 141, 142, 143, 144, 145, 146, 147]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=148, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-6.403139676196656, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140, 141, 142, 143, 144, 145, 146, 147]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23778,14 +23752,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=149, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-2.6296551462511264, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=149, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-3.317031203030196, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23867,14 +23841,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=150, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.29916645412251, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=150, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-8.858121164721023, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -23956,14 +23930,14 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=151, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-6.508010585168633, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
-       138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=151, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-8.087847115155439, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+       138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24045,15 +24019,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=152, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-3.026238299130365, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=152, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-3.690569249639173, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24135,15 +24109,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=153, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-4.287230507946973, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=153, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-5.4493377904960285, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24225,15 +24199,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=154, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-8.361100140638312, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=154, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-10.294815952176346, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24315,15 +24289,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=155, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-4.747900140548081, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=155, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-5.893945789877003, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153, 154]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153, 154]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24405,15 +24379,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=156, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.005319672946142, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=156, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-8.540424353737563, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153, 154, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153, 154, 155]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24495,15 +24469,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=157, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-6.78294434738743, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=157, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-8.250408161448647, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153, 154, 155, 156]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153, 154, 155, 156]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24585,15 +24559,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=158, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.825378107478798, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=158, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-7.264597388135753, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153, 154, 155, 156, 157]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153, 154, 155, 156, 157]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24675,15 +24649,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=159, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.569317850497796, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=159, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-6.776426839547588, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153, 154, 155, 156, 157, 158]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153, 154, 155, 156, 157, 158]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24765,15 +24739,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=160, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-8.55935510269207, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=160, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-10.471737431187478, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153, 154, 155, 156, 157, 158, 159]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153, 154, 155, 156, 157, 158, 159]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24855,15 +24829,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=161, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.648241317558647, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=161, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-9.612792210718112, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153, 154, 155, 156, 157, 158, 159, 160]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153, 154, 155, 156, 157, 158, 159, 160]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -24945,15 +24919,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=162, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.1529793263306605, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=162, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-6.448047358824827, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25035,15 +25009,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=163, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-4.219580930151482, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=163, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-5.197863380039, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25125,15 +25099,15 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=164, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-3.0829641325807, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=164, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-3.696359796827727, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
-       151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25215,16 +25189,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=165, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.265127611445295, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=165, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-8.675448874751808, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25306,16 +25280,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=166, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.366207484708642, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=166, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-6.474686259427416, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25397,16 +25371,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=167, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.291726168062659, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=167, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-6.200097158487353, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25488,16 +25462,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=168, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-4.912584697166954, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=168, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-6.19697768511532, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166, 167]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166, 167]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25579,16 +25553,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=169, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-4.462479882435039, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=169, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-5.150798522888592, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166, 167, 168]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25670,16 +25644,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=170, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-6.188309188188115, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=170, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-7.422843873668412, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166, 167, 168, 169]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166, 167, 168, 169]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25761,16 +25735,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=171, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-11.477001784864738, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=171, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-13.826812231838835, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166, 167, 168, 169, 170]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166, 167, 168, 169, 170]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25852,16 +25826,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=172, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-3.929579866045423, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=172, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-4.80137694929404, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166, 167, 168, 169, 170, 171]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166, 167, 168, 169, 170, 171]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -25943,16 +25917,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=173, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-3.928421723172965, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=173, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-4.8044483410963394, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166, 167, 168, 169, 170, 171, 172]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166, 167, 168, 169, 170, 171, 172]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26034,16 +26008,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=174, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-1.2038175803157158, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=174, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-1.4073707718138107, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166, 167, 168, 169, 170, 171, 172, 173]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166, 167, 168, 169, 170, 171, 172, 173]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26125,16 +26099,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=175, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.438774373298532, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=175, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-6.7026250706670645, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26216,16 +26190,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=176, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-3.7427689973531963, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=176, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-4.848398490296244, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26307,16 +26281,16 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=177, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.392390779316325, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=177, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-9.218278678306717, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-       164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26398,17 +26372,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=178, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-7.007853305589556, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=178, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-8.127165049176414, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
-       177]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       177]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26490,17 +26464,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=179, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-8.7701240609836, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=179, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-10.828866807112071, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
-       177, 178]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       177, 178]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26582,17 +26556,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=180, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-2.3043194853780267, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=180, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-2.9115300841080867, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
-       177, 178, 179]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       177, 178, 179]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26674,17 +26648,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=181, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-6.2718868711193245, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=181, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-7.685304970992989, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
-       177, 178, 179, 180]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       177, 178, 179, 180]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26766,17 +26740,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=182, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.096008988037937, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=182, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-6.069908947530707, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
-       177, 178, 179, 180, 181]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       177, 178, 179, 180, 181]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26858,17 +26832,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=183, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-3.317528039008737, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=183, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-4.057342133272919, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
-       177, 178, 179, 180, 181, 182]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.82330528, 21.52272366, 36.05707678]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), model=ScalarModel(intercept=8.138410702444535, linear_terms=array([-0.01278964, -0.0174968 ,  0.06252845]), square_terms=array([[0.00165592, 0.00103651, 0.00151292],
-       [0.00103651, 0.00072933, 0.00091554],
-       [0.00151292, 0.00091554, 0.00240447]]), scale=1e-06, shift=array([ 4.82330528, 21.52272366, 36.05707678])), vector_model=VectorModel(intercepts=array([0.03218643, 0.07860141, 0.08931354, 0.13473819, 0.17869229,
-       0.23360101, 0.30709936, 0.68047757, 0.82314669, 1.1502752 ,
-       1.1848269 , 1.60154455, 0.73644352, 0.6932338 , 0.62354613,
-       0.52705641, 0.45058687]), linear_terms=array([[0., 0., 0.],
+       177, 178, 179, 180, 181, 182]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.54795424, 20.0343194 , 40.45579176]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), model=ScalarModel(intercept=8.097458155205674, linear_terms=array([ 0.02885633, -0.01496915,  0.03448218]), square_terms=array([[ 0.00060098, -0.00022997,  0.00042661],
+       [-0.00022997,  0.00018578, -0.00015076],
+       [ 0.00042661, -0.00015076,  0.00061625]]), scale=1e-06, shift=array([ 4.54795424, 20.0343194 , 40.45579176])), vector_model=VectorModel(intercepts=array([0.03203741, 0.07827488, 0.08884135, 0.13406871, 0.17794275,
+       0.2326897 , 0.30617759, 0.6792581 , 0.82110399, 1.14835118,
+       1.18183551, 1.59833332, 0.74086741, 0.69696028, 0.62697548,
+       0.52962713, 0.45292974]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -26950,17 +26924,17 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.650520192108982, shift=array([ 4.75078971, 14.41694464, 46.50520192])), candidate_index=184, candidate_x=array([ 4.82330549, 21.52272394, 36.05707584]), index=81, x=array([ 4.82330528, 21.52272366, 36.05707678]), fval=7.777182247944023, rho=-5.305223497997517, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
-       114, 115, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
+        [0., 0., 0.]]]), scale=4.6465280693896815, shift=array([ 4.73735612, 14.6585077 , 46.46528069])), candidate_index=184, candidate_x=array([ 4.54795367, 20.03431974, 40.45579102]), index=81, x=array([ 4.54795424, 20.0343194 , 40.45579176]), fval=7.7732925722040385, rho=-6.631779264444555, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 81, 116, 117, 118, 120, 121, 122, 123, 124, 125, 127, 128]), old_indices_discarded=array([101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
+       114, 115, 119, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137,
        138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
        151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
-       177, 178, 179, 180, 181, 182, 183]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': None, 'tranquilo_history': History for least_squares function with 185 entries., 'history': {'params': [{'CRRA': 4.7507897148290485, 'BeqShift': 14.416944639071934, 'BeqFac': 46.50520192108982}, {'CRRA': 2.0, 'BeqShift': 10.67154325596777, 'BeqFac': 49.9656686692816}, {'CRRA': 8.49909028066445, 'BeqShift': 11.785770609271953, 'BeqFac': 50.242368059559276}, {'CRRA': 8.443500856662624, 'BeqShift': 18.165245204907336, 'BeqFac': 47.97472782087523}, {'CRRA': 5.691844039705532, 'BeqShift': 18.165245204907336, 'BeqFac': 43.18155650091268}, {'CRRA': 4.724824188531034, 'BeqShift': 17.967574647036347, 'BeqFac': 50.25350248692522}, {'CRRA': 8.469423180719584, 'BeqShift': 18.165245204907336, 'BeqFac': 49.28141866502165}, {'CRRA': 2.088586388010861, 'BeqShift': 18.165245204907336, 'BeqFac': 47.38168721578068}, {'CRRA': 2.125861110147427, 'BeqShift': 13.130015261230277, 'BeqFac': 50.25350248692522}, {'CRRA': 8.49909028066445, 'BeqShift': 15.628775958884763, 'BeqFac': 42.96487527623297}, {'CRRA': 5.122822415641091, 'BeqShift': 10.727272650219009, 'BeqFac': 42.75690135525442}, {'CRRA': 2.0, 'BeqShift': 15.549495730427298, 'BeqFac': 42.75690135525442}, {'CRRA': 8.49909028066445, 'BeqShift': 10.668644073236532, 'BeqFac': 47.93447592448193}, {'CRRA': 4.97754615217262, 'BeqShift': 18.165245204907336, 'BeqFac': 42.75690135525442}, {'CRRA': 4.8093216505797685, 'BeqShift': 18.679377326006023, 'BeqFac': 40.49326713162552}, {'CRRA': 4.563528874873022, 'BeqShift': 22.427677891841427, 'BeqFac': 36.74496656579012}, {'CRRA': 5.261095267368836, 'BeqShift': 24.62034659149714, 'BeqFac': 37.08028673505205}, {'CRRA': 5.464555630969227, 'BeqShift': 22.234764040666988, 'BeqFac': 36.03599289956575}, {'CRRA': 3.576752212634224, 'BeqShift': 22.392523146420928, 'BeqFac': 37.35876180144377}, {'CRRA': 3.5106904478589254, 'BeqShift': 22.546616782168787, 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8.310236141552691, 8.217421827510806, 8.112890319637806, 8.11286473542713, 8.11140720977071, 8.113833734634445, 8.113759241723626, 8.111643169545028, 8.11351894247408, 8.11174077070707, 8.113892508267709, 8.113761086026424, 8.113993721806287, 8.113824196109684, 8.110920690451632, 8.186311172133578, 7.959761464579415, 8.069736560897976, 8.069801303132722, 8.069305357254681, 8.069876569406771, 8.069936707098822, 8.069514370829783, 8.06924261649922, 8.069774301404566, 8.07009431768699, 8.069168689816442, 8.069225182529035, 8.070123449835958, 8.06977594727024, 8.155451540940044, 7.7732925722040385, 8.240395185896585, 8.240500875422034, 8.008165266800315, 8.118918527958392, 8.251438860867049, 8.251444779389791, 8.251448226161642, 8.251451780445281, 8.251449816245005, 8.251442848893657, 8.251440104695389, 8.251443080613061, 8.251446775677154, 8.251452838274611, 8.251439646280627, 8.251443278690962, 8.251428450753348, 8.229032655972661, 7.92007581518818, 8.047361976193686, 8.04736313072709, 8.047364150063343, 8.047369212803918, 8.047361228447116, 8.047366036437115, 8.047363046388242, 8.047366900074774, 8.04736825476496, 8.047366116899058, 8.047367605506302, 8.047364998734736, 8.047366714973693, 8.170661942780557, 8.004426562398358, 7.897060417756936, 8.360580965705033, 8.370476370623138, 8.171795007062007, 8.109372459376386, 8.016851389123966, 8.006945218432616, 8.127978485853948, 8.114489589459337, 8.133851134742773, 8.142377315918193, 8.207360425981863, 8.211823159105794, 8.124870941859951, 8.235221536177132, 7.870823411912076, 7.984042880821207, 8.11022953334525, 7.941383419338964, 8.195339160655063, 7.8173475516176545, 8.147632055233155, 8.175494409659226, 7.908594008937135, 8.123158195517917, 8.220365183464734, 8.051532774215445, 7.943544137436779, 7.917692238399405, 7.911075693440648, 8.284001758873664, 7.969086510610447, 8.072882527409757, 7.928489753553368, 8.187746146098572, 8.151706581185856, 7.945966843279715, 8.028256066226275, 8.254966189512286, 8.049058375305027, 8.172881754849172, 8.1593124837649, 8.113188391467487, 8.090347878815864, 8.263243987694466, 8.22305567803113, 8.074983667054886, 8.016490087163804, 7.946237771271381, 8.179199279102578, 8.076230047337347, 8.063382578656253, 8.063236624806258, 8.01428801768563, 8.120592424474674, 8.420221152033948, 7.997939288956463, 7.998082993167052, 7.839140604233222, 8.086894843312795, 8.000139331759671, 8.204597187746662, 8.153546187798828, 8.279953348905755, 7.909517176318274, 8.13287244839529, 8.057291335569081, 7.963127404712879, 8.083580111324109], 'runtime': [0.0, 1.4504399569996167, 1.6735209899779875, 1.848485857975902, 2.051726149977185, 2.2680805979762226, 2.4766933909850195, 2.6960455549997278, 2.9233444329875056, 3.1349784639896825, 3.351505247002933, 3.5519586500013247, 3.789971810998395, 4.980105636990629, 6.0681765969784465, 7.14518748698174, 8.242883124999935, 9.446400944987545, 10.908805482002208, 11.098579767975025, 11.300343257986242, 11.521744165976997, 11.709593644976849, 11.922371152992127, 12.14217539699166, 12.368907803989714, 12.575530548987444, 12.792101485974854, 13.016168340982404, 13.221883085992886, 14.423987668997142, 15.514919516979717, 16.605351261998294, 17.688254665990826, 18.79003464698326, 20.23973666600068, 20.567571216000943, 20.763643047976075, 20.964406384999165, 21.16431177899358, 21.37776899599703, 21.59986166199087, 21.80673703897628, 22.04653499298729, 22.243090450996533, 22.50231817900203, 22.704126974975225, 23.937891650974052, 25.02873625297798, 26.11588236899115, 27.210552746983012, 28.303122565994272, 29.752643885993166, 29.977851061994443, 30.196600117982598, 30.41534087600303, 30.64369718299713, 30.83668269799091, 31.214602166000986, 31.43971441499889, 31.636059698997997, 31.8673623209761, 32.06269055500161, 32.31829570498667, 33.605878393980674, 34.68461284699151, 35.7772903709847, 37.15140438597882, 37.35764386400115, 37.56079981298535, 37.805569541989826, 37.9869795999839, 38.189530017989455, 38.397976515989285, 38.6148340869986, 38.85020638399874, 39.07555837597465, 39.28626621697913, 39.52893761100131, 40.807649770984426, 41.88983869898948, 43.09675294798217, 44.18653134498163, 45.26392616098747, 46.3616533469758, 47.43514103299822, 48.85707815099158, 49.0468897089886, 49.23925091398996, 49.443507951975334, 49.66760054600309, 49.845997730997624, 50.10123870597454, 50.3494804869988, 50.55374181299703, 50.76037691699457, 51.00240563097759, 51.187482879002346, 52.40327418799279, 53.50749146100134, 54.73730688900105, 56.22406851800042, 56.44644689100096, 56.68469789598021, 56.8901742209855, 57.100525863002986, 57.3111777069862, 57.520736595994094, 57.78636092599481, 58.0168840479746, 58.23030952899717, 58.451445088983746, 58.664131533994805, 59.877345352986595, 60.97697681398131, 62.05383880698355, 63.13517369897454, 64.22225431297556, 65.31689493299928, 66.52228632598417, 67.64446152999881, 68.75158098599059, 69.8854100399767, 70.99814650599728, 72.10933911797474, 73.20828397700097, 74.32587041798979, 75.41264701998443, 76.51760924200062, 77.61268673298764, 78.8430725520011, 79.93394008697942, 81.02558974697604, 82.21587805397576, 83.33100570397801, 84.45169154999894, 85.57784125200124, 86.69246509697405, 87.80126954399748, 88.89460449200124, 90.00345614197431, 91.22872118197847, 92.34742080399883, 93.44853516799049, 94.55232269599219, 95.65972686398891, 96.76427458698163, 97.84543513998506, 98.94056727798306, 100.040699234989, 101.16735448097461, 102.42536465599551, 103.56162227498135, 104.67945627198787, 105.8182555079984, 106.92938266898273, 108.0311693699914, 109.14735993297654, 110.2438464279985, 111.35972577598295, 112.45283816198935, 113.53562331499415, 114.74259360798169, 115.86386259298888, 116.9776439369889, 118.10105244399165, 119.22360731297522, 120.32768536597723, 121.43662568199215, 122.53401154698804, 123.64936441497412, 124.7441345549887, 125.84787734498968, 127.06110377499135, 128.15759919700213, 129.24061648899806, 130.32890162998228, 131.40542019400164, 132.51752304498223, 133.6317157689773, 134.7516808619839, 135.87073868498555, 136.98414208198665, 138.0862668469781, 139.33996226999443], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 9, 10, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 15, 16, 17, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 21, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 33, 34, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107]}}, {'solution_x': array([ 4.59575776, 22.22521554, 45.00717222]), 'solution_criterion': 7.791322680405853, 'states': [State(trustregion=Region(center=array([ 4.60300984, 22.22100549, 44.99139068]), radius=4.499139067728223, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=[0], model=ScalarModel(intercept=7.908703892454978, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.],
        [0., 0., 0.],
-       [0., 0., 0.]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+       [0., 0., 0.]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27042,12 +27016,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=0, candidate_x=array([ 4.6220078 , 21.87938374, 45.04784782]), index=0, x=array([ 4.6220078 , 21.87938374, 45.04784782]), fval=7.905438690332966, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.6220078 , 21.87938374, 45.04784782]), radius=4.504784781820516, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=17.157132861135576, linear_terms=array([0.01738924, 0.35675109, 2.54406133]), square_terms=array([[ 7.11243666e+00,  2.05971063e-02, -3.74984030e-04],
-       [ 2.05971063e-02,  3.78538397e-03,  2.65298009e-02],
-       [-3.74984030e-04,  2.65298009e-02,  1.89317048e-01]]), scale=array([3.12642311, 3.63083841, 3.63083841]), shift=array([ 5.12642311, 21.87938374, 45.04784782])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=0, candidate_x=array([ 4.60300984, 22.22100549, 44.99139068]), index=0, x=array([ 4.60300984, 22.22100549, 44.99139068]), fval=7.9087038924549775, rho=nan, accepted=True, new_indices=[0], old_indices_used=[], old_indices_discarded=[], step_length=None, relative_step_length=None, n_evals_per_point=None, n_evals_acceptance=None), State(trustregion=Region(center=array([ 4.60300984, 22.22100549, 44.99139068]), radius=4.499139067728223, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), model=ScalarModel(intercept=16.98877201954729, linear_terms=array([0.30143477, 0.31973178, 2.21931763]), square_terms=array([[7.12190480e+00, 2.28960438e-02, 1.39534716e-03],
+       [2.28960438e-02, 3.08067304e-03, 2.08820599e-02],
+       [1.39534716e-03, 2.08820599e-02, 1.45508288e-01]]), scale=array([3.11464891, 3.62628799, 3.62628799]), shift=array([ 5.11464891, 22.22100549, 44.99139068])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27129,12 +27103,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=13, candidate_x=array([ 5.12766836, 18.24854532, 41.41700941]), index=0, x=array([ 4.6220078 , 21.87938374, 45.04784782]), fval=7.905438690332966, rho=-0.3763324238577014, accepted=False, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.6220078 , 21.87938374, 45.04784782]), radius=2.252392390910258, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13]), model=ScalarModel(intercept=15.916555696299055, linear_terms=array([-1.7658843 ,  1.48274403,  1.33182585]), square_terms=array([[ 3.67002682, -0.01202038, -0.05619285],
-       [-0.01202038,  0.07097818,  0.06265758],
-       [-0.05619285,  0.06265758,  0.05602176]]), scale=2.252392390910258, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=13, candidate_x=array([ 4.99344478, 18.5947175 , 41.36510269]), index=0, x=array([ 4.60300984, 22.22100549, 44.99139068]), fval=7.9087038924549775, rho=-0.35098914871852027, accepted=False, new_indices=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]), old_indices_used=array([0]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60300984, 22.22100549, 44.99139068]), radius=2.2495695338641113, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13]), model=ScalarModel(intercept=15.675309523583756, linear_terms=array([-1.62469208,  1.51491433,  1.14376625]), square_terms=array([[ 3.67472932e+00,  9.48603814e-04, -5.27344752e-02],
+       [ 9.48603814e-04,  7.56013870e-02,  5.56028249e-02],
+       [-5.27344752e-02,  5.56028249e-02,  4.19065780e-02]]), scale=2.2495695338641113, shift=array([ 4.60300984, 22.22100549, 44.99139068])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27216,12 +27190,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=14, candidate_x=array([ 5.31195   , 20.27584195, 43.62248423]), index=0, x=array([ 4.6220078 , 21.87938374, 45.04784782]), fval=7.905438690332966, rho=-0.5005869406721482, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13]), old_indices_discarded=array([ 2, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.6220078 , 21.87938374, 45.04784782]), radius=1.126196195455129, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  4,  5,  7,  8,  9, 10, 11, 13, 14]), model=ScalarModel(intercept=13.488744898994142, linear_terms=array([-2.65153352,  0.46047847, -0.25973136]), square_terms=array([[ 1.05903653, -0.03402051,  0.00881972],
-       [-0.03402051,  0.00807599, -0.00473875],
-       [ 0.00881972, -0.00473875,  0.0029532 ]]), scale=1.126196195455129, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=14, candidate_x=array([ 5.26422714, 20.39213879, 43.62921927]), index=0, x=array([ 4.60300984, 22.22100549, 44.99139068]), fval=7.9087038924549775, rho=-0.4627843401655227, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13]), old_indices_discarded=array([ 2, 12]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60300984, 22.22100549, 44.99139068]), radius=1.1247847669320556, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0,  1,  4,  5,  7,  8,  9, 10, 11, 13, 14]), model=ScalarModel(intercept=13.419802404462027, linear_terms=array([-2.4718645 ,  0.50534024, -0.27471505]), square_terms=array([[ 1.03070021, -0.03312329,  0.00602591],
+       [-0.03312329,  0.00981699, -0.00559109],
+       [ 0.00602591, -0.00559109,  0.00341696]]), scale=1.1247847669320556, shift=array([ 4.60300984, 22.22100549, 44.99139068])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27303,12 +27277,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=15, candidate_x=array([ 5.73968524, 21.91846742, 44.91523003]), index=0, x=array([ 4.6220078 , 21.87938374, 45.04784782]), fval=7.905438690332966, rho=-0.6306812915680658, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  4,  5,  7,  8,  9, 10, 11, 13, 14]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.6220078 , 21.87938374, 45.04784782]), radius=0.5630980977275645, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=8.02314885965588, linear_terms=array([ 0.13765167, -0.06448435, -0.07711981]), square_terms=array([[ 0.28885874, -0.01206548, -0.01037949],
-       [-0.01206548,  0.00081866,  0.00078135],
-       [-0.01037949,  0.00078135,  0.00076922]]), scale=0.5630980977275645, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=15, candidate_x=array([ 5.6662673 , 22.15983561, 45.35316766]), index=0, x=array([ 4.60300984, 22.22100549, 44.99139068]), fval=7.9087038924549775, rho=-0.5571710920084039, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0,  1,  4,  5,  7,  8,  9, 10, 11, 13, 14]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60300984, 22.22100549, 44.99139068]), radius=0.5623923834660278, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), model=ScalarModel(intercept=8.02530100947449, linear_terms=array([ 0.09224965, -0.09510597, -0.0205669 ]), square_terms=array([[ 0.28944828, -0.01132871, -0.00971877],
+       [-0.01132871,  0.00104691,  0.00055559],
+       [-0.00971877,  0.00055559,  0.00042591]]), scale=0.5623923834660278, shift=array([ 4.60300984, 22.22100549, 44.99139068])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27390,12 +27364,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=28, candidate_x=array([ 4.44221102, 22.24239685, 45.49145568]), index=0, x=array([ 4.6220078 , 21.87938374, 45.04784782]), fval=7.905438690332966, rho=-0.45662333342988426, accepted=False, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.6220078 , 21.87938374, 45.04784782]), radius=0.28154904886378224, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27]), model=ScalarModel(intercept=7.9850015498212565, linear_terms=array([-0.04067901, -0.00091665,  0.00352401]), square_terms=array([[7.21941507e-02, 1.16057547e-04, 5.47207326e-05],
-       [1.16057547e-04, 8.58887999e-07, 1.47047819e-07],
-       [5.47207326e-05, 1.47047819e-07, 1.02203968e-06]]), scale=0.28154904886378224, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=28, candidate_x=array([ 4.48650039, 22.77634209, 45.10002742]), index=0, x=array([ 4.60300984, 22.22100549, 44.99139068]), fval=7.9087038924549775, rho=-0.30679530884995854, accepted=False, new_indices=array([16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]), old_indices_used=array([ 0, 14, 15]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60300984, 22.22100549, 44.99139068]), radius=0.2811961917330139, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27]), model=ScalarModel(intercept=7.997055169208679, linear_terms=array([-0.05368596,  0.00324463,  0.01014842]), square_terms=array([[ 7.18549105e-02,  3.90192048e-05, -6.28561944e-06],
+       [ 3.90192048e-05,  7.46040940e-07,  2.09661588e-06],
+       [-6.28561944e-06,  2.09661588e-06,  6.67023673e-06]]), scale=0.2811961917330139, shift=array([ 4.60300984, 22.22100549, 44.99139068])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27477,12 +27451,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=29, candidate_x=array([ 4.77189438, 21.93588148, 44.81306231]), index=0, x=array([ 4.6220078 , 21.87938374, 45.04784782]), fval=7.905438690332966, rho=-16.5586755612528, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27]), old_indices_discarded=array([15, 24, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.6220078 , 21.87938374, 45.04784782]), radius=0.14077452443189112, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 29]), model=ScalarModel(intercept=8.006223359679272, linear_terms=array([-0.01743158,  0.00064127,  0.00504273]), square_terms=array([[ 1.82522843e-02,  4.48690099e-05, -1.63394384e-04],
-       [ 4.48690099e-05,  2.88983812e-07, -3.41570848e-07],
-       [-1.63394384e-04, -3.41570848e-07,  4.77469957e-06]]), scale=0.14077452443189112, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=29, candidate_x=array([ 4.78003456, 22.1525947 , 44.7791103 ]), index=0, x=array([ 4.60300984, 22.22100549, 44.99139068]), fval=7.9087038924549775, rho=-8.604220038688029, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27]), old_indices_discarded=array([15, 25, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60300984, 22.22100549, 44.99139068]), radius=0.14059809586650696, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 29]), model=ScalarModel(intercept=8.009863662064841, linear_terms=array([-2.21471565e-02, -2.83581814e-04,  1.63257602e-05]), square_terms=array([[ 1.83355000e-02, -8.22211703e-05, -2.36713211e-04],
+       [-8.22211703e-05,  9.10238166e-07,  2.51214631e-06],
+       [-2.36713211e-04,  2.51214631e-06,  7.05767224e-06]]), scale=0.14059809586650696, shift=array([ 4.60300984, 22.22100549, 44.99139068])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27564,12 +27538,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=30, candidate_x=array([ 4.71970397, 21.86524889, 44.94433425]), index=0, x=array([ 4.6220078 , 21.87938374, 45.04784782]), fval=7.905438690332966, rho=-13.052040228661848, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 29]), old_indices_discarded=array([24, 25, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.6220078 , 21.87938374, 45.04784782]), radius=0.07038726221594556, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=8.206659193259478, linear_terms=array([-0.02170082,  0.00434972,  0.01465537]), square_terms=array([[ 3.96765918e-03, -2.15442469e-04,  2.72715562e-04],
-       [-2.15442469e-04,  6.39575476e-05, -8.09748616e-06],
-       [ 2.72715562e-04, -8.09748616e-06,  5.69779397e-05]]), scale=0.07038726221594556, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=30, candidate_x=array([ 4.72955173, 22.26555657, 45.03346413]), index=0, x=array([ 4.60300984, 22.22100549, 44.99139068]), fval=7.9087038924549775, rho=-11.656013615935887, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 29]), old_indices_discarded=array([25, 27, 28]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60300984, 22.22100549, 44.99139068]), radius=0.07029904793325348, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=8.203141730269936, linear_terms=array([-0.03703803, -0.01308515, -0.00716443]), square_terms=array([[3.82188710e-03, 8.00733877e-05, 3.18044913e-04],
+       [8.00733877e-05, 6.70446613e-05, 7.45596386e-05],
+       [3.18044913e-04, 7.45596386e-05, 1.14626938e-04]]), scale=0.07029904793325348, shift=array([ 4.60300984, 22.22100549, 44.99139068])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27651,12 +27625,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=43, candidate_x=array([ 4.67644299, 21.8680842 , 45.00467957]), index=0, x=array([ 4.6220078 , 21.87938374, 45.04784782]), fval=7.905438690332966, rho=-13.477739986068798, accepted=False, new_indices=array([31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_used=array([ 0, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.6220078 , 21.87938374, 45.04784782]), radius=0.03519363110797278, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=8.207189638406799, linear_terms=array([ 0.0079307 , -0.00644329, -0.00445678]), square_terms=array([[ 1.24175577e-03, -7.66659154e-05, -4.56271729e-05],
-       [-7.66659154e-05,  7.28504211e-06,  4.53323723e-06],
-       [-4.56271729e-05,  4.53323723e-06,  2.86466633e-06]]), scale=0.03519363110797278, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=43, candidate_x=array([ 4.67074489, 22.23871501, 44.99773825]), index=0, x=array([ 4.60300984, 22.22100549, 44.99139068]), fval=7.9087038924549775, rho=-8.9032598889718, accepted=False, new_indices=array([31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_used=array([ 0, 29, 30]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60300984, 22.22100549, 44.99139068]), radius=0.03514952396662674, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42]), model=ScalarModel(intercept=8.206200137785832, linear_terms=array([ 0.00160985,  0.00242094, -0.00841933]), square_terms=array([[ 1.15636061e-03,  2.52603743e-05, -8.83487984e-05],
+       [ 2.52603743e-05,  9.82161732e-07, -3.34559435e-06],
+       [-8.83487984e-05, -3.34559435e-06,  1.15695617e-05]]), scale=0.03514952396662674, shift=array([ 4.60300984, 22.22100549, 44.99139068])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27738,12 +27712,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=44, candidate_x=array([ 4.59891703, 21.90115425, 45.06306139]), index=0, x=array([ 4.6220078 , 21.87938374, 45.04784782]), fval=7.905438690332966, rho=-26.815108159697523, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([30, 32, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.6220078 , 21.87938374, 45.04784782]), radius=0.01759681555398639, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 31, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44]), model=ScalarModel(intercept=8.204270210725667, linear_terms=array([ 0.00570083, -0.00325278, -0.00105809]), square_terms=array([[ 3.08772300e-04, -1.20395050e-05, -1.13743982e-06],
-       [-1.20395050e-05,  2.34042613e-06,  8.79672429e-07],
-       [-1.13743982e-06,  8.79672429e-07,  3.93805329e-07]]), scale=0.01759681555398639, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=44, candidate_x=array([ 4.59740853, 22.21083732, 45.02675261]), index=0, x=array([ 4.60300984, 22.22100549, 44.99139068]), fval=7.9087038924549775, rho=-30.40164704651966, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([30, 36, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60300984, 22.22100549, 44.99139068]), radius=0.01757476198331337, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44]), model=ScalarModel(intercept=8.202909677022546, linear_terms=array([ 0.00212889, -0.00116275, -0.00434937]), square_terms=array([[ 2.95987914e-04, -1.07665924e-05, -1.01312770e-05],
+       [-1.07665924e-05,  6.96109168e-07, -1.12205463e-07],
+       [-1.01312770e-05, -1.12205463e-07,  4.53236990e-06]]), scale=0.01757476198331337, shift=array([ 4.60300984, 22.22100549, 44.99139068])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27825,12 +27799,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=45, candidate_x=array([ 4.60634469, 21.8871799 , 45.04972792]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=13.778870792974253, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 33, 34, 35, 36, 37, 38, 39, 41, 42, 44]), old_indices_discarded=array([32, 40, 43]), step_length=0.017596815553986317, relative_step_length=0.9999999999999959, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=0.03519363110797278, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 31, 32, 34, 36, 37, 38, 39, 40, 42, 44, 45]), model=ScalarModel(intercept=8.16817662856793, linear_terms=array([-0.01013098,  0.00569035, -0.0063704 ]), square_terms=array([[ 8.36663155e-04,  6.62571254e-05, -2.34241529e-05],
-       [ 6.62571254e-05,  1.04601603e-05, -1.89680982e-06],
-       [-2.34241529e-05, -1.89680982e-06,  7.43347480e-06]]), scale=0.03519363110797278, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=45, candidate_x=array([ 4.59575699, 22.22521614, 45.00717243]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=18.41838972742683, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44]), old_indices_discarded=array([32, 36, 43]), step_length=0.01787168467525086, relative_step_length=1.0168948343209092, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=0.03514952396662674, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 31, 32, 33, 36, 37, 39, 40, 42, 43, 44, 45]), model=ScalarModel(intercept=8.169035424774385, linear_terms=array([0.00240049, 0.0091881 , 0.00496252]), square_terms=array([[1.10179583e-03, 8.88875474e-05, 1.25748103e-04],
+       [8.88875474e-05, 1.39752993e-05, 9.58920165e-06],
+       [1.25748103e-04, 9.58920165e-06, 2.75101588e-05]]), scale=0.03514952396662674, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27912,12 +27886,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=46, candidate_x=array([ 4.63251848, 21.87145653, 45.06722935]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-22.22432751510127, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 32, 34, 36, 37, 38, 39, 40, 42, 44, 45]), old_indices_discarded=array([30, 33, 35, 41, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=0.01759681555398639, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 31, 32, 34, 37, 38, 39, 40, 42, 44, 45, 46]), model=ScalarModel(intercept=8.151481603636242, linear_terms=array([-0.01758894,  0.01614957, -0.01222593]), square_terms=array([[ 1.24415953e-04,  3.62797159e-05, -1.92861309e-05],
-       [ 3.62797159e-05,  5.05717387e-05, -3.30909684e-05],
-       [-1.92861309e-05, -3.30909684e-05,  2.40875808e-05]]), scale=0.01759681555398639, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=46, candidate_x=array([ 4.58872111, 22.19363847, 44.99021442]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-26.1619876199946, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 32, 33, 36, 37, 39, 40, 42, 43, 44, 45]), old_indices_discarded=array([30, 34, 35, 38, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=0.01757476198331337, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 31, 32, 33, 36, 37, 39, 42, 43, 44, 45, 46]), model=ScalarModel(intercept=8.155649602120937, linear_terms=array([0.0026527 , 0.01104537, 0.01730511]), square_terms=array([[2.91872458e-04, 6.56347767e-05, 1.19971892e-04],
+       [6.56347767e-05, 2.36700506e-05, 3.92286792e-05],
+       [1.19971892e-04, 3.92286792e-05, 7.08303714e-05]]), scale=0.01757476198331337, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -27999,12 +27973,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=47, candidate_x=array([ 4.61787625, 21.87657563, 45.05775463]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-2.33432645935296, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 32, 34, 37, 38, 39, 40, 42, 44, 45, 46]), old_indices_discarded=array([33, 35, 36, 41, 43]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=0.008798407776993195, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 44, 45, 46, 47]), model=ScalarModel(intercept=7.829512018231579, linear_terms=array([0.15599134, 0.21217729, 0.09591715]), square_terms=array([[0.00518646, 0.00660101, 0.00272495],
-       [0.00660101, 0.0086599 , 0.00360026],
-       [0.00272495, 0.00360026, 0.00179299]]), scale=0.008798407776993195, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=47, candidate_x=array([ 4.59363561, 22.21582116, 44.99246248]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-3.1875317482232464, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 31, 32, 33, 36, 37, 39, 42, 43, 44, 45, 46]), old_indices_discarded=array([34, 35, 38, 40, 41]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=0.008787380991656685, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 44, 45, 46, 47]), model=ScalarModel(intercept=7.830994672212957, linear_terms=array([ 0.12582699, -0.13106438,  0.04819067]), square_terms=array([[ 0.00349529, -0.00314255,  0.0012651 ],
+       [-0.00314255,  0.00296258, -0.00120124],
+       [ 0.0012651 , -0.00120124,  0.00070836]]), scale=0.008787380991656685, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28086,12 +28060,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=48, candidate_x=array([ 4.60064833, 21.88172258, 45.04583162]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-0.8218389380362578, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 44, 45, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=0.0043992038884965974, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 44, 45, 47, 48]), model=ScalarModel(intercept=7.9729155715724485, linear_terms=array([-0.02606058,  0.01829026,  0.02313683]), square_terms=array([[ 1.64541397e-04, -3.08707103e-05, -1.04712277e-04],
-       [-3.08707103e-05,  1.92145365e-04, -3.82529600e-06],
-       [-1.04712277e-04, -3.82529600e-06,  1.53560946e-04]]), scale=0.0043992038884965974, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=48, candidate_x=array([ 4.59054761, 22.23174741, 45.00444793]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-1.200376326073863, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 44, 45, 46, 47]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=0.004393690495828342, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 0, 45, 47, 48]), model=ScalarModel(intercept=7.8160422308762705, linear_terms=array([-0.04172222,  0.08428649, -0.06711791]), square_terms=array([[ 0.0003991 , -0.00052348,  0.00045604],
+       [-0.00052348,  0.00132767, -0.00090953],
+       [ 0.00045604, -0.00090953,  0.00078764]]), scale=0.004393690495828342, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28173,12 +28147,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=49, candidate_x=array([ 4.60922111, 21.88511094, 45.04712049]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-2.255319553354316, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 44, 45, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=0.0021996019442482987, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=8.081963624024892, linear_terms=array([-0.02854491,  0.02481454,  0.03088891]), square_terms=array([[ 0.00011768, -0.00011494, -0.0001438 ],
-       [-0.00011494,  0.00013011,  0.00015839],
-       [-0.0001438 ,  0.00015839,  0.00019386]]), scale=0.0021996019442482987, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=49, candidate_x=array([ 4.59751749, 22.22224366, 45.00988712]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-0.7887842817376451, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 0, 45, 47, 48]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=0.002196845247914171, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), model=ScalarModel(intercept=8.075768245888694, linear_terms=array([ 0.00719986,  0.00325555, -0.02559561]), square_terms=array([[ 3.21776955e-05,  2.88151244e-06, -5.87746271e-05],
+       [ 2.88151244e-06,  5.78560573e-06, -1.47550086e-05],
+       [-5.87746271e-05, -1.47550086e-05,  1.28835138e-04]]), scale=0.002196845247914171, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28260,12 +28234,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=62, candidate_x=array([ 4.60764215, 21.88608711, 45.04832767]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-6.10545228756748, accepted=False, new_indices=array([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_used=array([45, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=0.0010998009721241494, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 50, 51, 52, 53, 55, 56, 57, 58, 60, 61, 62]), model=ScalarModel(intercept=8.087475387085085, linear_terms=array([ 0.00129737, -0.00122677, -0.00198736]), square_terms=array([[ 2.45787169e-06, -6.84061254e-07, -1.10872473e-06],
-       [-6.84061254e-07,  2.49120277e-07,  4.03993967e-07],
-       [-1.10872473e-06,  4.03993967e-07,  6.55547243e-07]]), scale=0.0010998009721241494, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=62, candidate_x=array([ 4.59517945, 22.22494079, 45.00927404]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-11.216964734279573, accepted=False, new_indices=array([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61]), old_indices_used=array([45, 48, 49]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=0.0010984226239570856, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 50, 51, 52, 53, 54, 55, 57, 58, 60, 61, 62]), model=ScalarModel(intercept=8.085231899896462, linear_terms=array([0.00369885, 0.00150388, 0.01109945]), square_terms=array([[6.01044157e-06, 1.43647205e-06, 1.05478566e-05],
+       [1.43647205e-06, 3.78294551e-07, 2.78012590e-06],
+       [1.05478566e-05, 2.78012590e-06, 2.04329212e-05]]), scale=0.0010984226239570856, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28347,12 +28321,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=63, candidate_x=array([ 4.60581204, 21.88768551, 45.05054657]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-199.53440596042915, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 50, 51, 52, 53, 55, 56, 57, 58, 60, 61, 62]), old_indices_discarded=array([49, 54, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=0.0005499004860620747, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 51, 52, 53, 55, 56, 57, 58, 60, 61, 62, 63]), model=ScalarModel(intercept=8.109489575083733, linear_terms=array([-0.00337006,  0.00152369,  0.00918076]), square_terms=array([[ 1.11624114e-06, -6.30581780e-07, -3.87633793e-06],
-       [-6.30581780e-07,  4.09288352e-07,  2.51526649e-06],
-       [-3.87633793e-06,  2.51526649e-06,  1.55246939e-05]]), scale=0.0005499004860620747, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=63, candidate_x=array([ 4.59541528, 22.22507437, 45.00613819]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-45.05549186610429, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 50, 51, 52, 53, 54, 55, 57, 58, 60, 61, 62]), old_indices_discarded=array([49, 56, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=0.0005492113119785428, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 51, 52, 53, 54, 55, 57, 58, 60, 61, 62, 63]), model=ScalarModel(intercept=8.11223763941749, linear_terms=array([-0.00636154, -0.00182872, -0.01097363]), square_terms=array([[5.44966507e-06, 1.78464937e-06, 1.08616010e-05],
+       [1.78464937e-06, 6.00708238e-07, 3.65185601e-06],
+       [1.08616010e-05, 3.65185601e-06, 2.22249746e-05]]), scale=0.0005492113119785428, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28434,12 +28408,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=64, candidate_x=array([ 4.60653108, 21.88709459, 45.04921765]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-19.782135357924695, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 51, 52, 53, 55, 56, 57, 58, 60, 61, 62, 63]), old_indices_discarded=array([50, 54, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=0.00027495024303103734, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=8.157976218345157, linear_terms=array([-0.02173612, -0.00263809,  0.05163706]), square_terms=array([[ 1.00359903e-04, -1.21221162e-04, -1.70990031e-04],
-       [-1.21221162e-04,  5.63382747e-04, -2.73016014e-05],
-       [-1.70990031e-04, -2.73016014e-05,  4.23758840e-04]]), scale=0.00027495024303103734, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=64, candidate_x=array([ 4.59602783, 22.22529589, 45.00764351]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-15.252297150038775, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 51, 52, 53, 54, 55, 57, 58, 60, 61, 62, 63]), old_indices_discarded=array([50, 56, 59]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=0.0002746056559892714, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), model=ScalarModel(intercept=8.169207520801116, linear_terms=array([-0.02131673, -0.00536662, -0.0372964 ]), square_terms=array([[7.36354315e-05, 1.63697546e-05, 1.01686629e-04],
+       [1.63697546e-05, 5.57444459e-06, 4.45403315e-05],
+       [1.01686629e-04, 4.45403315e-05, 3.89174202e-04]]), scale=0.0002746056559892714, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28521,12 +28495,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=77, candidate_x=array([ 4.60644849, 21.88719057, 45.04947353]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-6.987650928066692, accepted=False, new_indices=array([65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), old_indices_used=array([45, 63, 64]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=0.00013747512151551867, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 76]), model=ScalarModel(intercept=8.166078489007557, linear_terms=array([-0.00779963, -0.00238974,  0.01208643]), square_terms=array([[ 9.65421153e-06,  3.01672834e-06, -1.52569357e-05],
-       [ 3.01672834e-06,  9.44262966e-07, -4.77554509e-06],
-       [-1.52569357e-05, -4.77554509e-06,  2.41519917e-05]]), scale=0.00013747512151551867, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=77, candidate_x=array([ 4.59588922, 22.2252528 , 45.0074103 ]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-9.064443762512273, accepted=False, new_indices=array([65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]), old_indices_used=array([45, 63, 64]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=0.0001373028279946357, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77]), model=ScalarModel(intercept=8.16649973123999, linear_terms=array([0.00544442, 0.00151066, 0.01450806]), square_terms=array([[5.22485165e-06, 1.40201953e-06, 1.34739096e-05],
+       [1.40201953e-06, 3.77257365e-07, 3.62555674e-06],
+       [1.34739096e-05, 3.62555674e-06, 3.48427578e-05]]), scale=0.0001373028279946357, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28608,12 +28582,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=78, candidate_x=array([ 4.60641768, 21.88720284, 45.04961369]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-22.32261983084995, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 76]), old_indices_discarded=array([64, 75, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=6.873756075775934e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 78]), model=ScalarModel(intercept=8.163684606657888, linear_terms=array([-0.00343677, -0.00195969,  0.00572196]), square_terms=array([[ 7.90273200e-06,  1.31087603e-06, -1.61146560e-05],
-       [ 1.31087603e-06,  5.88529406e-07, -2.39946743e-06],
-       [-1.61146560e-05, -2.39946743e-06,  3.30708052e-05]]), scale=6.873756075775934e-05, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=78, candidate_x=array([ 4.59570938, 22.22520252, 45.00704437]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-20.985794556684933, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77]), old_indices_discarded=array([64, 65, 66]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=6.865141399731785e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 67, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]), model=ScalarModel(intercept=8.163898431868743, linear_terms=array([0.00334727, 0.0014425 , 0.00635805]), square_terms=array([[5.04215673e-06, 1.54369072e-06, 1.27266263e-05],
+       [1.54369072e-06, 5.06181834e-07, 3.68846064e-06],
+       [1.27266263e-05, 3.68846064e-06, 3.34479521e-05]]), scale=6.865141399731785e-05, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28695,12 +28669,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=79, candidate_x=array([ 4.60637819, 21.88719965, 45.04967123]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-30.233062305938052, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 78]), old_indices_discarded=array([73, 75, 77]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=3.436878037887967e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91]), model=ScalarModel(intercept=7.96719393041435, linear_terms=array([ 0.01764197,  0.00556742, -0.02780435]), square_terms=array([[ 8.41086276e-05,  2.01621891e-05, -1.29324894e-04],
-       [ 2.01621891e-05,  6.92675250e-06, -3.19984668e-05],
-       [-1.29324894e-04, -3.19984668e-05,  1.99325666e-04]]), scale=3.436878037887967e-05, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=79, candidate_x=array([ 4.59572605, 22.22520229, 45.00711273]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-29.13082416298415, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 67, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78]), old_indices_discarded=array([65, 66, 68]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=3.4325706998658925e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91]), model=ScalarModel(intercept=7.969197338008739, linear_terms=array([-0.01187119, -0.00321192, -0.03108092]), square_terms=array([[3.14712776e-05, 7.83542115e-06, 8.69639134e-05],
+       [7.83542115e-06, 2.68838568e-06, 1.81356582e-05],
+       [8.69639134e-05, 1.81356582e-05, 2.57101530e-04]]), scale=3.4325706998658925e-05, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28782,12 +28756,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=92, candidate_x=array([ 4.60632701, 21.88717382, 45.04975675]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-4.845444849805127, accepted=False, new_indices=array([80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91]), old_indices_used=array([45, 78, 79]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=1.7184390189439834e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 80, 81, 82, 83, 84, 85, 86, 88, 90, 91, 92]), model=ScalarModel(intercept=7.961188545938655, linear_terms=array([0.00085392, 0.00316381, 0.00482155]), square_terms=array([[1.31190062e-07, 4.74189184e-07, 7.22649332e-07],
-       [4.74189184e-07, 1.71659122e-06, 2.61603077e-06],
-       [7.22649332e-07, 2.61603077e-06, 3.98674823e-06]]), scale=1.7184390189439834e-05, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=92, candidate_x=array([ 4.59576886, 22.22521969, 45.00720444]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-4.900476000431597, accepted=False, new_indices=array([80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91]), old_indices_used=array([45, 78, 79]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=1.7162853499329462e-05, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 80, 81, 83, 84, 85, 86, 87, 88, 89, 90, 92]), model=ScalarModel(intercept=7.964319113453259, linear_terms=array([ 0.00191686, -0.00188917,  0.00241938]), square_terms=array([[ 6.39124995e-07, -6.21180993e-07,  7.95519589e-07],
+       [-6.21180993e-07,  6.03935733e-07, -7.73434332e-07],
+       [ 7.95519589e-07, -7.73434332e-07,  9.90503846e-07]]), scale=1.7162853499329462e-05, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28869,12 +28843,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=93, candidate_x=array([ 4.60634215, 21.8871706 , 45.04971369]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-43.43379695693431, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 80, 81, 82, 83, 84, 85, 86, 88, 90, 91, 92]), old_indices_discarded=array([79, 87, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=8.592195094719917e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 80, 81, 82, 83, 84, 86, 88, 90, 91, 92, 93]), model=ScalarModel(intercept=7.971987722718319, linear_terms=array([-0.00168489, -0.00196448, -0.00305946]), square_terms=array([[5.79564597e-07, 8.11692412e-07, 1.21867980e-06],
-       [8.11692412e-07, 1.27620628e-06, 1.87589356e-06],
-       [1.21867980e-06, 1.87589356e-06, 2.76771135e-06]]), scale=8.592195094719917e-06, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=93, candidate_x=array([ 4.59574791, 22.22522511, 45.00716095]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-70.706736157059, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 80, 81, 83, 84, 85, 86, 87, 88, 89, 90, 92]), old_indices_discarded=array([79, 82, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=8.581426749664731e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([45, 80, 81, 83, 84, 85, 86, 88, 89, 90, 92, 93]), model=ScalarModel(intercept=7.973922094870431, linear_terms=array([-0.00140109,  0.00175076, -0.00139797]), square_terms=array([[ 5.39378271e-07, -5.69987369e-07,  7.60616602e-07],
+       [-5.69987369e-07,  6.36258606e-07, -7.37234319e-07],
+       [ 7.60616602e-07, -7.37234319e-07,  1.20371667e-06]]), scale=8.581426749664731e-06, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -28956,13 +28930,13 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=94, candidate_x=array([ 4.60634831, 21.8871841 , 45.04973448]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-85.99905363642286, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 80, 81, 82, 83, 84, 86, 88, 90, 91, 92, 93]), old_indices_discarded=array([85, 87, 89]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=4.2960975473599584e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 45,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,
-       105, 106]), model=ScalarModel(intercept=8.008348977857061, linear_terms=array([ 0.01881916, -0.00133711, -0.00096365]), square_terms=array([[ 7.53976177e-05, -4.05873970e-06, -2.03762050e-06],
-       [-4.05873970e-06,  7.88599885e-06,  1.16552545e-05],
-       [-2.03762050e-06,  1.16552545e-05,  1.74408942e-05]]), scale=4.2960975473599584e-06, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=94, candidate_x=array([ 4.59576153, 22.22521045, 45.00717697]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-130.1823917961646, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([45, 80, 81, 83, 84, 85, 86, 88, 89, 90, 92, 93]), old_indices_discarded=array([82, 87, 91]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=4.290713374832366e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 45,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,
+       105, 106]), model=ScalarModel(intercept=8.009765521084711, linear_terms=array([ 0.00151823, -0.01101754, -0.00756228]), square_terms=array([[ 7.05171811e-06, -9.96915842e-06,  5.95649977e-06],
+       [-9.96915842e-06,  3.24848272e-05,  9.78332984e-06],
+       [ 5.95649977e-06,  9.78332984e-06,  2.30546273e-05]]), scale=4.290713374832366e-06, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -29044,12 +29018,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=107, candidate_x=array([ 4.60634041, 21.88718019, 45.04972814]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-10.181356638236267, accepted=False, new_indices=array([ 95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106]), old_indices_used=array([45, 93, 94]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=2.1480487736799792e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 45,  95,  96,  98,  99, 100, 101, 103, 104, 105, 106, 107]), model=ScalarModel(intercept=7.990879872204247, linear_terms=array([-0.00176748,  0.00039348,  0.00237276]), square_terms=array([[ 5.68744030e-07, -1.26769518e-07, -7.64442598e-07],
-       [-1.26769518e-07,  2.82563247e-08,  1.70390631e-07],
-       [-7.64442598e-07,  1.70390631e-07,  1.02748562e-06]]), scale=2.1480487736799792e-06, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=107, candidate_x=array([ 4.59575655, 22.22521969, 45.0071748 ]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-14.358884588498027, accepted=False, new_indices=array([ 95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106]), old_indices_used=array([45, 93, 94]), old_indices_discarded=array([], dtype=int64), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=2.145356687416183e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 45,  95,  96,  97,  98,  99, 100, 101, 103, 105, 106, 107]), model=ScalarModel(intercept=7.9921450285659414, linear_terms=array([ 0.0010261 ,  0.00195493, -0.00044703]), square_terms=array([[ 1.93050671e-07,  3.66858480e-07, -8.38887823e-08],
+       [ 3.66858480e-07,  6.97162043e-07, -1.59418626e-07],
+       [-8.38887823e-08, -1.59418626e-07,  3.64539329e-08]]), scale=2.145356687416183e-06, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -29131,12 +29105,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=108, candidate_x=array([ 4.60634596, 21.88717961, 45.04972621]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-129.68804031688902, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45,  95,  96,  98,  99, 100, 101, 103, 104, 105, 106, 107]), old_indices_discarded=array([ 94,  97, 102]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=1.0740243868399896e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 45,  95,  96,  98,  99, 100, 101, 104, 105, 106, 107, 108]), model=ScalarModel(intercept=8.006869024524448, linear_terms=array([ 0.0040133 , -0.0031247 , -0.00689686]), square_terms=array([[ 2.68593565e-06, -2.09103005e-06, -4.61426805e-06],
-       [-2.09103005e-06,  1.63491637e-06,  3.59405742e-06],
-       [-4.61426805e-06,  3.59405742e-06,  7.92748398e-06]]), scale=1.0740243868399896e-06, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=108, candidate_x=array([ 4.59575601, 22.22521428, 45.00717285]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-171.98821208652902, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45,  95,  96,  97,  98,  99, 100, 101, 103, 105, 106, 107]), old_indices_discarded=array([ 94, 102, 104]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=1.0726783437080914e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 45,  95,  96,  97,  98,  99, 100, 101, 103, 105, 106, 108]), model=ScalarModel(intercept=8.008185418676952, linear_terms=array([-0.00178053, -0.00681805,  0.00334466]), square_terms=array([[ 5.30670694e-07,  2.01549302e-06, -9.92577240e-07],
+       [ 2.01549302e-06,  7.74206430e-06, -3.79425153e-06],
+       [-9.92577240e-07, -3.79425153e-06,  1.86338282e-06]]), scale=1.0726783437080914e-06, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -29218,12 +29192,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=109, candidate_x=array([ 4.60634419, 21.88718029, 45.04972878]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-61.96691318760934, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45,  95,  96,  98,  99, 100, 101, 104, 105, 106, 107, 108]), old_indices_discarded=array([ 97, 102, 103]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 45,  95,  96,  98,  99, 100, 101, 104, 106, 107, 108, 109]), model=ScalarModel(intercept=8.035927624859937, linear_terms=array([ 0.00034502,  0.00057461, -0.00334754]), square_terms=array([[ 8.51597811e-07, -1.15320590e-06, -1.72300524e-06],
-       [-1.15320590e-06,  1.73813218e-06,  1.89147335e-06],
-       [-1.72300524e-06,  1.89147335e-06,  4.74307352e-06]]), scale=1e-06, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=109, candidate_x=array([ 4.59575723, 22.22521708, 45.00717198]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-68.01659358670524, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45,  95,  96,  97,  98,  99, 100, 101, 103, 105, 106, 108]), old_indices_discarded=array([102, 104, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 45,  95,  96,  97,  98,  99, 100, 101, 105, 106, 108, 109]), model=ScalarModel(intercept=8.03835125751088, linear_terms=array([-0.00461255,  0.00096586,  0.00148282]), square_terms=array([[ 3.95696422e-06, -2.17409459e-06, -8.21544424e-07],
+       [-2.17409459e-06,  4.89255929e-06, -9.18493452e-07],
+       [-8.21544424e-07, -9.18493452e-07,  6.89457734e-07]]), scale=1e-06, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -29305,12 +29279,12 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=110, candidate_x=array([ 4.60634459, 21.88717973, 45.0497289 ]), index=45, x=array([ 4.60634469, 21.8871799 , 45.04972792]), fval=7.815860945360358, rho=-78.87157499617388, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45,  95,  96,  98,  99, 100, 101, 104, 106, 107, 108, 109]), old_indices_discarded=array([ 97, 102, 103, 105]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.60634469, 21.8871799 , 45.04972792]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 45,  95,  96,  98,  99, 100, 101, 106, 107, 108, 109, 110]), model=ScalarModel(intercept=8.041259710326003, linear_terms=array([-4.05719661e-05,  1.39959914e-04, -3.93032804e-04]), square_terms=array([[ 1.28829208e-06, -1.10310706e-06, -3.73960799e-06],
-       [-1.10310706e-06,  9.89638466e-07,  3.11184786e-06],
-       [-3.73960799e-06,  3.11184786e-06,  1.11568352e-05]]), scale=1e-06, shift=array([ 4.60634469, 21.8871799 , 45.04972792])), vector_model=VectorModel(intercepts=array([0.03090559, 0.07627597, 0.08410847, 0.12802702, 0.17144909,
-       0.22284329, 0.29117985, 0.63523094, 0.74779403, 1.07417342,
-       1.09055049, 1.46002679, 0.80786398, 0.7588734 , 0.6839269 ,
-       0.57209968, 0.50023216]), linear_terms=array([[0., 0., 0.],
+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=110, candidate_x=array([ 4.59575792, 22.22521595, 45.00717213]), index=45, x=array([ 4.59575699, 22.22521614, 45.00717243]), fval=7.816042230876271, rho=-54.28818040104223, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([ 45,  95,  96,  97,  98,  99, 100, 101, 105, 106, 108, 109]), old_indices_discarded=array([102, 103, 104, 107]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([ 4.59575699, 22.22521614, 45.00717243]), radius=1e-06, bounds=Bounds(lower=array([ 2.,  0., 20.]), upper=array([20., 70., 70.]))), model_indices=array([ 45,  96,  97,  98,  99, 100, 101, 105, 106, 108, 109, 110]), model=ScalarModel(intercept=8.044010446782652, linear_terms=array([-0.00306728,  0.00240378,  0.00084139]), square_terms=array([[ 1.67154840e-06, -6.71899414e-07, -5.82989675e-07],
+       [-6.71899414e-07,  1.09594462e-05, -2.71875232e-06],
+       [-5.82989675e-07, -2.71875232e-06,  1.08428635e-06]]), scale=1e-06, shift=array([ 4.59575699, 22.22521614, 45.00717243])), vector_model=VectorModel(intercepts=array([0.03072718, 0.07584085, 0.08347188, 0.12720477, 0.17044688,
+       0.22160574, 0.28976183, 0.63330038, 0.74571708, 1.07232581,
+       1.08763664, 1.45620583, 0.81428665, 0.76413099, 0.68847568,
+       0.5758324 , 0.50364734]), linear_terms=array([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
@@ -29392,7 +29366,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
 
        [[0., 0., 0.],
         [0., 0., 0.],
-        [0., 0., 0.]]]), scale=4.504784781820516, shift=array([ 4.6220078 , 21.87938374, 45.04784782])), candidate_index=111, candidate_x=array([ 4.60634481, 21.88717956, 45.04972885]), index=111, x=array([ 4.60634481, 21.88717956, 45.04972885]), fval=7.788386782653423, rho=66.12168627861587, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 45,  95,  96,  98,  99, 100, 101, 106, 107, 108, 109, 110]), old_indices_discarded=array([ 97, 102, 103, 104, 105]), step_length=9.999999985600382e-07, relative_step_length=0.9999999985600383, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 112 entries., 'history': {'params': [{'CRRA': 4.622007804545175, 'BeqShift': 21.879383736410876, 'BeqFac': 45.047847818205156}, {'CRRA': 2.2260447753432366, 'BeqShift': 18.248545324975623, 'BeqFac': 47.94982696230849}, {'CRRA': 8.182709343173336, 'BeqShift': 18.248545324975623, 'BeqFac': 48.358877771074575}, {'CRRA': 8.25284621598043, 'BeqShift': 25.35209191889481, 'BeqFac': 48.001735340802604}, {'CRRA': 5.033825606132808, 'BeqShift': 25.391561263046473, 'BeqFac': 41.4170094067699}, {'CRRA': 4.492416726426665, 'BeqShift': 25.51022214784613, 'BeqFac': 48.63548155450485}, {'CRRA': 8.25284621598043, 'BeqShift': 25.199472959879948, 'BeqFac': 48.079632641978634}, {'CRRA': 2.046507033562174, 'BeqShift': 25.51022214784613, 'BeqFac': 45.94508732236388}, {'CRRA': 2.0, 'BeqShift': 21.568135716547204, 'BeqFac': 48.52867640246565}, {'CRRA': 8.162765986815081, 'BeqShift': 22.90121294059893, 'BeqFac': 41.4170094067699}, {'CRRA': 5.150365371768842, 'BeqShift': 18.248545324975623, 'BeqFac': 41.701710428823176}, {'CRRA': 2.0, 'BeqShift': 24.482894484365502, 'BeqFac': 41.56316318491201}, {'CRRA': 8.03239179098238, 'BeqShift': 18.248545324975623, 'BeqFac': 48.50212478178833}, {'CRRA': 5.127668361050633, 'BeqShift': 18.248545324975623, 'BeqFac': 41.4170094067699}, {'CRRA': 5.311949996710708, 'BeqShift': 20.275841947632458, 'BeqFac': 43.62248422529479}, {'CRRA': 5.739685243499719, 'BeqShift': 21.918467422111178, 'BeqFac': 44.91523002670756}, {'CRRA': 4.989174979461541, 'BeqShift': 22.03967031837645, 'BeqFac': 44.652151034076034}, {'CRRA': 4.244507137337031, 'BeqShift': 21.64561798804183, 'BeqFac': 45.39415157043904}, {'CRRA': 4.088389551446555, 'BeqShift': 22.056771531533695, 'BeqFac': 45.077251861563916}, {'CRRA': 4.493271292970869, 'BeqShift': 22.38747285513172, 'BeqFac': 45.25364341021226}, {'CRRA': 5.044007359025966, 'BeqShift': 21.682731912532, 'BeqFac': 45.36458730713498}, {'CRRA': 4.354805236210627, 'BeqShift': 21.878591674542005, 'BeqFac': 44.55218500158703}, {'CRRA': 5.003276708356909, 'BeqShift': 22.23999709588951, 'BeqFac': 45.25198379155658}, {'CRRA': 4.568507024462446, 'BeqShift': 22.34933794526167, 'BeqFac': 44.742298843770016}, {'CRRA': 4.292443107936963, 'BeqShift': 21.46444559609255, 'BeqFac': 44.857340815386074}, {'CRRA': 4.604627816167372, 'BeqShift': 21.926012434792497, 'BeqFac': 45.60874278890433}, {'CRRA': 4.7545770026464576, 'BeqShift': 21.34354872876739, 'BeqFac': 45.15913896773149}, {'CRRA': 4.884843316902956, 'BeqShift': 21.54968992525231, 'BeqFac': 44.67462007005162}, {'CRRA': 4.442211023307086, 'BeqShift': 22.242396852104754, 'BeqFac': 45.49145567586931}, {'CRRA': 4.771894378226301, 'BeqShift': 21.935881476263525, 'BeqFac': 44.813062311750556}, {'CRRA': 4.719703965560183, 'BeqShift': 21.865248885771592, 'BeqFac': 44.94433425293832}, {'CRRA': 4.581337982432187, 'BeqShift': 21.824181819637776, 'BeqFac': 45.06375632649883}, {'CRRA': 4.591757282748623, 'BeqShift': 21.94291207065408, 'BeqFac': 45.04969805104899}, {'CRRA': 4.683019291430464, 'BeqShift': 21.877973926817702, 'BeqFac': 45.08291885450598}, {'CRRA': 4.601888853296095, 'BeqShift': 21.84476715963638, 'BeqFac': 44.989957515949}, {'CRRA': 4.620491212594748, 'BeqShift': 21.814421227199578, 'BeqFac': 45.02079344650512}, {'CRRA': 4.638841197023986, 'BeqShift': 21.835925925204698, 'BeqFac': 45.10059648856829}, {'CRRA': 4.573102898037877, 'BeqShift': 21.90630001132553, 'BeqFac': 45.004973689100446}, {'CRRA': 4.652605350284186, 'BeqShift': 21.907213230162828, 'BeqFac': 44.990894537750776}, {'CRRA': 4.602131097321572, 'BeqShift': 21.907014702477394, 'BeqFac': 45.10945798706091}, {'CRRA': 4.573790616910264, 'BeqShift': 21.865170736152017, 'BeqFac': 44.99857855676886}, {'CRRA': 4.681102998670846, 'BeqShift': 21.850330489429354, 'BeqFac': 45.02298753756844}, {'CRRA': 4.657491053035934, 'BeqShift': 21.936514086345614, 'BeqFac': 45.068618684223054}, {'CRRA': 4.676442988949215, 'BeqShift': 21.86808420185974, 'BeqFac': 45.00467956713933}, {'CRRA': 4.598917032749433, 'BeqShift': 21.90115424772846, 'BeqFac': 45.0630613899558}, {'CRRA': 4.606344694808925, 'BeqShift': 21.88717989862962, 'BeqFac': 45.049727915460686}, {'CRRA': 4.632518482651829, 'BeqShift': 21.871456534076298, 'BeqFac': 45.067229352762865}, {'CRRA': 4.617876251053907, 'BeqShift': 21.876575632017328, 'BeqFac': 45.05775462892748}, {'CRRA': 4.600648332448165, 'BeqShift': 21.88172258029419, 'BeqFac': 45.04583162082976}, {'CRRA': 4.609221108111269, 'BeqShift': 21.885110939683916, 'BeqFac': 45.047120493314644}, {'CRRA': 4.6076202815839, 'BeqShift': 21.88723026551446, 'BeqFac': 45.0515191692868}, {'CRRA': 4.607698747173904, 'BeqShift': 21.888911530284815, 'BeqFac': 45.04980692632549}, {'CRRA': 4.607472197702472, 'BeqShift': 21.88549990263294, 'BeqFac': 45.05059081718703}, {'CRRA': 4.605778410399498, 'BeqShift': 21.88890488211978, 'BeqFac': 45.050969689449566}, {'CRRA': 4.605546519661048, 'BeqShift': 21.886969224159458, 'BeqFac': 45.05176673409266}, {'CRRA': 4.6064165778637784, 'BeqShift': 21.88510709985695, 'BeqFac': 45.04899539461621}, {'CRRA': 4.605270454214987, 'BeqShift': 21.88644698384068, 'BeqFac': 45.047953911015476}, {'CRRA': 4.608501006506304, 'BeqShift': 21.887220450613032, 'BeqFac': 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+        [0., 0., 0.]]]), scale=4.499139067728223, shift=array([ 4.60300984, 22.22100549, 44.99139068])), candidate_index=111, candidate_x=array([ 4.59575776, 22.22521554, 45.00717222]), index=111, x=array([ 4.59575776, 22.22521554, 45.00717222]), fval=7.791322680405853, rho=6.204424626203775, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([ 45,  96,  97,  98,  99, 100, 101, 105, 106, 108, 109, 110]), old_indices_discarded=array([ 95, 102, 103, 104, 107]), step_length=9.9999999973806e-07, relative_step_length=0.9999999997380602, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 112 entries., 'history': {'params': [{'CRRA': 4.6030098356790825, 'BeqShift': 22.22100549073852, 'BeqFac': 44.991390677282226}, {'CRRA': 2.1686670035088382, 'BeqShift': 18.594717502148992, 'BeqFac': 47.89902410308778}, {'CRRA': 8.215116066330635, 'BeqShift': 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45.00711273253762}, {'CRRA': 4.595781457898256, 'BeqShift': 22.22523968189662, 'BeqFac': 45.00717746627078}, {'CRRA': 4.595748646392313, 'BeqShift': 22.22524938507495, 'BeqFac': 45.0071705588096}, {'CRRA': 4.595745101067643, 'BeqShift': 22.225232423187403, 'BeqFac': 45.007144649028525}, {'CRRA': 4.595723392695329, 'BeqShift': 22.225222848149446, 'BeqFac': 45.007174569243716}, {'CRRA': 4.595757682445916, 'BeqShift': 22.225183204245337, 'BeqFac': 45.007182070851314}, {'CRRA': 4.595779095304809, 'BeqShift': 22.22521268226681, 'BeqFac': 45.00719846152999}, {'CRRA': 4.595776571902689, 'BeqShift': 22.22522668405593, 'BeqFac': 45.00714628507158}, {'CRRA': 4.595763446565303, 'BeqShift': 22.22519319038839, 'BeqFac': 45.0071477365774}, {'CRRA': 4.595742888404555, 'BeqShift': 22.225204529276922, 'BeqFac': 45.00720149284276}, {'CRRA': 4.595731055638077, 'BeqShift': 22.225195276223566, 'BeqFac': 45.007164041856754}, {'CRRA': 4.595787408086691, 'BeqShift': 22.225200626660282, 'BeqFac': 45.00716893520995}, {'CRRA': 4.595749802028708, 'BeqShift': 22.22523543094006, 'BeqFac': 45.00719989928936}, {'CRRA': 4.595768860882911, 'BeqShift': 22.225219694829107, 'BeqFac': 45.007204441542804}, {'CRRA': 4.595747912781513, 'BeqShift': 22.225225107758316, 'BeqFac': 45.00716095091317}, {'CRRA': 4.5957615299003365, 'BeqShift': 22.225210450845562, 'BeqFac': 45.007176973930626}, {'CRRA': 4.595760019728249, 'BeqShift': 22.22521910460297, 'BeqFac': 45.00717176597001}, {'CRRA': 4.595758708751953, 'BeqShift': 22.225216944394543, 'BeqFac': 45.00717627889929}, {'CRRA': 4.5957593910768875, 'BeqShift': 22.225213208333773, 'BeqFac': 45.00717042157384}, {'CRRA': 4.595752807309069, 'BeqShift': 22.2252152408403, 'BeqFac': 45.00717209243142}, {'CRRA': 4.595761172801362, 'BeqShift': 22.225215485522465, 'BeqFac': 45.00717311975225}, {'CRRA': 4.5957560915347, 'BeqShift': 22.225211945426807, 'BeqFac': 45.007172450394954}, {'CRRA': 4.595758487044665, 'BeqShift': 22.225217205688242, 'BeqFac': 45.0071685537516}, {'CRRA': 4.595754770820451, 'BeqShift': 22.225219111065627, 'BeqFac': 45.00717026971645}, {'CRRA': 4.59575396817088, 'BeqShift': 22.22521757279648, 'BeqFac': 45.007175121007386}, {'CRRA': 4.595755906480718, 'BeqShift': 22.22521384911636, 'BeqFac': 45.00717589285804}, {'CRRA': 4.595755201675691, 'BeqShift': 22.225214689375573, 'BeqFac': 45.00716881035402}, {'CRRA': 4.59575681833367, 'BeqShift': 22.225220197907284, 'BeqFac': 45.00717381818509}, {'CRRA': 4.5957565475300735, 'BeqShift': 22.225219692314067, 'BeqFac': 45.007174798466124}, {'CRRA': 4.595756014461826, 'BeqShift': 22.225214277289368, 'BeqFac': 45.00717285393025}, {'CRRA': 4.595757229431893, 'BeqShift': 22.225217082089948, 'BeqFac': 45.00717197538009}, {'CRRA': 4.595757922959899, 'BeqShift': 22.2252159469517, 'BeqFac': 45.00717213155224}, {'CRRA': 4.595757756927482, 'BeqShift': 22.225215537478146, 'BeqFac': 45.00717221765283}], 'criterion': [7.908703892454978, 24.354015534628513, 26.042817736301426, 26.17069024863219, 8.792396125096417, 8.507167052256625, 26.17069024863219, 27.696207243728846, 24.8492554555944, 26.08787814853432, 9.014151722855194, 29.039321100778334, 23.746744977545713, 8.784716082727853, 8.914700427051153, 9.016447199527112, 8.162633724756942, 8.169153904415174, 8.384574649214162, 7.94939059583106, 8.002861895211028, 8.065792399835658, 7.995192708096741, 7.904042116939473, 8.088098394769872, 7.912380177455075, 7.904804750534113, 7.971551335906759, 7.941670494485338, 8.149707855352208, 8.056482135652642, 8.235401436572236, 8.234988142491785, 8.243350916624019, 8.233742145105305, 8.23348695513823, 8.23663035890089, 8.238174640712534, 8.239126714353134, 8.2336930475348, 8.237920233392735, 8.243047292882464, 8.238172244958466, 8.245291075192837, 8.194181573233898, 7.816042230876272, 8.105548280922486, 7.88184812194913, 8.03641759998537, 7.906033468540016, 8.115440480007729, 8.1152493252223, 8.114948164773573, 8.115683864117612, 8.115700752607824, 8.115713917262731, 8.115813025209869, 8.115184380671398, 8.115635028344542, 8.115663268502852, 8.115705632181612, 8.115944101325182, 8.115648461746009, 8.346927105784792, 8.011290471290385, 8.206604236079741, 8.20660697646508, 8.206571100761565, 8.206604497957859, 8.206606642390227, 8.206597623205205, 8.206569327401086, 8.20660433805969, 8.206615364984053, 8.206591821677465, 8.206568593498465, 8.206622305942963, 8.206575846261448, 8.142352814844118, 8.02897452882541, 7.979115770709086, 7.979143542247974, 7.979146543156638, 7.979164918484806, 7.979135893900598, 7.9791177703110066, 7.979119906029858, 7.97913101510726, 7.979148416045366, 7.9791584319588935, 7.979110734775206, 7.979142564076598, 7.97912643246333, 8.071846219393372, 8.159904866266874, 8.008845804460774, 8.008846415225342, 8.008846097339703, 8.008849164677226, 8.008845267264428, 8.008847634562276, 8.008846518516005, 8.008848249877326, 8.008848623830637, 8.008847720777755, 8.008848049143214, 8.008847295951638, 8.008847422117006, 8.203391613036608, 8.346228418892789, 8.084114293732494, 7.791322680405853], 'runtime': [0.0, 1.467822182981763, 1.6736844489932992, 1.882481053005904, 2.0830091389943846, 2.324024801986525, 2.506373612995958, 2.7061985659820493, 2.8981667519838084, 3.113962506002281, 3.3480925129842944, 3.540149715001462, 3.7538298899889924, 4.976753445982467, 6.077797271980671, 7.166567100008251, 8.64832116200705, 8.840608445985708, 9.039025879988912, 9.252943317987956, 9.602900341007626, 9.81306773799588, 10.023079011996742, 10.260854837979423, 10.435564023006009, 10.674281547981082, 10.943407919985475, 11.16077995399246, 12.349485140992329, 13.459667338000145, 14.53581642100471, 16.027221194992308, 16.227292311989004, 16.423276796005666, 16.664927256002557, 16.84623675898183, 17.05827632098226, 17.344185463007307, 17.559184985992033, 17.800149238988524, 17.973481822002213, 18.18430495800567, 18.40490086999489, 19.61003144699498, 20.72558624300291, 21.97143481200328, 23.071054075000575, 24.18709500800469, 25.31175472898758, 26.405791375000263, 27.847302138980012, 28.051393632980762, 28.25948259999859, 28.49071983198519, 28.68191400100477, 28.891587945981883, 29.128893329994753, 29.36047214298742, 29.605695912992815, 29.83540926198475, 30.051718475995585, 30.25172810899676, 31.513942460005637, 32.723557385994354, 33.81817759599653, 35.22017287599738, 35.429388958000345, 35.621025306987576, 35.83923009899445, 36.05087348498637, 36.26455218999763, 36.492123996984446, 36.729372725007124, 36.94044939300511, 37.18360538300476, 37.40170505997958, 37.61402891599573, 38.95400295898435, 40.080736489006085, 41.200933241983876, 42.61655236899969, 42.808437509986106, 43.027956486999756, 43.25584368198179, 43.62841733798268, 43.86705839500064, 44.07551298398175, 44.293850928981556, 44.49158935699961, 44.70258724098676, 44.93024832798983, 45.1434052529803, 46.3527250670013, 47.449062968982616, 48.53478102199733, 50.032614188006846, 50.23048215999734, 50.43948698998429, 50.65542563199415, 50.88813652598765, 51.115212184988195, 51.373782215989195, 51.5619246159913, 51.79838335499517, 51.984175304998644, 52.231052438000916, 52.44462518999353, 53.63236635798239, 54.729435732995626, 55.9801713549823, 57.10864428099012, 58.21862255400629], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 12, 13, 14, 15, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 19, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 23, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 27, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 31, 32, 33, 34]}}], 'exploration_sample': array([[ 4.70567639, 16.96622308, 46.46531825],
        [ 4.8125    , 10.9375    , 46.5625    ],
        [ 4.25      , 43.75      , 38.75      ],
        [ 5.375     , 21.875     , 66.875     ],
@@ -29421,7 +29395,7 @@ algorithm_output,"{'states': [State(trustregion=Region(center=array([ 4.49799881
        [17.75      , 61.25      , 26.25      ],
        [18.3125    , 28.4375    , 59.0625    ],
        [18.875     ,  4.375     , 54.375     ],
-       [19.4375    , 41.5625    , 37.1875    ]]), 'exploration_results': array([  7.8484842 ,   8.04234718,   8.48661498,   8.95471279,
+       [19.4375    , 41.5625    , 37.1875    ]]), 'exploration_results': array([  7.81310671,   8.04234718,   8.48661498,   8.95471279,
          9.52815641,  10.27586568,  12.00230193,  12.3747888 ,
         16.07272175,  20.10794912,  23.5871464 ,  29.56480172,
         34.04180187,  40.97293748,  51.53022936,  55.11303949,
diff --git a/content/tables/TRP/WealthPortfolioShareOnlySub(Stock)Market_estimate_results.csv b/content/tables/TRP/WealthPortfolioShareOnlySub(Stock)Market_estimate_results.csv
deleted file mode 100644
index a748060..0000000
--- a/content/tables/TRP/WealthPortfolioShareOnlySub(Stock)Market_estimate_results.csv
+++ /dev/null
@@ -1,8273 +0,0 @@
-CRRA,2.0
-WealthShare,0.5743521335960693
-time_to_estimate,106.7550938129425
-params,"{'CRRA': 2.0, 'WealthShare': 0.5743521335960693}"
-criterion,0.4458024296501866
-start_criterion,5.798287399334166
-start_params,"{'CRRA': 6.083993580373348, 'WealthShare': 0.5}"
-algorithm,multistart_tranquilo_ls
-direction,minimize
-n_free,2
-message,Absolute criterion change smaller than tolerance.
-success,
-n_criterion_evaluations,
-n_derivative_evaluations,
-n_iterations,
-history,"{'params': [{'CRRA': 3.125, 'WealthShare': 0.6562500000000001}, {'CRRA': 2.8877532906292425, 'WealthShare': 0.3793040857960132}, {'CRRA': 3.4019459142039867, 'WealthShare': 0.6381804033892549}, {'CRRA': 2.8480540857960133, 'WealthShare': 0.643495893144851}, {'CRRA': 3.4019459142039867, 'WealthShare': 0.6996416572176442}, {'CRRA': 3.4019459142039867, 'WealthShare': 0.5035998228203895}, {'CRRA': 3.4019459142039867, 'WealthShare': 0.4654562407159629}, {'CRRA': 3.0420375065377327, 'WealthShare': 0.7}, {'CRRA': 3.4019459142039867, 'WealthShare': 0.6480071852517115}, {'CRRA': 3.4019459142039867, 'WealthShare': 0.6961507292207706}, {'CRRA': 2.8480540857960133, 'WealthShare': 0.6528878978426165}, {'CRRA': 3.1962521589007267, 'WealthShare': 0.3793040857960132}, {'CRRA': 3.2804766548165785, 'WealthShare': 0.7}, {'CRRA': 3.0937739301816674, 'WealthShare': 0.6737108106245127}, {'CRRA': 2.9865270428980066, 'WealthShare': 0.5177770428980066}, {'CRRA': 3.0557635214490033, 'WealthShare': 0.6118879406018319}, {'CRRA': 2.91729056434701, 'WealthShare': 0.6252660935878529}, {'CRRA': 2.8480540857960133, 'WealthShare': 0.6464187049615613}, {'CRRA': 2.886742769892113, 'WealthShare': 0.6003605172329373}, {'CRRA': 2.809635033650277, 'WealthShare': 0.5864485515223637}, {'CRRA': 2.7067587087900864, 'WealthShare': 0.6590753551197297}, {'CRRA': 2.7656941604719103, 'WealthShare': 0.6522297032750187}, {'CRRA': 2.7704170474633156, 'WealthShare': 0.5941668290094727}, {'CRRA': 2.793605132442605, 'WealthShare': 0.5740332459677072}, {'CRRA': 2.7545095199042655, 'WealthShare': 0.5727108799815052}, {'CRRA': 2.6764192103579307, 'WealthShare': 0.5757603791310802}, {'CRRA': 2.5379462532559374, 'WealthShare': 0.580218816756151}, {'CRRA': 2.2610003390519506, 'WealthShare': 0.5760357609376121}, {'CRRA': 2.0, 'WealthShare': 0.5792295083032165}, {'CRRA': 2.553891828407974, 'WealthShare': 0.025337679895242715}, {'CRRA': 2.0, 'WealthShare': 0.5701969737360522}, {'CRRA': 2.0, 'WealthShare': 0.7}, {'CRRA': 2.0, 'WealthShare': 0.5830253257046072}, {'CRRA': 2.1384729571019934, 'WealthShare': 0.4407565512012231}, {'CRRA': 2.0, 'WealthShare': 0.6187452252789505}, {'CRRA': 2.0, 'WealthShare': 0.615946802561197}, {'CRRA': 2.0346182392754986, 'WealthShare': 0.5792295083032165}, {'CRRA': 2.0, 'WealthShare': 0.5763996717635504}, {'CRRA': 2.017309119637749, 'WealthShare': 0.5937087914012996}, {'CRRA': 2.0, 'WealthShare': 0.5760574232985666}, {'CRRA': 2.017309119637749, 'WealthShare': 0.5933665429173204}, {'CRRA': 2.0, 'WealthShare': 0.5759687001780883}, {'CRRA': 2.0, 'WealthShare': 0.5932778197507708}, {'CRRA': 2.0, 'WealthShare': 0.5753209050683805}, {'CRRA': 2.0, 'WealthShare': 0.5580117854306313}, {'CRRA': 2.017309119637749, 'WealthShare': 0.5753209050683805}, {'CRRA': 2.000000000052453, 'WealthShare': 0.5666663452495059}, {'CRRA': 2.0, 'WealthShare': 0.5749242354168713}, {'CRRA': 2.0086545598188748, 'WealthShare': 0.583578795235746}, {'CRRA': 2.0, 'WealthShare': 0.5749246651028804}, {'CRRA': 2.0043272799094374, 'WealthShare': 0.5749242354168713}, {'CRRA': 2.0, 'WealthShare': 0.5745368885419802}, {'CRRA': 2.0, 'WealthShare': 0.574350020666426}, {'CRRA': 2.0021636399547185, 'WealthShare': 0.574350020666426}, {'CRRA': 2.0, 'WealthShare': 0.5742545517651398}, {'CRRA': 2.0005409099886795, 'WealthShare': 0.574350020666426}, {'CRRA': 2.0, 'WealthShare': 0.5742765676646966}, {'CRRA': 2.0, 'WealthShare': 0.5743057874790445}, {'CRRA': 2.000067613748585, 'WealthShare': 0.574350020666426}, {'CRRA': 2.0, 'WealthShare': 0.5743316221270061}, {'CRRA': 2.0, 'WealthShare': 0.5743634000623333}, {'CRRA': 2.000008451718573, 'WealthShare': 0.5743500229114534}, {'CRRA': 2.0, 'WealthShare': 0.5743457948071394}, {'CRRA': 2.0, 'WealthShare': 0.5743521335960693}], 'criterion': [2.6485507328955578, 12309.019191923308, 2.7875108546838874, 2.2509690627291943, 3.2116637126420313, 7.769269445922345, 65.09447783484818, 2.868162880931596, 2.8701084637521097, 3.189286924034111, 2.3223449087505954, 12208.650402784679, 3.1010498085590203, 2.738606781194643, 5.24055381856353, 2.200339567352016, 2.157470119637134, 2.273086622587243, 1.9030712014018638, 1.6833473530893972, 2.213966174574849, 2.2248796097377914, 1.6945638119959199, 1.611312137233269, 1.5571821480458312, 1.451106305881791, 1.2661512832820518, 0.8337617949332514, 0.4652180568597024, nan, 0.46704478588651527, 2.007175336487229, 0.49849658966580906, 12287.906435768238, 1.0794347898842398, 1.0325216948696196, 0.5117654157871131, 0.45035797883588746, 0.6740501227389969, 0.4492051846572234, 0.6682171490205365, 0.4489641699450065, 0.6482426385430978, 0.4469758296464361, 0.9606033046222777, 0.4704847405034189, 0.5290009546380467, 0.4462215572280142, 0.5155206213465858, 0.44622215192959996, 0.451965707562142, 0.4458553100420242, 0.4458031683468515, 0.4486832541908095, 0.4458181211605623, 0.446518192227849, 0.4458108503591465, 0.4458115453297318, 0.44589151718099584, 0.4458065540242595, 0.44580704780938707, 0.4458140437932345, 0.4458065753878447, 0.4458024296501867], 'runtime': [0.0, 1.1379165299877059, 1.1788369659916498, 1.222313560982002, 1.271481898991624, 1.3128336559748277, 1.3646902149775997, 1.4130658529757056, 1.4538682449783664, 1.5001444679801352, 1.5513987049926072, 1.5932847759977449, 1.6745215870032553, 2.796865151001839, 3.7065433129901066, 4.633996711985674, 5.529494611982955, 6.402550692990189, 7.281059565983014, 8.177067625976633, 9.064791270997375, 9.942752303002635, 10.85382893300266, 11.728921710979193, 12.61486469997908, 13.486227588990005, 14.362521584989736, 15.24584228999447, 16.11966375799966, 17.022648105979897, 17.93754075097968, 18.866184728976805, 19.76942301300005, 20.68266301197582, 21.584630033001304, 22.46384815897909, 23.35887856897898, 24.2504323629837, 25.140152245992795, 26.030393078981433, 26.953723457001615, 27.838894469983643, 28.73019578299136, 29.624959079985274, 30.502454334986396, 31.38241345298593, 32.26468711000052, 33.14085441199131, 34.06507943299948, 34.98094516599667, 35.90926745100296, 36.83396410397836, 37.73956535899197, 38.626529995002784, 39.52222040499328, 40.424842537991935, 41.33055950899143, 42.235568575997604, 43.133661772997584, 44.00837361998856, 44.902821667987155, 45.77760492899688, 46.65120018098969, 47.537088937999215], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52]}"
-convergence_report,
-multistart_info,"{'start_parameters': [{'CRRA': 3.125, 'WealthShare': 0.6562500000000001}, {'CRRA': 3.318019484660536, 'WealthShare': 0.559897228331805}], 'local_optima': [Minimize with 2 free parameters terminated.
-
-The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance.
-
-Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
-
-                           one_step  five_steps
-relative_criterion_change  0.7922      2.464   
-relative_params_change     0.1306     0.3969   
-absolute_criterion_change  0.3685      1.146   
-absolute_params_change      0.261     0.7936   
-
-(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 2 free parameters terminated.
-
-The tranquilo_ls algorithm reported: Relative criterion change smaller than tolerance.], 'exploration_sample': [{'CRRA': 3.125, 'WealthShare': 0.65625}, {'CRRA': 6.5, 'WealthShare': 0.5249999999999999}, {'CRRA': 14.375, 'WealthShare': 0.56875}, {'CRRA': 8.1875, 'WealthShare': 0.5031249999999999}, {'CRRA': 12.6875, 'WealthShare': 0.678125}, {'CRRA': 6.083993580373348, 'WealthShare': 0.5}, {'CRRA': 17.75, 'WealthShare': 0.6124999999999999}, {'CRRA': 16.625, 'WealthShare': 0.48124999999999996}, {'CRRA': 4.25, 'WealthShare': 0.4375}, {'CRRA': 9.875, 'WealthShare': 0.39375}, {'CRRA': 11.0, 'WealthShare': 0.35}, {'CRRA': 12.125, 'WealthShare': 0.30624999999999997}, {'CRRA': 3.6875, 'WealthShare': 0.328125}, {'CRRA': 8.75, 'WealthShare': 0.26249999999999996}], 'exploration_results': array([2.64855073e+00, 4.67136561e+00, 5.68514018e+00, 5.96336919e+00,
-       6.05930440e+00, 6.06924607e+00, 6.07847870e+00, 8.18119868e+00,
-       1.50677123e+02, 2.82339863e+02, 3.48255341e+03, 1.20657550e+04,
-       1.23229562e+04, 1.23231198e+04])}"
-algorithm_output,"{'states': [State(trustregion=Region(center=array([3.125  , 0.65625]), radius=0.3125, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=[0], model=ScalarModel(intercept=2.6485507328955578, linear_terms=array([0., 0.]), square_terms=array([[0., 0.],
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-       [-8.60578656e+03,  2.94123501e-03]]), scale=array([0.00057432, 0.00114864]), shift=array([2.00057432, 0.5742701 ])), vector_model=VectorModel(intercepts=array([ 0.12729494,  0.23595012,  0.24664438,  0.25049476,  0.22614447,
-        0.18584467,  0.14241263,  0.02560346, -0.04955012,  0.05513681,
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-       [[0., 0.],
-        [0., 0.]]]), scale=0.33180194846605365, shift=array([3.31801948, 0.55989723])), candidate_index=53, candidate_x=array([2.       , 0.5742701]), index=53, x=array([2.       , 0.5742701]), fval=0.4458136747042406, rho=-inf, accepted=True, new_indices=array([52]), old_indices_used=array([40, 41, 43, 46, 48, 49, 50, 51]), old_indices_discarded=array([24, 33, 34, 35, 37, 38, 44, 45, 47]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Relative criterion change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 54 entries., 'history': {'params': [{'CRRA': 3.318019484660536, 'WealthShare': 0.559897228331805}, {'CRRA': 3.070601740404982, 'WealthShare': 0.26584540768349985}, {'CRRA': 3.6120713053088416, 'WealthShare': 0.52614430948447}, {'CRRA': 3.023967664012231, 'WealthShare': 0.5802299902475706}, {'CRRA': 3.6000647938215327, 'WealthShare': 0.7}, {'CRRA': 3.6120713053088416, 'WealthShare': 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{'CRRA': 2.0, 'WealthShare': 0.5754411067089666}, {'CRRA': 2.0, 'WealthShare': 0.5746990416181145}, {'CRRA': 2.004594559697431, 'WealthShare': 0.5792936013157443}, {'CRRA': 2.0, 'WealthShare': 0.5746017692839706}, {'CRRA': 2.0045945596976296, 'WealthShare': 0.5746017692839706}, {'CRRA': 2.002297279848815, 'WealthShare': 0.5768990491327854}, {'CRRA': 2.0, 'WealthShare': 0.5746086694249553}, {'CRRA': 2.0011486399244074, 'WealthShare': 0.575750409208378}, {'CRRA': 2.0, 'WealthShare': 0.5745402580265249}, {'CRRA': 2.0, 'WealthShare': 0.5744167609160861}, {'CRRA': 2.0000000000016387, 'WealthShare': 0.5732681209916787}, {'CRRA': 2.0, 'WealthShare': 0.5742701013447485}, {'CRRA': 2.0, 'WealthShare': 0.5731214614203411}, {'CRRA': 2.0, 'WealthShare': 0.5742701013447485}], 'criterion': [2.2736235577210238, nan, 3.5816375224760213, 1.9269659248194926, 3.387494774598995, 11342.485984510382, 12357.52944941114, 2.9200589510290174, 2.9795071508758486, 3.3467302682773794, 2.804308424408307, 12375.334816783046, 3.1527153481156147, 2.7746629835595535, 2.6521399491388546, 2.713821869542156, 2.213013238949726, 2.119689100959275, 1.9432980044603692, 2.571544833606474, 1.792505688838332, 2.234746903691395, 1.5585590160974347, 1.135885175647875, 0.47491729045941633, 1.3122995942587277, 24.080497413525357, 1.6071034038603749, 0.6152296723274457, 2.007175336487229, 0.6581376036926437, 0.6197022205481675, 0.5196254247599591, 0.46417880977413056, 0.46001284732756403, 0.47974543788251006, 0.4722199479717838, 0.4549529054231862, 0.4485271390252752, 0.4796738641593418, 0.4473225460891819, 0.44599171917691777, 0.47178431077713845, 0.44595174869423626, 0.4520010856387635, 0.45533068925184916, 0.44596277338780516, 0.4498133941676171, 0.4458606157661878, 0.4458360990775131, 0.447078925727915, 0.4458136747042406, 0.4474127317129297, 0.4458136747042406], 'runtime': [0.0, 0.9899638220085762, 1.0301116320188157, 1.0701232440187596, 1.1119882260099985, 1.1533186590240803, 1.1978919840184972, 1.2399261480022687, 1.2822486990189645, 1.326103516999865, 1.3777528160135262, 1.586090366996359, 1.6433335890178569, 2.7274333420209587, 3.63364489399828, 4.529870641010348, 5.431443823006703, 6.337987622013316, 7.259594168019248, 8.191466067015426, 9.080919975007419, 9.978456167009426, 10.869256434001727, 11.76199958100915, 12.644227265001973, 13.537333761021728, 14.424947499996051, 15.31928239800618, 16.204995175008662, 17.071827509003924, 17.951470049010823, 18.822904496017145, 19.72411548101809, 20.64895732101286, 21.57120971000404, 22.50657585801673, 23.402666234003846, 24.305269909003982, 25.195143311022548, 26.092281356017338, 26.99668736802414, 27.89130717201624, 28.802428840019275, 29.672431540006073, 30.560647845995845, 31.449604544002796, 32.34405404701829, 33.21794630799559, 34.09952454699669, 34.974623768008314, 35.86363754799822, 36.78038537999964, 37.703454994014464, 38.62931076801033], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]}}], 'exploration_sample': array([[ 3.125     ,  0.65625   ],
-       [ 6.5       ,  0.525     ],
-       [14.375     ,  0.56875   ],
-       [ 8.1875    ,  0.503125  ],
-       [12.6875    ,  0.678125  ],
-       [ 6.08399358,  0.5       ],
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-       [ 4.25      ,  0.4375    ],
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-       [12.125     ,  0.30625   ],
-       [ 3.6875    ,  0.328125  ],
-       [ 8.75      ,  0.2625    ]]), 'exploration_results': array([2.64855073e+00, 4.67136561e+00, 5.68514018e+00, 5.96336919e+00,
-       6.05930440e+00, 6.06924607e+00, 6.07847870e+00, 8.18119868e+00,
-       1.50677123e+02, 2.82339863e+02, 3.48255341e+03, 1.20657550e+04,
-       1.23229562e+04, 1.23231198e+04])}}"
diff --git a/content/tables/TRP/WealthPortfolioShareOnly_estimate_results.csv b/content/tables/TRP/WealthPortfolioShareOnly_estimate_results.csv
deleted file mode 100644
index 6d722c9..0000000
--- a/content/tables/TRP/WealthPortfolioShareOnly_estimate_results.csv
+++ /dev/null
@@ -1,11632 +0,0 @@
-CRRA,3.4209626121844594
-WealthShare,0.5376972730774127
-time_to_estimate,255.03781032562256
-params,"{'CRRA': 3.4209626121844594, 'WealthShare': 0.5376972730774127}"
-criterion,0.346482551161961
-start_criterion,1.0153159000790366
-start_params,"{'CRRA': 4.062017216065237, 'WealthShare': 0.5}"
-algorithm,multistart_tranquilo_ls
-direction,minimize
-n_free,2
-message,Absolute params change smaller than tolerance.
-success,
-n_criterion_evaluations,
-n_derivative_evaluations,
-n_iterations,
-history,"{'params': [{'CRRA': 3.3386194259076523, 'WealthShare': 0.5695566755752649}, {'CRRA': 3.042741982999753, 'WealthShare': 0.2867780329602785}, {'CRRA': 3.6344968688155515, 'WealthShare': 0.5079487501260587}, {'CRRA': 3.042741982999753, 'WealthShare': 0.5748737851483574}, {'CRRA': 3.6078001651502842, 'WealthShare': 0.7}, {'CRRA': 3.6344968688155515, 'WealthShare': 0.28307853852634224}, {'CRRA': 3.476891161403192, 'WealthShare': 0.2736792326673658}, {'CRRA': 3.0471233520310506, 'WealthShare': 0.7}, {'CRRA': 3.6344968688155515, 'WealthShare': 0.6323882185973801}, {'CRRA': 3.534075308965987, 'WealthShare': 0.7}, {'CRRA': 3.042741982999753, 'WealthShare': 0.6807394495197496}, {'CRRA': 3.26279702987695, 'WealthShare': 0.2736792326673658}, {'CRRA': 3.3211875707940965, 'WealthShare': 0.7}, {'CRRA': 3.3363297427107534, 'WealthShare': 0.6483669135811885}, {'CRRA': 3.4865581473616016, 'WealthShare': 0.6326875615827778}, {'CRRA': 3.422674496381681, 'WealthShare': 0.5396364506704997}, {'CRRA': 3.5706132178356302, 'WealthShare': 0.5521882808032498}, {'CRRA': 3.5061910364965376, 'WealthShare': 0.5407765859604109}, {'CRRA': 3.462120391781281, 'WealthShare': 0.5230884227930105}, {'CRRA': 3.4018378483087495, 'WealthShare': 0.5385230985316353}, {'CRRA': 3.43314375423342, 'WealthShare': 0.5367739074602529}, {'CRRA': 3.4278931033242768, 'WealthShare': 0.5393046263822009}, {'CRRA': 3.422775862296884, 'WealthShare': 0.5370301246740676}, {'CRRA': 3.421510266002226, 'WealthShare': 0.5373450862346448}, {'CRRA': 3.419602567617778, 'WealthShare': 0.5355663515376152}, {'CRRA': 3.4227384692201754, 'WealthShare': 0.5378425010418437}, {'CRRA': 3.42096164565888, 'WealthShare': 0.5376975296476835}, {'CRRA': 3.419906939474906, 'WealthShare': 0.5385171725160157}, {'CRRA': 3.4215808231178073, 'WealthShare': 0.5374897684427973}, {'CRRA': 3.420863117604297, 'WealthShare': 0.5373867364968709}, {'CRRA': 3.420808287490293, 'WealthShare': 0.5376422457245175}, {'CRRA': 3.4210426726149232, 'WealthShare': 0.5376886756992656}, {'CRRA': 3.420956959752845, 'WealthShare': 0.5377380139938767}, {'CRRA': 3.420953441098266, 'WealthShare': 0.5376788770289492}, {'CRRA': 3.4209718251872263, 'WealthShare': 0.5376970984252681}, {'CRRA': 3.420959274301229, 'WealthShare': 0.5377020384003296}, {'CRRA': 3.4209607795016863, 'WealthShare': 0.5376951342732661}, {'CRRA': 3.4209628614265273, 'WealthShare': 0.5376979090165643}, {'CRRA': 3.4209607846532513, 'WealthShare': 0.5376980382431097}, {'CRRA': 3.420961269074393, 'WealthShare': 0.5376984560299611}, {'CRRA': 3.420962202635516, 'WealthShare': 0.5376983601758926}, {'CRRA': 3.4209606596533337, 'WealthShare': 0.5376973629351676}, {'CRRA': 3.4209606434551967, 'WealthShare': 0.5376974637953592}, {'CRRA': 3.420960644793028, 'WealthShare': 0.5376973057400205}, {'CRRA': 3.4209626121844594, 'WealthShare': 0.5376972730774127}], 'criterion': [0.6301090926881846, nan, 0.7774810195136778, 0.8678302920761525, 1.7264341681826587, 287237.03106731933, 152960835.52376047, 2.3162289656176305, 1.2090023761871, 1.75797577023391, 2.131941711346775, 2153.2716064686983, 1.9136658068854424, 1.4776915050219648, 1.246768870798662, 0.35379367668616224, 0.44059795663307527, 0.364420562727979, 0.42079920227924855, 0.3578285762881687, 0.35417006494906106, 0.37128718730340127, 0.35346920478633514, 0.34919592055532217, 0.3587532739397038, 0.36167409517560234, 0.34671046258525756, 0.3625866176887983, 0.3624948824009584, 0.3490703503451608, 0.3538318284444429, 0.35179534944347157, 0.35300438888203234, 0.36066624268870795, 0.35291645540905814, 0.352555805241827, 0.357325714721436, 0.35776910215971736, 0.35685826183947184, 0.3569331419926175, 0.3591611170129715, 0.35440261294876385, 0.3563976215058756, 0.362831121393899, 0.346482551161961], 'runtime': [0.0, 1.0191910249996, 1.0606226570089348, 1.1021161110256799, 1.1485184550110716, 1.1866832980012987, 1.2278694510168862, 1.2720553370018024, 1.3216069210029673, 1.358521448011743, 1.402720191021217, 1.4472510120249353, 1.498645823012339, 2.645608896011254, 3.559202689997619, 4.453557917004218, 5.336892166000325, 6.232588665006915, 7.134310378023656, 8.022814402997028, 8.913387243024772, 9.792689254012657, 10.675620683003217, 11.574354105017846, 12.442508993000956, 13.314171353005804, 14.181816046009772, 15.069552407017909, 15.988469026022358, 16.921192283014534, 17.82632799600833, 18.73840284300968, 19.65942828200059, 20.56417285700445, 21.463274089997867, 22.35210949901375, 23.23928930601687, 24.1411823570088, 25.015946863015415, 25.930222812021384, 26.798822711018147, 27.691662287019426, 28.605715241021244, 29.47253520900267, 30.344772226002533], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]}"
-convergence_report,"{'one_step': {'relative_criterion_change': 0.005765404658376922, 'relative_params_change': 0.007743336544638255, 'absolute_criterion_change': 0.00199761211451549, 'absolute_params_change': 0.0073562267926089615}, 'five_steps': {'relative_criterion_change': 0.005765404658376922, 'relative_params_change': 0.007743336544638255, 'absolute_criterion_change': 0.00199761211451549, 'absolute_params_change': 0.0073562267926089615}}"
-multistart_info,"{'start_parameters': [{'CRRA': 4.062017216065237, 'WealthShare': 0.5}, {'CRRA': 3.3386194259076523, 'WealthShare': 0.5695566755752649}], 'local_optima': [Minimize with 2 free parameters terminated., Minimize with 2 free parameters terminated.
-
-The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance.], 'exploration_sample': [{'CRRA': 4.062017216065237, 'WealthShare': 0.5}, {'CRRA': 3.125, 'WealthShare': 0.65625}, {'CRRA': 6.5, 'WealthShare': 0.5249999999999999}, {'CRRA': 8.1875, 'WealthShare': 0.5031249999999999}, {'CRRA': 14.375, 'WealthShare': 0.56875}, {'CRRA': 12.6875, 'WealthShare': 0.678125}, {'CRRA': 16.625, 'WealthShare': 0.48124999999999996}, {'CRRA': 17.75, 'WealthShare': 0.6124999999999999}, {'CRRA': 4.25, 'WealthShare': 0.4375}, {'CRRA': 9.875, 'WealthShare': 0.39375}, {'CRRA': 11.0, 'WealthShare': 0.35}, {'CRRA': 12.125, 'WealthShare': 0.30624999999999997}, {'CRRA': 3.6875, 'WealthShare': 0.328125}, {'CRRA': 8.75, 'WealthShare': 0.26249999999999996}], 'exploration_results': array([1.09651816e+00, 1.78877610e+00, 1.91826957e+00, 2.78701709e+00,
-       4.47833564e+00, 4.73260048e+00, 4.94712751e+00, 5.16688748e+00,
-       3.22686930e+01, 6.03634308e+01, 5.97496752e+02, 2.06329016e+03,
-       2.18671524e+03, 2.20657404e+03])}"
-algorithm_output,"{'states': [State(trustregion=Region(center=array([3.33861943, 0.56955668]), radius=0.33386194259076524, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=[0], model=ScalarModel(intercept=0.6301090926881846, linear_terms=array([0., 0.]), square_terms=array([[0., 0.],
-       [0., 0.]]), scale=0.33386194259076524, shift=array([3.33861943, 0.56955668])), vector_model=VectorModel(intercepts=array([ 0.11573249,  0.20332604,  0.18891861,  0.1704584 ,  0.12476306,
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-        [0., 0.]]]), scale=0.33386194259076524, shift=array([3.33861943, 0.56955668])), candidate_index=44, candidate_x=array([3.42096261, 0.53769727]), index=44, x=array([3.42096261, 0.53769727]), fval=0.346482551161961, rho=0.42191522352390554, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([26, 36, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([], dtype=int64), step_length=9.999999999134728e-07, relative_step_length=0.9999999999134729, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 45 entries., 'history': {'params': [{'CRRA': 3.3386194259076523, 'WealthShare': 0.5695566755752649}, {'CRRA': 3.042741982999753, 'WealthShare': 0.2867780329602785}, {'CRRA': 3.6344968688155515, 'WealthShare': 0.5079487501260587}, {'CRRA': 3.042741982999753, 'WealthShare': 0.5748737851483574}, {'CRRA': 3.6078001651502842, 'WealthShare': 0.7}, {'CRRA': 3.6344968688155515, 'WealthShare': 0.28307853852634224}, {'CRRA': 3.476891161403192, 'WealthShare': 0.2736792326673658}, {'CRRA': 3.0471233520310506, 'WealthShare': 0.7}, {'CRRA': 3.6344968688155515, 'WealthShare': 0.6323882185973801}, {'CRRA': 3.534075308965987, 'WealthShare': 0.7}, {'CRRA': 3.042741982999753, 'WealthShare': 0.6807394495197496}, {'CRRA': 3.26279702987695, 'WealthShare': 0.2736792326673658}, {'CRRA': 3.3211875707940965, 'WealthShare': 0.7}, {'CRRA': 3.3363297427107534, 'WealthShare': 0.6483669135811885}, {'CRRA': 3.4865581473616016, 'WealthShare': 0.6326875615827778}, {'CRRA': 3.422674496381681, 'WealthShare': 0.5396364506704997}, {'CRRA': 3.5706132178356302, 'WealthShare': 0.5521882808032498}, {'CRRA': 3.5061910364965376, 'WealthShare': 0.5407765859604109}, {'CRRA': 3.462120391781281, 'WealthShare': 0.5230884227930105}, {'CRRA': 3.4018378483087495, 'WealthShare': 0.5385230985316353}, {'CRRA': 3.43314375423342, 'WealthShare': 0.5367739074602529}, {'CRRA': 3.4278931033242768, 'WealthShare': 0.5393046263822009}, {'CRRA': 3.422775862296884, 'WealthShare': 0.5370301246740676}, {'CRRA': 3.421510266002226, 'WealthShare': 0.5373450862346448}, {'CRRA': 3.419602567617778, 'WealthShare': 0.5355663515376152}, {'CRRA': 3.4227384692201754, 'WealthShare': 0.5378425010418437}, {'CRRA': 3.42096164565888, 'WealthShare': 0.5376975296476835}, {'CRRA': 3.419906939474906, 'WealthShare': 0.5385171725160157}, {'CRRA': 3.4215808231178073, 'WealthShare': 0.5374897684427973}, {'CRRA': 3.420863117604297, 'WealthShare': 0.5373867364968709}, {'CRRA': 3.420808287490293, 'WealthShare': 0.5376422457245175}, {'CRRA': 3.4210426726149232, 'WealthShare': 0.5376886756992656}, {'CRRA': 3.420956959752845, 'WealthShare': 0.5377380139938767}, {'CRRA': 3.420953441098266, 'WealthShare': 0.5376788770289492}, {'CRRA': 3.4209718251872263, 'WealthShare': 0.5376970984252681}, {'CRRA': 3.420959274301229, 'WealthShare': 0.5377020384003296}, {'CRRA': 3.4209607795016863, 'WealthShare': 0.5376951342732661}, {'CRRA': 3.4209628614265273, 'WealthShare': 0.5376979090165643}, {'CRRA': 3.4209607846532513, 'WealthShare': 0.5376980382431097}, {'CRRA': 3.420961269074393, 'WealthShare': 0.5376984560299611}, {'CRRA': 3.420962202635516, 'WealthShare': 0.5376983601758926}, {'CRRA': 3.4209606596533337, 'WealthShare': 0.5376973629351676}, {'CRRA': 3.4209606434551967, 'WealthShare': 0.5376974637953592}, {'CRRA': 3.420960644793028, 'WealthShare': 0.5376973057400205}, {'CRRA': 3.4209626121844594, 'WealthShare': 0.5376972730774127}], 'criterion': [0.6301090926881846, nan, 0.7774810195136778, 0.8678302920761525, 1.7264341681826587, 287237.03106731933, 152960835.52376047, 2.3162289656176305, 1.2090023761871, 1.75797577023391, 2.131941711346775, 2153.2716064686983, 1.9136658068854424, 1.4776915050219648, 1.246768870798662, 0.35379367668616224, 0.44059795663307527, 0.364420562727979, 0.42079920227924855, 0.3578285762881687, 0.35417006494906106, 0.37128718730340127, 0.35346920478633514, 0.34919592055532217, 0.3587532739397038, 0.36167409517560234, 0.34671046258525756, 0.3625866176887983, 0.3624948824009584, 0.3490703503451608, 0.3538318284444429, 0.35179534944347157, 0.35300438888203234, 0.36066624268870795, 0.35291645540905814, 0.352555805241827, 0.357325714721436, 0.35776910215971736, 0.35685826183947184, 0.3569331419926175, 0.3591611170129715, 0.35440261294876385, 0.3563976215058756, 0.362831121393899, 0.346482551161961], 'runtime': [0.0, 1.0191910249996, 1.0606226570089348, 1.1021161110256799, 1.1485184550110716, 1.1866832980012987, 1.2278694510168862, 1.2720553370018024, 1.3216069210029673, 1.358521448011743, 1.402720191021217, 1.4472510120249353, 1.498645823012339, 2.645608896011254, 3.559202689997619, 4.453557917004218, 5.336892166000325, 6.232588665006915, 7.134310378023656, 8.022814402997028, 8.913387243024772, 9.792689254012657, 10.675620683003217, 11.574354105017846, 12.442508993000956, 13.314171353005804, 14.181816046009772, 15.069552407017909, 15.988469026022358, 16.921192283014534, 17.82632799600833, 18.73840284300968, 19.65942828200059, 20.56417285700445, 21.463274089997867, 22.35210949901375, 23.23928930601687, 24.1411823570088, 25.015946863015415, 25.930222812021384, 26.798822711018147, 27.691662287019426, 28.605715241021244, 29.47253520900267, 30.344772226002533], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 4.06201722,  0.5       ],
-       [ 3.125     ,  0.65625   ],
-       [ 6.5       ,  0.525     ],
-       [ 8.1875    ,  0.503125  ],
-       [14.375     ,  0.56875   ],
-       [12.6875    ,  0.678125  ],
-       [16.625     ,  0.48125   ],
-       [17.75      ,  0.6125    ],
-       [ 4.25      ,  0.4375    ],
-       [ 9.875     ,  0.39375   ],
-       [11.        ,  0.35      ],
-       [12.125     ,  0.30625   ],
-       [ 3.6875    ,  0.328125  ],
-       [ 8.75      ,  0.2625    ]]), 'exploration_results': array([1.09651816e+00, 1.78877610e+00, 1.91826957e+00, 2.78701709e+00,
-       4.47833564e+00, 4.73260048e+00, 4.94712751e+00, 5.16688748e+00,
-       3.22686930e+01, 6.03634308e+01, 5.97496752e+02, 2.06329016e+03,
-       2.18671524e+03, 2.20657404e+03])}}"
diff --git a/content/tables/TRP/WealthPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/TRP/WealthPortfolioSub(Stock)Market_estimate_results.csv
deleted file mode 100644
index 44cf259..0000000
--- a/content/tables/TRP/WealthPortfolioSub(Stock)Market_estimate_results.csv
+++ /dev/null
@@ -1,30714 +0,0 @@
-CRRA,6.847819814398697
-WealthShare,0.3887893874243111
-WealthShift,41.34527824301813
-time_to_estimate,294.568989276886
-params,"{'CRRA': 6.847819814398697, 'WealthShare': 0.3887893874243111, 'WealthShift': 41.34527824301813}"
-criterion,0.15388219450569224
-start_criterion,0.8084686841989532
-start_params,"{'CRRA': 6.847819814398697, 'WealthShare': 0.3887893874243111, 'WealthShift': 41.34527824301813}"
-algorithm,multistart_tranquilo_ls
-direction,minimize
-n_free,3
-message,
-success,
-n_criterion_evaluations,
-n_derivative_evaluations,
-n_iterations,
-history,"{'params': [{'CRRA': 6.847819814398697, 'WealthShare': 0.3887893874243111, 'WealthShift': 41.34527824301813}, {'CRRA': 3.5461380077742324, 'WealthShare': 0.0, 'WealthShift': 43.203584507974064}, {'CRRA': 8.766080519247641, 'WealthShare': 0.0, 'WealthShift': 43.469705404666556}, {'CRRA': 10.149614998191591, 'WealthShare': 0.427597498009201, 'WealthShift': 44.677691036234}, {'CRRA': 5.5406886498589625, 'WealthShare': 0.43639785887820415, 'WealthShift': 38.012865449802256}, {'CRRA': 5.673183157159503, 'WealthShare': 0.5, 'WealthShift': 44.674555400839964}, {'CRRA': 9.89103343193754, 'WealthShare': 0.5, 'WealthShift': 44.62651578682769}, {'CRRA': 3.6297226762856667, 'WealthShare': 0.5, 'WealthShift': 42.1402680312433}, {'CRRA': 3.549332474912331, 'WealthShare': 0.21269781172620106, 'WealthShift': 44.677691036234}, {'CRRA': 10.180232607614567, 'WealthShare': 0.27724319141201087, 'WealthShift': 38.10649301147815}, {'CRRA': 6.721885008279584, 'WealthShare': 0.0, 'WealthShift': 38.107895118671756}, {'CRRA': 3.6356172566516163, 'WealthShare': 0.5, 'WealthShift': 39.42387555793129}, {'CRRA': 9.870698799705265, 'WealthShare': 0.0, 'WealthShift': 44.4800222651468}, {'CRRA': 3.5154070211828268, 'WealthShare': 0.5, 'WealthShift': 44.677691036234}, {'CRRA': 5.181613417790762, 'WealthShare': 0.30456683823269565, 'WealthShift': 41.62110585153618}, {'CRRA': 6.014716616094729, 'WealthShare': 0.5, 'WealthShift': 42.178381441322095}, {'CRRA': 7.2319642025010005, 'WealthShare': 0.019336363830240094, 'WealthShift': 40.92872664386614}, {'CRRA': 6.464262963755857, 'WealthShare': 0.49484802051102866, 'WealthShift': 40.92872664386614}, {'CRRA': 6.889369515573619, 'WealthShare': 0.48647847562853674, 'WealthShift': 41.761829842170116}, {'CRRA': 7.1775087640203035, 'WealthShare': 0.0664752477995276, 'WealthShift': 41.761829842170116}, {'CRRA': 6.431268215246713, 'WealthShare': 0.1440114695379734, 'WealthShift': 41.006614782852886}, {'CRRA': 7.245988354510613, 'WealthShare': 0.0, 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97.39746205700794, 98.29795667500002, 99.20367347600404, 100.13006205001147, 101.05771576199913, 101.98077489400748, 102.9100705460005, 103.82748462699237, 104.73702308000065, 105.65016225501313], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 15, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 19, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 23, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 27, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108]}"
-convergence_report,
-multistart_info,"{'start_parameters': [{'CRRA': 6.847819814398697, 'WealthShare': 0.3887893874243111, 'WealthShift': 41.34527824301813}, {'CRRA': 5.274371021987561, 'WealthShare': 0.42258471880485865, 'WealthShift': 47.644754881410044}, {'CRRA': 8.848407153798535, 'WealthShare': 0.3977279513770549, 'WealthShift': 36.05207106133323}], 'local_optima': [Minimize with 3 free parameters terminated., Minimize with 3 free parameters terminated.
-
-The tranquilo_ls algorithm reported: Absolute criterion change smaller than tolerance.
-
-Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
-
-                           one_step  five_steps
-relative_criterion_change    1.29       1.29   
-relative_params_change     0.4415     0.4415   
-absolute_criterion_change   2.115      2.115   
-absolute_params_change      4.714      4.714   
-
-(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.), Minimize with 3 free parameters terminated.
-
-The tranquilo_ls algorithm reported: Maximum number of criterion evaluations reached.
-
-Independent of the convergence criteria used by tranquilo_ls, the strength of convergence can be assessed by the following criteria:
-
-                            one_step   five_steps
-relative_criterion_change  0.04828     0.04828   
-relative_params_change      0.2086      0.2086   
-absolute_criterion_change    0.292       0.292   
-absolute_params_change       3.151       3.151   
-
-(***: change <= 1e-10, **: change <= 1e-8, *: change <= 1e-5. Change refers to a change between accepted steps. The first column only considers the last step. The second column considers the last five steps.)], 'exploration_sample': [{'CRRA': 6.847819814398697, 'WealthShare': 0.3887893874243111, 'WealthShift': 41.34527824301813}, {'CRRA': 3.125, 'WealthShare': 0.46875, 'WealthShift': 56.25}, {'CRRA': 17.75, 'WealthShare': 0.4375, 'WealthShift': 12.5}, {'CRRA': 9.3125, 'WealthShare': 0.453125, 'WealthShift': 28.125}, {'CRRA': 14.375, 'WealthShare': 0.40625, 'WealthShift': 43.75}, {'CRRA': 16.0625, 'WealthShare': 0.390625, 'WealthShift': 65.625}, {'CRRA': 12.6875, 'WealthShare': 0.484375, 'WealthShift': 96.875}, {'CRRA': 6.5, 'WealthShare': 0.375, 'WealthShift': 75.0}, {'CRRA': 5.9375, 'WealthShare': 0.421875, 'WealthShift': 9.375}, {'CRRA': 8.1875, 'WealthShare': 0.359375, 'WealthShift': 71.875}, {'CRRA': 16.625, 'WealthShare': 0.34375, 'WealthShift': 81.25}, {'CRRA': 13.8125, 'WealthShare': 0.328125, 'WealthShift': 3.125}, {'CRRA': 19.4375, 'WealthShare': 0.296875, 'WealthShift': 34.375}, {'CRRA': 11.0, 'WealthShare': 0.25, 'WealthShift': 50.0}, {'CRRA': 9.875, 'WealthShare': 0.28125, 'WealthShift': 18.75}, {'CRRA': 4.25, 'WealthShare': 0.3125, 'WealthShift': 37.5}], 'exploration_results': array([1.53882195e-01, 4.20641104e+00, 6.93681122e+00, 7.03901654e+00,
-       8.35364220e+00, 8.48589683e+00, 8.93277872e+00, 1.71200695e+01,
-       2.34262778e+01, 4.90622456e+02, 5.67558263e+02, 7.68837064e+03,
-       1.20956150e+04, 1.23214597e+04, 1.23220921e+04, 1.23221745e+04])}"
-algorithm_output,"{'states': [State(trustregion=Region(center=array([ 6.84781981,  0.38878939, 41.34527824]), radius=4.134527824301813, bounds=Bounds(lower=array([2., 0., 0.]), upper=array([ 20. ,   0.5, 100. ]))), model_indices=[0], model=ScalarModel(intercept=0.15388219450569224, linear_terms=array([0., 0., 0.]), square_terms=array([[0., 0., 0.],
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-       [16.0625    ,  0.390625  , 65.625     ],
-       [12.6875    ,  0.484375  , 96.875     ],
-       [ 6.5       ,  0.375     , 75.        ],
-       [ 5.9375    ,  0.421875  ,  9.375     ],
-       [ 8.1875    ,  0.359375  , 71.875     ],
-       [16.625     ,  0.34375   , 81.25      ],
-       [13.8125    ,  0.328125  ,  3.125     ],
-       [19.4375    ,  0.296875  , 34.375     ],
-       [11.        ,  0.25      , 50.        ],
-       [ 9.875     ,  0.28125   , 18.75      ],
-       [ 4.25      ,  0.3125    , 37.5       ]]), 'exploration_results': array([1.53882195e-01, 4.20641104e+00, 6.93681122e+00, 7.03901654e+00,
-       8.35364220e+00, 8.48589683e+00, 8.93277872e+00, 1.71200695e+01,
-       2.34262778e+01, 4.90622456e+02, 5.67558263e+02, 7.68837064e+03,
-       1.20956150e+04, 1.23214597e+04, 1.23220921e+04, 1.23221745e+04])}}"
diff --git a/content/tables/TRP/WealthPortfolio_estimate_results.csv b/content/tables/TRP/WealthPortfolio_estimate_results.csv
index b98214a..3ff384e 100644
--- a/content/tables/TRP/WealthPortfolio_estimate_results.csv
+++ b/content/tables/TRP/WealthPortfolio_estimate_results.csv
@@ -1,26887 +1,11700 @@
-CRRA,5.927650549987919
-WealthShare,0.4337387151345948
-WealthShift,9.395681818099618
-time_to_estimate,234.48415184020996
-params,"{'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}"
-criterion,0.05795576874851694
-start_criterion,0.1416056173308317
-start_params,"{'CRRA': 5.927289381287416, 'WealthShare': 0.433617473501538, 'WealthShift': 9.395755629247377}"
+CRRA,3.394330573661952
+WealthShare,0.537543812530427
+time_to_estimate,146.67856311798096
+params,"{'CRRA': 3.394330573661952, 'WealthShare': 0.537543812530427}"
+criterion,0.3476591866783385
+start_criterion,0.4310570192429367
+start_params,"{'CRRA': 3.385996288692849, 'WealthShare': 0.5363280846095942}"
 algorithm,multistart_tranquilo_ls
 direction,minimize
-n_free,3
-message,
+n_free,2
+message,Absolute params change smaller than tolerance.
 success,
 n_criterion_evaluations,
 n_derivative_evaluations,
 n_iterations,
-history,"{'params': [{'CRRA': 5.927289381287417, 'WealthShare': 0.433617473501538, 'WealthShift': 9.395755629247377}, {'CRRA': 5.185052531150577, 'WealthShare': 0.0, 'WealthShift': 9.793334799820117}, {'CRRA': 6.357436849405872, 'WealthShare': 0.0, 'WealthShift': 9.902651425036044}, {'CRRA': 6.684583505100003, 'WealthShare': 0.3932159958615903, 'WealthShift': 9.990381739023128}, {'CRRA': 5.585985694329924, 'WealthShare': 0.34544379980904444, 'WealthShift': 8.63846150543479}, {'CRRA': 5.568675067542343, 'WealthShare': 0.4805669268604726, 'WealthShift': 10.153049753059964}, {'CRRA': 6.6661171572979105, 'WealthShare': 0.4853859541743232, 'WealthShift': 10.153049753059964}, {'CRRA': 5.169995257474831, 'WealthShare': 0.467883883510143, 'WealthShift': 9.563533915412682}, {'CRRA': 5.169995257474831, 'WealthShare': 0.14065451845743066, 'WealthShift': 10.099856752617312}, {'CRRA': 6.684583505100003, 'WealthShare': 0.26501606105266895, 'WealthShift': 8.764892873616436}, {'CRRA': 5.844261046533687, 'WealthShare': 0.0, 'WealthShift': 8.666091359470377}, {'CRRA': 5.2341239960568675, 'WealthShare': 0.4847228136732892, 'WealthShift': 8.63846150543479}, {'CRRA': 6.684583505100003, 'WealthShare': 0.011195616275918502, 'WealthShift': 10.120465973357607}, {'CRRA': 5.169995257474831, 'WealthShare': 0.5, 'WealthShift': 9.470943655456052}, {'CRRA': 5.84268329779124, 'WealthShare': 0.054970411595244906, 'WealthShift': 9.77440269115367}, {'CRRA': 5.7379658503342705, 'WealthShare': 0.5, 'WealthShift': 9.585079160200523}, {'CRRA': 6.021655219545377, 'WealthShare': 0.34619088120627634, 'WealthShift': 9.301093863770804}, {'CRRA': 5.8713249545346216, 'WealthShare': 0.4955936832190454, 'WealthShift': 9.301093863770804}, {'CRRA': 5.940263516854802, 'WealthShare': 0.5, 'WealthShift': 9.486132493413468}, {'CRRA': 5.998726648544123, 'WealthShare': 0.3659866942655981, 'WealthShift': 9.49041739472395}, {'CRRA': 5.832627615810844, 'WealthShare': 0.3794930757265516, 'WealthShift': 9.320587953973034}, {'CRRA': 6.021441421947278, 'WealthShare': 0.3389557080249647, 'WealthShift': 9.415178723938707}, {'CRRA': 6.02195114676399, 'WealthShare': 0.49648282623830364, 'WealthShift': 9.309776183646196}, {'CRRA': 5.834013497478194, 'WealthShare': 0.5, 'WealthShift': 9.466134434016604}, {'CRRA': 5.832627615810844, 'WealthShare': 0.3983657744901068, 'WealthShift': 9.486204414038081}, {'CRRA': 6.02195114676399, 'WealthShare': 0.49882261278046336, 'WealthShift': 9.489514798732149}, {'CRRA': 5.894830691132047, 'WealthShare': 0.3389557080249647, 'WealthShift': 9.301093863770804}, {'CRRA': 5.837646115248923, 'WealthShare': 0.3389557080249647, 'WealthShift': 9.472087261144681}, {'CRRA': 5.832627615810844, 'WealthShare': 0.4836895674295131, 'WealthShift': 9.461661656721093}, {'CRRA': 5.892934175716558, 'WealthShare': 0.4746037158260715, 'WealthShift': 9.42779679112728}, {'CRRA': 5.932404752119262, 'WealthShare': 0.4617796261879173, 'WealthShift': 9.389210577811099}, {'CRRA': 5.919907102024529, 'WealthShare': 0.4228247748737032, 'WealthShift': 9.389081237929894}, {'CRRA': 5.917121097859418, 'WealthShare': 0.43654784676940245, 'WealthShift': 9.385579833706766}, {'CRRA': 5.9372667181758665, 'WealthShare': 0.4339080587439327, 'WealthShift': 9.406521138946259}, {'CRRA': 5.926773207086299, 'WealthShare': 0.4464580473156103, 'WealthShift': 9.388657663442663}, {'CRRA': 5.919758814114408, 'WealthShare': 0.421023888276584, 'WealthShift': 9.395286524269718}, {'CRRA': 5.913889173638341, 'WealthShare': 0.4280368054670466, 'WealthShift': 9.397950730496955}, {'CRRA': 5.919983053120503, 'WealthShare': 0.4424858238704275, 'WealthShift': 9.386617915053039}, {'CRRA': 5.93460550498648, 'WealthShare': 0.42196383892490175, 'WealthShift': 9.400873736848794}, {'CRRA': 5.933430848349458, 'WealthShare': 0.4210489122800005, 'WealthShift': 9.391301244734139}, {'CRRA': 5.931292177100905, 'WealthShare': 0.4425427106926806, 'WealthShift': 9.384808225125722}, {'CRRA': 5.924691351864214, 'WealthShare': 0.42461142350930114, 'WealthShift': 9.407054708718686}, {'CRRA': 5.921756597817574, 'WealthShare': 0.42888318167350464, 'WealthShift': 9.408503283078899}, {'CRRA': 5.9168053090526875, 'WealthShare': 0.43326101806931255, 'WealthShift': 9.385476631883977}, {'CRRA': 5.923513082439774, 'WealthShare': 0.4338128213362978, 'WealthShift': 9.402058394199619}, {'CRRA': 5.926184196036881, 'WealthShare': 0.43387302411228973, 'WealthShift': 9.399258163249826}, {'CRRA': 5.929057550291667, 'WealthShare': 0.434106374237333, 'WealthShift': 9.395802295381072}, {'CRRA': 5.926494649466185, 'WealthShare': 0.43527111894802584, 'WealthShift': 9.395717140876194}, {'CRRA': 5.928359344444315, 'WealthShare': 0.433491432140597, 'WealthShift': 9.397241198398678}, {'CRRA': 5.925547062588301, 'WealthShare': 0.4340724784526388, 'WealthShift': 9.39610906853533}, {'CRRA': 5.926319507058149, 'WealthShare': 0.43395535773267413, 'WealthShift': 9.394234839481882}, {'CRRA': 5.927530713172369, 'WealthShare': 0.43494772232762613, 'WealthShift': 9.396996524193407}, {'CRRA': 5.925839116528331, 'WealthShare': 0.4325657016347811, 'WealthShift': 9.396153286579683}, {'CRRA': 5.927093986867131, 'WealthShare': 0.43229786847739204, 'WealthShift': 9.397015822441238}, {'CRRA': 5.926772491997869, 'WealthShare': 0.4321949613093904, 'WealthShift': 9.394717887559564}, {'CRRA': 5.92819249324905, 'WealthShare': 0.43329383226798696, 'WealthShift': 9.39419125447713}, {'CRRA': 5.928419936896417, 'WealthShare': 0.43217401660841787, 'WealthShift': 9.395678821059354}, {'CRRA': 5.9279936592739135, 'WealthShare': 0.4350127815846545, 'WealthShift': 9.394793999819784}, {'CRRA': 5.9290977785919114, 'WealthShare': 0.4337405836095407, 'WealthShift': 9.39546727020135}, {'CRRA': 5.928058495579955, 'WealthShare': 0.4337346748382437, 'WealthShift': 9.395255225095774}, {'CRRA': 5.92771207338598, 'WealthShare': 0.43368936419869186, 'WealthShift': 9.395572089279295}, {'CRRA': 5.9275456649371545, 'WealthShare': 0.43373512502522144, 'WealthShift': 9.3964736448347}, {'CRRA': 5.927266356141892, 'WealthShare': 0.43371142851188993, 'WealthShift': 9.395680582985545}, {'CRRA': 5.927487429540767, 'WealthShare': 0.4336673839044781, 'WealthShift': 9.395505937008066}, {'CRRA': 5.927673099652866, 'WealthShare': 0.4337298020475924, 'WealthShift': 9.395682374455484}, {'CRRA': 5.927449171541386, 'WealthShare': 0.43371728175011565, 'WealthShift': 9.395634166136793}, {'CRRA': 5.927764050360411, 'WealthShare': 0.4338009069782971, 'WealthShift': 9.395690590946838}, {'CRRA': 5.927705348780054, 'WealthShare': 0.43369648769144115, 'WealthShift': 9.395645686830811}, {'CRRA': 5.927655415308468, 'WealthShare': 0.43370638236058445, 'WealthShift': 9.39567970321352}, {'CRRA': 5.927665120042916, 'WealthShare': 0.43365201571459716, 'WealthShift': 9.39569515573913}, {'CRRA': 5.927650333840555, 'WealthShare': 0.4337345967942775, 'WealthShift': 9.395680881153949}, {'CRRA': 5.927663139867967, 'WealthShare': 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9.39568093253225}, {'CRRA': 5.9276506605172345, 'WealthShare': 0.43373716305602067, 'WealthShift': 9.395681000078362}, {'CRRA': 5.927650552065239, 'WealthShare': 0.43373717882353047, 'WealthShift': 9.395681065215475}, {'CRRA': 5.927650586593226, 'WealthShare': 0.4337371681997891, 'WealthShift': 9.395680976183518}, {'CRRA': 5.927650274877122, 'WealthShare': 0.4337374326448868, 'WealthShift': 9.395680420521492}, {'CRRA': 5.927651045140691, 'WealthShare': 0.43373761913352415, 'WealthShift': 9.39568018706593}, {'CRRA': 5.927651592645607, 'WealthShare': 0.4337377040131243, 'WealthShift': 9.395680979237271}, {'CRRA': 5.927651656384041, 'WealthShare': 0.43373790653277816, 'WealthShift': 9.395680789628603}, {'CRRA': 5.927651583690701, 'WealthShare': 0.43373799817209246, 'WealthShift': 9.395681462884273}, {'CRRA': 5.927651088388353, 'WealthShare': 0.43373807855552526, 'WealthShift': 9.395681874790462}, {'CRRA': 5.927651419348874, 'WealthShare': 0.43373851169130295, 'WealthShift': 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{'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}, {'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}, {'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}, {'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}, {'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}, {'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}, {'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}, {'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}, {'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}, {'CRRA': 5.927650549987919, 'WealthShare': 0.4337387151345948, 'WealthShift': 9.395681818099618}, {'CRRA': 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-convergence_report,
-multistart_info,"{'start_parameters': [{'CRRA': 5.927289381287417, 'WealthShare': 0.433617473501538, 'WealthShift': 9.395755629247377}, {'CRRA': 5.931813417384156, 'WealthShare': 0.4287245191265473, 'WealthShift': 9.386940653247144}], 'local_optima': [Minimize with 3 free parameters terminated., Minimize with 3 free parameters terminated.], 'exploration_sample': [{'CRRA': 5.927289381287416, 'WealthShare': 0.433617473501538, 'WealthShift': 9.395755629247377}, {'CRRA': 5.9375, 'WealthShare': 0.421875, 'WealthShift': 9.375}, {'CRRA': 17.75, 'WealthShare': 0.4375, 'WealthShift': 12.5}, {'CRRA': 9.3125, 'WealthShare': 0.453125, 'WealthShift': 28.125}, {'CRRA': 14.375, 'WealthShare': 0.40625, 'WealthShift': 43.75}, {'CRRA': 16.0625, 'WealthShare': 0.390625, 'WealthShift': 65.625}, {'CRRA': 12.6875, 'WealthShare': 0.484375, 'WealthShift': 96.875}, {'CRRA': 3.125, 'WealthShare': 0.46875, 'WealthShift': 56.25}, {'CRRA': 6.5, 'WealthShare': 0.375, 'WealthShift': 75.0}, {'CRRA': 8.1875, 'WealthShare': 0.359375, 'WealthShift': 71.875}, {'CRRA': 16.625, 'WealthShare': 0.34375, 'WealthShift': 81.25}, {'CRRA': 13.8125, 'WealthShare': 0.328125, 'WealthShift': 3.125}, {'CRRA': 11.0, 'WealthShare': 0.25, 'WealthShift': 50.0}, {'CRRA': 9.875, 'WealthShare': 0.28125, 'WealthShift': 18.75}, {'CRRA': 19.4375, 'WealthShare': 0.296875, 'WealthShift': 34.375}, {'CRRA': 4.25, 'WealthShare': 0.3125, 'WealthShift': 37.5}], 'exploration_results': array([6.48950726e-02, 2.60350190e+00, 4.73786169e+00, 6.10567758e+00,
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-       [ 4.25      ,  0.3125    , 37.5       ]]), 'exploration_results': array([6.48950726e-02, 2.60350190e+00, 4.73786169e+00, 6.10567758e+00,
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+history,"{'params': [{'CRRA': 3.3095587758238634, 'WealthShare': 0.571466130146853}, {'CRRA': 3.016256765973506, 'WealthShare': 0.29061885915302493}, {'CRRA': 3.602860785674221, 'WealthShare': 0.5097704838031792}, {'CRRA': 3.016256765973506, 'WealthShare': 0.5760370098839758}, {'CRRA': 3.5760478459618126, 'WealthShare': 0.7}, {'CRRA': 3.602860785674221, 'WealthShare': 0.2878392037263486}, {'CRRA': 3.4478588052515033, 'WealthShare': 0.27816412029649534}, {'CRRA': 3.0207215987193226, 'WealthShare': 0.7}, {'CRRA': 3.602860785674221, 'WealthShare': 0.6327324745145311}, {'CRRA': 3.5030790241126377, 'WealthShare': 0.7}, {'CRRA': 3.016256765973506, 'WealthShare': 0.6807913272054849}, {'CRRA': 3.234215209095579, 'WealthShare': 0.27816412029649534}, {'CRRA': 3.292914640909409, 'WealthShare': 0.7}, {'CRRA': 3.303133696480183, 'WealthShare': 0.6468852446280728}, {'CRRA': 3.456209780749042, 'WealthShare': 0.6330260460920982}, {'CRRA': 3.393118516188777, 'WealthShare': 0.5377100904840499}, {'CRRA': 3.5397695211139557, 'WealthShare': 0.5509522833458697}, {'CRRA': 3.4758674679770443, 'WealthShare': 0.5400001711234805}, {'CRRA': 3.434064260747328, 'WealthShare': 0.5243866818724292}, {'CRRA': 3.4137951701197853, 'WealthShare': 0.5371316890165021}, {'CRRA': 3.3827347013962545, 'WealthShare': 0.5356230431833575}, {'CRRA': 3.3886868172194666, 'WealthShare': 0.5404246379718759}, {'CRRA': 3.3929877161471165, 'WealthShare': 0.5351278082679701}, {'CRRA': 3.394411039178595, 'WealthShare': 0.5376829528332149}, {'CRRA': 3.3950422594077354, 'WealthShare': 0.5375349901544691}, {'CRRA': 3.394403048866773, 'WealthShare': 0.5380060531468045}, {'CRRA': 3.394330571104913, 'WealthShare': 0.5375428125336963}, {'CRRA': 3.3942641227083596, 'WealthShare': 0.5372265179426579}, {'CRRA': 3.394173480699256, 'WealthShare': 0.5375027375064382}, {'CRRA': 3.394410295274285, 'WealthShare': 0.537555952565116}, {'CRRA': 3.394368932528548, 'WealthShare': 0.5375301416190236}, {'CRRA': 3.394326995715963, 'WealthShare': 0.5375626935376785}, {'CRRA': 3.3943246528733915, 'WealthShare': 0.5375346281603528}, {'CRRA': 3.3943356046345925, 'WealthShare': 0.5375424051802747}, {'CRRA': 3.394329304009661, 'WealthShare': 0.5375449965801891}, {'CRRA': 3.3943299520790617, 'WealthShare': 0.5375417122135792}, {'CRRA': 3.3943315684886763, 'WealthShare': 0.5375428848222087}, {'CRRA': 3.3943296151296067, 'WealthShare': 0.5375425190865739}, {'CRRA': 3.394329649205157, 'WealthShare': 0.5375432004343061}, {'CRRA': 3.3943315656125383, 'WealthShare': 0.537542917197843}, {'CRRA': 3.3943295696848708, 'WealthShare': 0.5375427637114514}, {'CRRA': 3.3943305792741665, 'WealthShare': 0.5375438125003273}, {'CRRA': 3.394330680756775, 'WealthShare': 0.5375438065037508}, {'CRRA': 3.394331046758134, 'WealthShare': 0.5375419308777375}, {'CRRA': 3.394330573661952, 'WealthShare': 0.537543812530427}], 'criterion': [0.6607127140516494, nan, 0.7133179969678111, 0.9122250510532794, 1.738814245006418, 50904625.67032898, 116246.71705175268, 2.371483432043779, 1.21707149810191, 1.7742326776217476, 2.1844788091340064, nan, 1.9431589702823127, 1.4900363321437669, 1.2619256393554017, 0.35160518753317954, 0.42838937661127785, 0.36008666150771423, 0.4076867472422282, 0.35498950666354673, 0.3567456338142511, 0.37666426015405113, 0.354837869983808, 0.35106354160290787, 0.36144004176498795, 0.363428759218245, 0.3478920125068537, 0.36129474761431013, 0.36389252870011624, 0.3506283566979166, 0.35511565098469156, 0.35306860499987824, 0.3541434194955959, 0.3617849686943767, 0.3540838274551238, 0.35370468558579427, 0.3585447569605501, 0.35897473002769464, 0.3580444096251574, 0.35816686951261745, 0.3603114273224131, 0.3557617475147986, 0.35761591726779984, 0.3641098629626268, 0.3476591866783385], 'runtime': [0.0, 0.9876696610008366, 1.0269310979929287, 1.0667359170038253, 1.1066995759902056, 1.146514563006349, 1.188373284006957, 1.2297300389909651, 1.2781228669919074, 1.3206576250086073, 1.3693073309841566, 1.420242080988828, 1.4671239820017945, 2.6949373869865667, 3.6092029479914345, 4.507678669004235, 5.437428473989712, 6.387053313985234, 7.320997596980305, 8.266617074987153, 9.191725206008414, 10.118209832988214, 11.042316315986682, 11.9644784439879, 12.8848473179969, 13.782509017997654, 14.689824258995941, 15.593694101000438, 16.504139386990573, 17.403498230007244, 18.31440812998335, 19.222797163994983, 20.122583561984356, 21.038069117988925, 21.969905721984105, 22.90410190098919, 23.847403305990156, 24.791996433981694, 25.731922432983993, 26.64831998400041, 27.567248210980324, 28.47857929300517, 29.400026577990502, 30.30448704698938, 31.211013243999332], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]}"
+convergence_report,"{'one_step': {'relative_criterion_change': 0.0063703811355266165, 'relative_params_change': 0.0033118460013637236, 'absolute_criterion_change': 0.002214721524408214, 'absolute_params_change': 0.008410565685333831}, 'five_steps': {'relative_criterion_change': 0.0063703811355266165, 'relative_params_change': 0.0033118460013637236, 'absolute_criterion_change': 0.002214721524408214, 'absolute_params_change': 0.008410565685333831}}"
+multistart_info,"{'start_parameters': [{'CRRA': 3.385996288692849, 'WealthShare': 0.5363280846095942}, {'CRRA': 3.3095587758238634, 'WealthShare': 0.571466130146853}], 'local_optima': [Minimize with 2 free parameters terminated., Minimize with 2 free parameters terminated.
+
+The tranquilo_ls algorithm reported: Absolute params change smaller than tolerance.], 'exploration_sample': [{'CRRA': 3.385996288692849, 'WealthShare': 0.5363280846095942}, {'CRRA': 3.125, 'WealthShare': 0.65625}, {'CRRA': 6.5, 'WealthShare': 0.5249999999999999}, {'CRRA': 8.1875, 'WealthShare': 0.5031249999999999}, {'CRRA': 14.375, 'WealthShare': 0.56875}, {'CRRA': 12.6875, 'WealthShare': 0.678125}, {'CRRA': 16.625, 'WealthShare': 0.48124999999999996}, {'CRRA': 17.75, 'WealthShare': 0.6124999999999999}, {'CRRA': 4.25, 'WealthShare': 0.4375}, {'CRRA': 9.875, 'WealthShare': 0.39375}, {'CRRA': 11.0, 'WealthShare': 0.35}, {'CRRA': 12.125, 'WealthShare': 0.30624999999999997}, {'CRRA': 3.6875, 'WealthShare': 0.328125}, {'CRRA': 8.75, 'WealthShare': 0.26249999999999996}], 'exploration_results': array([3.64521041e-01, 1.78877610e+00, 1.91826957e+00, 2.78701709e+00,
+       4.47833564e+00, 4.73260048e+00, 4.94712751e+00, 5.16688748e+00,
+       3.22686930e+01, 6.03634308e+01, 5.97496752e+02, 2.06329016e+03,
+       2.18671524e+03, 2.20657404e+03])}"
+algorithm_output,"{'states': [State(trustregion=Region(center=array([3.30955878, 0.57146613]), radius=0.33095587758238637, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=[0], model=ScalarModel(intercept=0.6607127140516494, linear_terms=array([0., 0.]), square_terms=array([[0., 0.],
+       [0., 0.]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
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+       [-1343499.04166053,  1596421.69293623]]), scale=array([0.29330201, 0.21091794]), shift=array([3.30955878, 0.48908206])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
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+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
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+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
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+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
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+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
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+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.]]), square_terms=array([[[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
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+       [[0., 0.],
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+       [[0., 0.],
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+       [[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=43, candidate_x=array([3.39433105, 0.53754193]), index=26, x=array([3.39433057, 0.53754281]), fval=0.3478920125068537, rho=-11.19745237584309, accepted=False, new_indices=array([], dtype=int64), old_indices_used=array([26, 35, 36, 37, 38, 39, 40, 41, 42]), old_indices_discarded=array([34]), step_length=0.0, relative_step_length=0.0, n_evals_per_point=1, n_evals_acceptance=1), State(trustregion=Region(center=array([3.39433057, 0.53754281]), radius=1e-06, bounds=Bounds(lower=array([2., 0.]), upper=array([20. ,  0.7]))), model_indices=array([26, 36, 37, 38, 39, 40, 41, 42, 43]), model=ScalarModel(intercept=0.3580325302964799, linear_terms=array([-4.05258899e-05, -2.76046390e-03]), square_terms=array([[1.11011536e-05, 1.00347631e-05],
+       [1.00347631e-05, 5.07447680e-05]]), scale=1e-06, shift=array([3.39433057, 0.53754281])), vector_model=VectorModel(intercepts=array([ 0.11508426,  0.20157134,  0.18616043,  0.16705331,  0.12075446,
+        0.0633746 ,  0.00131166, -0.20958869, -0.30102071, -0.20053616,
+       -0.43112738, -0.31074888, -0.02453033,  0.09227073,  0.14025736,
+        0.14064248,  0.14112309]), linear_terms=array([[0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
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+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.],
+       [0., 0.]]), square_terms=array([[[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
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+       [[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
+        [0., 0.]],
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+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]],
+
+       [[0., 0.],
+        [0., 0.]]]), scale=0.33095587758238637, shift=array([3.30955878, 0.57146613])), candidate_index=44, candidate_x=array([3.39433057, 0.53754381]), index=44, x=array([3.39433057, 0.53754381]), fval=0.3476591866783385, rho=0.08512327562783835, accepted=True, new_indices=array([], dtype=int64), old_indices_used=array([26, 36, 37, 38, 39, 40, 41, 42, 43]), old_indices_discarded=array([34, 35]), step_length=9.999999999835112e-07, relative_step_length=0.9999999999835112, n_evals_per_point=1, n_evals_acceptance=1)], 'message': 'Absolute params change smaller than tolerance.', 'tranquilo_history': History for least_squares function with 45 entries., 'history': {'params': [{'CRRA': 3.3095587758238634, 'WealthShare': 0.571466130146853}, {'CRRA': 3.016256765973506, 'WealthShare': 0.29061885915302493}, {'CRRA': 3.602860785674221, 'WealthShare': 0.5097704838031792}, {'CRRA': 3.016256765973506, 'WealthShare': 0.5760370098839758}, {'CRRA': 3.5760478459618126, 'WealthShare': 0.7}, {'CRRA': 3.602860785674221, 'WealthShare': 0.2878392037263486}, {'CRRA': 3.4478588052515033, 'WealthShare': 0.27816412029649534}, {'CRRA': 3.0207215987193226, 'WealthShare': 0.7}, {'CRRA': 3.602860785674221, 'WealthShare': 0.6327324745145311}, {'CRRA': 3.5030790241126377, 'WealthShare': 0.7}, {'CRRA': 3.016256765973506, 'WealthShare': 0.6807913272054849}, {'CRRA': 3.234215209095579, 'WealthShare': 0.27816412029649534}, {'CRRA': 3.292914640909409, 'WealthShare': 0.7}, {'CRRA': 3.303133696480183, 'WealthShare': 0.6468852446280728}, {'CRRA': 3.456209780749042, 'WealthShare': 0.6330260460920982}, {'CRRA': 3.393118516188777, 'WealthShare': 0.5377100904840499}, {'CRRA': 3.5397695211139557, 'WealthShare': 0.5509522833458697}, {'CRRA': 3.4758674679770443, 'WealthShare': 0.5400001711234805}, {'CRRA': 3.434064260747328, 'WealthShare': 0.5243866818724292}, {'CRRA': 3.4137951701197853, 'WealthShare': 0.5371316890165021}, {'CRRA': 3.3827347013962545, 'WealthShare': 0.5356230431833575}, {'CRRA': 3.3886868172194666, 'WealthShare': 0.5404246379718759}, {'CRRA': 3.3929877161471165, 'WealthShare': 0.5351278082679701}, {'CRRA': 3.394411039178595, 'WealthShare': 0.5376829528332149}, {'CRRA': 3.3950422594077354, 'WealthShare': 0.5375349901544691}, {'CRRA': 3.394403048866773, 'WealthShare': 0.5380060531468045}, {'CRRA': 3.394330571104913, 'WealthShare': 0.5375428125336963}, {'CRRA': 3.3942641227083596, 'WealthShare': 0.5372265179426579}, {'CRRA': 3.394173480699256, 'WealthShare': 0.5375027375064382}, {'CRRA': 3.394410295274285, 'WealthShare': 0.537555952565116}, {'CRRA': 3.394368932528548, 'WealthShare': 0.5375301416190236}, {'CRRA': 3.394326995715963, 'WealthShare': 0.5375626935376785}, {'CRRA': 3.3943246528733915, 'WealthShare': 0.5375346281603528}, {'CRRA': 3.3943356046345925, 'WealthShare': 0.5375424051802747}, {'CRRA': 3.394329304009661, 'WealthShare': 0.5375449965801891}, {'CRRA': 3.3943299520790617, 'WealthShare': 0.5375417122135792}, {'CRRA': 3.3943315684886763, 'WealthShare': 0.5375428848222087}, {'CRRA': 3.3943296151296067, 'WealthShare': 0.5375425190865739}, {'CRRA': 3.394329649205157, 'WealthShare': 0.5375432004343061}, {'CRRA': 3.3943315656125383, 'WealthShare': 0.537542917197843}, {'CRRA': 3.3943295696848708, 'WealthShare': 0.5375427637114514}, {'CRRA': 3.3943305792741665, 'WealthShare': 0.5375438125003273}, {'CRRA': 3.394330680756775, 'WealthShare': 0.5375438065037508}, {'CRRA': 3.394331046758134, 'WealthShare': 0.5375419308777375}, {'CRRA': 3.394330573661952, 'WealthShare': 0.537543812530427}], 'criterion': [0.6607127140516494, nan, 0.7133179969678111, 0.9122250510532794, 1.738814245006418, 50904625.67032898, 116246.71705175268, 2.371483432043779, 1.21707149810191, 1.7742326776217476, 2.1844788091340064, nan, 1.9431589702823127, 1.4900363321437669, 1.2619256393554017, 0.35160518753317954, 0.42838937661127785, 0.36008666150771423, 0.4076867472422282, 0.35498950666354673, 0.3567456338142511, 0.37666426015405113, 0.354837869983808, 0.35106354160290787, 0.36144004176498795, 0.363428759218245, 0.3478920125068537, 0.36129474761431013, 0.36389252870011624, 0.3506283566979166, 0.35511565098469156, 0.35306860499987824, 0.3541434194955959, 0.3617849686943767, 0.3540838274551238, 0.35370468558579427, 0.3585447569605501, 0.35897473002769464, 0.3580444096251574, 0.35816686951261745, 0.3603114273224131, 0.3557617475147986, 0.35761591726779984, 0.3641098629626268, 0.3476591866783385], 'runtime': [0.0, 0.9876696610008366, 1.0269310979929287, 1.0667359170038253, 1.1066995759902056, 1.146514563006349, 1.188373284006957, 1.2297300389909651, 1.2781228669919074, 1.3206576250086073, 1.3693073309841566, 1.420242080988828, 1.4671239820017945, 2.6949373869865667, 3.6092029479914345, 4.507678669004235, 5.437428473989712, 6.387053313985234, 7.320997596980305, 8.266617074987153, 9.191725206008414, 10.118209832988214, 11.042316315986682, 11.9644784439879, 12.8848473179969, 13.782509017997654, 14.689824258995941, 15.593694101000438, 16.504139386990573, 17.403498230007244, 18.31440812998335, 19.222797163994983, 20.122583561984356, 21.038069117988925, 21.969905721984105, 22.90410190098919, 23.847403305990156, 24.791996433981694, 25.731922432983993, 26.64831998400041, 27.567248210980324, 28.47857929300517, 29.400026577990502, 30.30448704698938, 31.211013243999332], 'batches': [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]}, 'multistart_info': {...}}], 'exploration_sample': array([[ 3.38599629,  0.53632808],
+       [ 3.125     ,  0.65625   ],
+       [ 6.5       ,  0.525     ],
+       [ 8.1875    ,  0.503125  ],
+       [14.375     ,  0.56875   ],
+       [12.6875    ,  0.678125  ],
+       [16.625     ,  0.48125   ],
+       [17.75      ,  0.6125    ],
+       [ 4.25      ,  0.4375    ],
+       [ 9.875     ,  0.39375   ],
+       [11.        ,  0.35      ],
+       [12.125     ,  0.30625   ],
+       [ 3.6875    ,  0.328125  ],
+       [ 8.75      ,  0.2625    ]]), 'exploration_results': array([3.64521041e-01, 1.78877610e+00, 1.91826957e+00, 2.78701709e+00,
+       4.47833564e+00, 4.73260048e+00, 4.94712751e+00, 5.16688748e+00,
+       3.22686930e+01, 6.03634308e+01, 5.97496752e+02, 2.06329016e+03,
+       2.18671524e+03, 2.20657404e+03])}}"
diff --git a/content/tables/parameters.tex b/content/tables/parameters.tex
new file mode 100644
index 0000000..be69baa
--- /dev/null
+++ b/content/tables/parameters.tex
@@ -0,0 +1,9 @@
+\begin{tabular}{lrrlll}
+\toprule
+Name & criterion & CRRA & WealthShare & BeqFac & BeqShift \\
+\midrule
+Portfolio & 0.895000 & 6.374000 &  &  &  \\
+WarmGlowPortfolio & 7.719000 & 4.706000 &  & 46.465000 & 16.966000 \\
+WealthPortfolio & 0.347000 & 3.386000 & 0.536000 &  &  \\
+\bottomrule
+\end{tabular}