Small changes to documentation

docs/source/algorithms.md

@@ -392,7 +392,7 @@ install optimagic.
     .. warning::
         In our benchmark using a quadratic objective function, the trust_constr
         algorithm did not find the optimum very precisely (less than 4 decimal places).
-        If you require high precision, you should refine an optimum found with Powell
+        If you require high precision, you should refine an optimum found with trust_constr
         with another local optimizer.
 
     .. note::
@@ -907,12 +907,6 @@ We implement a few algorithms from scratch. They are currently considered experi
     and therefore may require fewer iterations to arrive at a local optimum than
     Nelder-Mead.
 
-    The criterion function :func:`func` should return a dictionary with the following
-    fields:
-
-    1. ``"value"``: The sum of squared (potentially weighted) errors.
-    2. ``"root_contributions"``: An array containing the root (weighted) contributions.
-
     Scaling the problem is necessary such that bounds correspond to the unit hypercube
     :math:`[0, 1]^n`. For unconstrained problems, scale each parameter such that unit
     changes in parameters result in similar order-of-magnitude changes in the criterion
@@ -1015,12 +1009,6 @@ need to have [petsc4py](https://pypi.org/project/petsc4py/) installed.
     and therefore may require fewer iterations to arrive at a local optimum than
     Nelder-Mead.
 
-    The criterion function :func:`func` should return a dictionary with the following
-    fields:
-
-    1. ``"value"``: The sum of squared (potentially weighted) errors.
-    2. ``"root_contributions"``: An array containing the root (weighted) contributions.
-
     Scaling the problem is necessary such that bounds correspond to the unit hypercube
     :math:`[0, 1]^n`. For unconstrained problems, scale each parameter such that unit
     changes in parameters result in similar order-of-magnitude changes in the criterion

docs/source/how_to/how_to_algorithm_selection.ipynb

@@ -52,7 +52,7 @@
     "    E[\"Can you exploit<br/>a least-squares<br/>structure?\"] -- yes --> F[\"differentiable?\"]\n",
     "    E[\"Can you exploit<br/>a least-squares<br/>structure?\"] -- no --> G[\"differentiable?\"]\n",
     "\n",
-    "    F[\"differentiable?\"] -- yes --> H[\"scipy_ls_lm<br/>scipy_ls_trf<br/>scipy_ls_dogleg\"]\n",
+    "    F[\"differentiable?\"] -- yes --> H[\"scipy_ls_lm<br/>scipy_ls_trf<br/>scipy_ls_dogbox\"]\n",
     "    F[\"differentiable?\"] -- no --> I[\"nag_dflos<br/>pounders<br/>tao_pounders\"]\n",
     "\n",
     "    G[\"differentiable?\"] -- yes --> J[\"scipy_lbfgsb<br/>nlopt_lbfgsb<br/>fides\"]\n",

src/optimagic/optimizers/_pounders/gqtpar.py

@@ -55,7 +55,7 @@ def gqtpar(model, x_candidate, *, k_easy=0.1, k_hard=0.2, maxiter=200):
             - ``linear_terms``, a np.ndarray of shape (n,) and
             - ``square_terms``, a np.ndarray of shape (n,n).
         x_candidate (np.ndarray): Initial guess for the solution of the subproblem.
-        k_easy (float): topping criterion for the "easy" case.
+        k_easy (float): Stopping criterion for the "easy" case.
         k_hard (float): Stopping criterion for the "hard" case.
         maxiter (int): Maximum number of iterations to perform. If reached,
             terminate.

src/optimagic/optimizers/_pounders/pounders_auxiliary.py

@@ -240,7 +240,7 @@ def solve_subproblem(
         gtol_rel_conjugate_gradient (float): Convergence tolerance for the relative
             gradient norm in the conjugate gradient step of the trust-region
             subproblem ("bntr").
-        k_easy (float): topping criterion for the "easy" case in the trust-region
+        k_easy (float): Stopping criterion for the "easy" case in the trust-region
             subproblem ("gqtpar").
         k_hard (float): Stopping criterion for the "hard" case in the trust-region
             subproblem ("gqtpar").

src/optimagic/optimizers/pounders.py

@@ -262,7 +262,7 @@ def internal_solve_pounders(
         gtol_rel_conjugate_gradient_sub (float): Convergence tolerance for the
             relative gradient norm in the conjugate gradient step of the trust-region
             subproblem if "cg" is used as ``conjugate_gradient_method_sub`` ("bntr").
-        k_easy_sub (float): topping criterion for the "easy" case in the trust-region
+        k_easy_sub (float): Stopping criterion for the "easy" case in the trust-region
             subproblem ("gqtpar").
         k_hard_sub (float): Stopping criterion for the "hard" case in the trust-region
             subproblem ("gqtpar").