From db3b25be5b74472bc3cf2bab16330d328d17018c Mon Sep 17 00:00:00 2001 From: Siddharth Srinivasan Date: Thu, 12 May 2022 12:52:45 -0700 Subject: [PATCH] Bug fix + parameter changes. --- demo_brain.py | 6 ++-- demo_cardiac_unconstrained.py | 6 ++-- demo_mrf3d.py | 20 ++++++------- demo_radial.py | 6 ++-- figures/brain.png | Bin 562598 -> 555494 bytes figures/cardiac.png | Bin 2004150 -> 2076427 bytes figures/cardiac_c.png | Bin 460254 -> 454352 bytes figures/cardiac_m.png | Bin 319004 -> 319890 bytes figures/mrf3d.png | Bin 1844019 -> 1558259 bytes figures/radial.png | Bin 2853100 -> 2680942 bytes optalg.py | 2 +- plot_brain.ipynb | 12 ++++---- plot_cardiac_consensus.ipynb | 2 +- plot_cardiac_constrained.ipynb | 2 +- plot_cardiac_unconstrained.ipynb | 19 ++++++------- plot_mrf3d.ipynb | 47 ++++++++++++++++++------------- plot_radial.ipynb | 16 +++++------ 17 files changed, 72 insertions(+), 66 deletions(-) diff --git a/demo_brain.py b/demo_brain.py index 8119674..caf5f38 100644 --- a/demo_brain.py +++ b/demo_brain.py @@ -1,5 +1,5 @@ devnum = 0 # Device to run optimization on. -ptol = 0.5 # Percentage tolerance between iterates. +ptol = 0.2 # Percentage tolerance between iterates. pdeg = 2 # Degree of polynomial to use. norm = "l_2" # Cost function for polynomial optimization. l = 0 # Smallest eigenvalue of A.H * A, if known. @@ -60,7 +60,7 @@ # Empty run. optalg.unconstrained(2, 1, A, b, lp_proxg, verbose=False) # Actual run. - optalg.unconstrained(500, ptol, A, b, lp_proxg, save=loc) + optalg.unconstrained(100, ptol, A, b, lp_proxg, save=loc) loc = "results/brain/pc" os.mkdir(loc) @@ -68,5 +68,5 @@ optalg.unconstrained(2, 1, A, b, pc_proxg, save=loc, l=l, norm=norm, pdeg=pdeg, verbose=False) # Actual run. - optalg.unconstrained(500, ptol, A, b, pc_proxg, save=loc, \ + optalg.unconstrained(100, ptol, A, b, pc_proxg, save=loc, \ l=l, norm=norm, pdeg=pdeg) diff --git a/demo_cardiac_unconstrained.py b/demo_cardiac_unconstrained.py index d2557be..c4e0796 100644 --- a/demo_cardiac_unconstrained.py +++ b/demo_cardiac_unconstrained.py @@ -1,5 +1,5 @@ devnum = 0 # Device to run optimization on. -ptol = 0.5 # Percentage tolerance between iterates. +ptol = 0.2 # Percentage tolerance between iterates. pdeg = 4 # Degree of polynomial to use. norm = "l_2" # Cost function for polynomial optimization. l = 0 # Smallest eigenvalue of A.H * A, if known. @@ -59,7 +59,7 @@ # Empty run. optalg.unconstrained(2, 1, A, b, lp_proxg, verbose=False) # Actual run. - optalg.unconstrained(500, ptol, A, b, lp_proxg, save=loc) + optalg.unconstrained(100, ptol, A, b, lp_proxg, save=loc) loc = "results/cardiac_unconstrained/pc" os.mkdir(loc) @@ -67,5 +67,5 @@ optalg.unconstrained(2, 1, A, b, pc_proxg, l=l, norm=norm, \ pdeg=pdeg, verbose=False) # Actual run. - optalg.unconstrained(500, ptol, A, b, pc_proxg, save=loc, \ + optalg.unconstrained(100, ptol, A, b, pc_proxg, save=loc, \ l=l, norm=norm, pdeg=pdeg) diff --git a/demo_mrf3d.py b/demo_mrf3d.py index 6cf3185..f14b9e0 100644 --- a/demo_mrf3d.py +++ b/demo_mrf3d.py @@ -1,11 +1,11 @@ -devnum = 0 # Device to run optimization on. -ptol = 0.5 # Percentage tolerance between iterates. -pdeg = 9 # Degree of polynomial to use. -norm = "l_2" # Cost function for polynomial optimization. -l = 0 # Smallest eigenvalue of A.H * A, if known. -blk = 8 # Block size for locally low-rank. -lp_lamda = 2.5e-6 # Regularization value for LP. -pc_lamda = 5e-5 # Regularization value for PC. +devnum = 0 # Device to run optimization on. +ptol = 0.2 # Percentage tolerance between iterates. +pdeg = 9 # Degree of polynomial to use. +norm = "l_2" # Cost function for polynomial optimization. +l = 0 # Smallest eigenvalue of A.H * A, if known. +blk = 8 # Block size for locally low-rank. +lp_lamda = 2.5e-6 # Regularization value for LP. +pc_lamda = 8.75e-5 # Regularization value for PC. ksp_file = "data/mrf3d/ksp.npy" trj_file = "data/mrf3d/trj.npy" @@ -93,7 +93,7 @@ # Actual run. loc = "results/mrf3d/lp_%s" % repr(lp_lamda) - optalg.unconstrained(500, ptol, A, b, lp_proxg, save=loc, idx=idx) + optalg.unconstrained(100, ptol, A, b, lp_proxg, save=loc, idx=idx) # Empty run. optalg.unconstrained(2, 1, A, b, pc_proxg, pdeg=pdeg, idx=idx, \ @@ -101,5 +101,5 @@ # Actual run. loc = "results/mrf3d/pc_%s" % repr(pc_lamda) - optalg.unconstrained(500, ptol, A, b, pc_proxg, save=loc, \ + optalg.unconstrained(100, ptol, A, b, pc_proxg, save=loc, \ pdeg=pdeg, idx=idx) diff --git a/demo_radial.py b/demo_radial.py index dab3c5c..60289f1 100644 --- a/demo_radial.py +++ b/demo_radial.py @@ -1,5 +1,5 @@ devnum = 0 # Device to run optimization on. -ptol = 0.5 # Percentage tolerance between iterates. +ptol = 0.2 # Percentage tolerance between iterates. pdeg = 5 # Degree of polynomial to use. norm = "l_2" # Cost function for polynomial optimization. l = 0 # Smallest eigenvalue of A.H * A, if known. @@ -59,11 +59,11 @@ # Empty run. optalg.unconstrained(2, 1, A, b, lp_proxg, verbose=False) # Actual run. - optalg.unconstrained(500, 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