Bug fix + parameter changes.

sidward · May 14, 2022 · db3b25b · db3b25b
1 parent a692176
commit db3b25b
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Showing 17 changed files with 72 additions and 66 deletions.
diff --git 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.
@@ -60,13 +60,13 @@
     # 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)
     # Empty run.
     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
@@ -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.
@@ -59,13 +59,13 @@
     # 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)
     # Empty run.
     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
@@ -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"
@@ -93,13 +93,13 @@
 
     # 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, \
                          verbose=False)
 
     # 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
@@ -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, ptol, A, b, lp_proxg, save=loc)
+    optalg.unconstrained(100, ptol, A, b, lp_proxg, save=loc)
 
     loc = "results/radial/pc"
     os.mkdir(loc)
     # Empty run.
     optalg.unconstrained(2, 1, A, b, pc_proxg, pdeg=pdeg, verbose=False)
     # Actual run.
-    optalg.unconstrained(500, ptol, A, b, pc_proxg, save=loc, pdeg=pdeg)
+    optalg.unconstrained(100, ptol, A, b, pc_proxg, save=loc, pdeg=pdeg)
diff --git a/figures/brain.png b/figures/brain.png
diff --git a/figures/cardiac.png b/figures/cardiac.png
diff --git a/figures/cardiac_c.png b/figures/cardiac_c.png
diff --git a/figures/cardiac_m.png b/figures/cardiac_m.png
diff --git a/figures/mrf3d.png b/figures/mrf3d.png
diff --git a/figures/radial.png b/figures/radial.png
diff --git a/optalg.py b/optalg.py
@@ -83,7 +83,7 @@ def unconstrained(num_iters, ptol, A, b, proxg, pdeg=None,
       x = proxg(1, x - P(gr))
 
       if accelerate:
-        if l == 0:
+        if l > 0:
           # DOI: 10.1007/978-3-319-91578-4_2
           step = (1 - l**(0.5))/(1 + l**(0.5))
         elif stepfn == None:

diff --git a/plot_brain.ipynb b/plot_brain.ipynb
diff --git a/plot_cardiac_consensus.ipynb b/plot_cardiac_consensus.ipynb
diff --git a/plot_cardiac_constrained.ipynb b/plot_cardiac_constrained.ipynb
diff --git a/plot_cardiac_unconstrained.ipynb b/plot_cardiac_unconstrained.ipynb
diff --git a/plot_mrf3d.ipynb b/plot_mrf3d.ipynb
diff --git a/plot_radial.ipynb b/plot_radial.ipynb