Merge pull request #22 from jtamir/fix_gradmethod_maxeig

for LinearLeastSquares App, dont compute max eig if alpha is specifie…
mikgroup · Jul 2, 2019 · d7f865c · d7f865c
2 parents 5743d41 + 9e21875
commit d7f865c
Show file tree

Hide file tree

Showing 2 changed files with 9 additions and 9 deletions.
diff --git a/sigpy/app.py b/sigpy/app.py
@@ -290,14 +290,14 @@ def gradf(x):
         if self.mu != 0:
             AHA += self.mu * I
 
-        max_eig = MaxEig(AHA, dtype=self.x.dtype,
-                         device=self.x_device, max_iter=self.max_power_iter,
-                         show_pbar=self.show_pbar).run()
-
-        if max_eig == 0:
-            self.alpha = 1
-        else:
-            self.alpha = 1 / max_eig
+        if self.alpha is None:
+            max_eig = MaxEig(AHA, dtype=self.x.dtype, device=self.x_device,
+                             max_iter=self.max_power_iter,
+                             show_pbar=self.show_pbar).run()
+            if max_eig == 0:
+                self.alpha = 1
+            else:
+                self.alpha = 1 / max_eig
 
         self.alg = GradientMethod(
             gradf,

diff --git a/tests/test_app.py b/tests/test_app.py
@@ -96,7 +96,7 @@ def test_precond_LinearLeastSquares(self):
         x_rec = app.LinearLeastSquares(A, y, show_pbar=False).run()
         npt.assert_allclose(x_rec, x_lstsq, atol=1e-3)
 
-        alpha = p / app.MaxEig(P * A.H * A, show_pbar=False).run()
+        alpha = 1 / app.MaxEig(P * A.H * A, show_pbar=False).run()
         x_rec = app.LinearLeastSquares(
             A,
             y,