From: Michael Orlitzky <michael@orlitzky.com>
Date: Mon, 25 Mar 2013 16:50:29 +0000 (-0400)
Subject: Use the infinity norm in the PCGM tests.
X-Git-Url: https://gitweb.michael.orlitzky.com/?a=commitdiff_plain;h=36afdc59f58ac6a8a1fe366e1cde070db24905ac;p=octave.git

Use the infinity norm in the PCGM tests.
---

diff --git a/tests/preconditioned_conjugate_gradient_method_tests.m b/tests/preconditioned_conjugate_gradient_method_tests.m
index 2c8ff84..6a11a63 100644
--- a/tests/preconditioned_conjugate_gradient_method_tests.m
+++ b/tests/preconditioned_conjugate_gradient_method_tests.m
@@ -1,3 +1,9 @@
+## Used throughout. The PCGM uses the infinity norm as the stopping
+## condition, so we had better also.
+max_iterations = 100000;
+tolerance = 1e-11;
+
+## First a simple example.
 A = [5,1,2; ...
      1,6,3; ...
      2,3,7];
@@ -6,50 +12,57 @@ M = eye(3);
 b = [1;2;3];
 x0 = [1;1;1];
 
-cgm  = conjugate_gradient_method(A, b, x0, 1e-6, 1000);
-pcgm = preconditioned_conjugate_gradient_method(A, M, b, x0, 1e-6, 1000);
-diff = norm(cgm - pcgm);
+cgm  = conjugate_gradient_method(A, b, x0, tolerance, max_iterations);
+pcgm = preconditioned_conjugate_gradient_method(A, ...
+						M, ...
+						b, ...
+						x0, ...
+						tolerance, ...
+						max_iterations);
+diff = norm(cgm - pcgm, 'inf');
 
 unit_test_equals("PCGM agrees with CGM when M == I", ...
 		 true, ...
-		 norm(diff) < 1e-6);
+		 diff < 2*tolerance);
 
-pcgm_simple = simple_preconditioned_cgm(A, M, b, x0, 1e-6, 1000);
-diff = norm(pcgm_simple - pcgm);
+pcgm_simple = simple_preconditioned_cgm(A, M, b, x0, tolerance, max_iterations);
+diff = norm(pcgm_simple - pcgm, 'inf');
 
 unit_test_equals("PCGM agrees with SimplePCGM when M == I", ...
 		 true, ...
-		 norm(diff) < 1e-6);
+		 diff < 2*tolerance);
 
 ## Needs to be symmetric!
 M = [0.97466, 0.24345, 0.54850; ...
      0.24345, 0.73251, 0.76639; ...
      0.54850, 0.76639, 1.47581];
 
-pcgm = preconditioned_conjugate_gradient_method(A, M, b, x0, 1e-6, 1000);
-diff = norm(cgm - pcgm);
+pcgm = preconditioned_conjugate_gradient_method(A, ...
+						M, ...
+						b, ...
+						x0, ...
+						tolerance, ...
+						max_iterations);
+diff = norm(cgm - pcgm, 'inf');
 
 unit_test_equals("PCGM agrees with CGM when M != I", ...
 		 true, ...
-		 norm(diff) < 1e-6);
+		 diff < 2*tolerance);
 
 
-pcgm_simple = simple_preconditioned_cgm(A, M, b, x0, 1e-6, 1000);
-diff = norm(pcgm_simple - pcgm);
+pcgm_simple = simple_preconditioned_cgm(A, M, b, x0, tolerance, max_iterations);
+diff = norm(pcgm_simple - pcgm, 'inf');
 
 unit_test_equals("PCGM agrees with Simple PCGM when M != I", ...
 		 true, ...
-		 norm(diff) < 1e-6);
+		 diff < 2*tolerance);
 
 
 # Test again Octave's pcg() function.
-max_iterations = 100000;
-tolerance = 1e-11;
-C = random_positive_definite_matrix(5, 1000);
-M = C*C';
-
 for n = [ 5, 10, 25, 50, 100 ]
   A = random_positive_definite_matrix(5, 1000);
+  C = random_positive_definite_matrix(5, 1000);
+  M = C*C';
 
   # Assumed by Octave's implementation when you don't supply a
   # preconditioner.
@@ -62,7 +75,7 @@ for n = [ 5, 10, 25, 50, 100 ]
 						    x0,
 						    tolerance,
 						    max_iterations);
-  diff = norm(o_x - x);
+  diff = norm(o_x - x, 'inf');
   msg = sprintf("Our PCGM agrees with Octave's, n=%d.", n);
-  unit_test_equals(msg, true, norm(diff) < 1e-10);
+  unit_test_equals(msg, true, diff < 2*tolerance);
 end