X-Git-Url: http://gitweb.michael.orlitzky.com/?p=octave.git;a=blobdiff_plain;f=tests%2Fsteepest_descent_tests.m;h=d6de1ccaf4b013539d45833a70c3e293bb0ad6b6;hp=035c82d20bfafbc56021f1b2a11b7ffc1839dee2;hb=2607d0ce1289b34ecce0a82b11e9935e239fe708;hpb=f7b0583a5615b9314c818e4037af554cfb679d8c

diff --git a/tests/steepest_descent_tests.m b/tests/steepest_descent_tests.m
index 035c82d..d6de1cc 100644
--- a/tests/steepest_descent_tests.m
+++ b/tests/steepest_descent_tests.m
@@ -6,7 +6,7 @@
 ## stopping condition, so we should too.
 ##
 max_iterations = 100000;
-tolerance = 1e-10;
+tolerance = 1e-8;
 
 ## First a simple example.
 Q = [5,1,2; ...
@@ -23,7 +23,7 @@ q = @(x) (1/2)*x'*Q*x - b'*x;
 g = @(x) Q*x - b; % The gradient of q at x.
 
 % The step size algorithm to use in the steepest descent method.
-step_size = @(x) step_length_positive_definite(g(x), Q);
+step_size = @(x) step_length_positive_definite(g(x), Q, -g(x));
 sd = steepest_descent(g, x0, step_size, tolerance, max_iterations);
 
 diff = norm(cgm - sd, 'inf');
@@ -46,7 +46,7 @@ for n = [ 5, 10, 25, 50, 100 ]
   g = @(x) Q*x - b; % The gradient of q at x.
 
   % The step size algorithm to use in the steepest descent method.
-  step_size = @(x) step_length_positive_definite(g(x), Q);
+  step_size = @(x) step_length_positive_definite(g(x), Q, -g(x));
 
   ## pcg() stops when the /relative/ norm falls below tolerance. To
   ## eliminate the relativity, we divide the tolerance by the
@@ -57,10 +57,13 @@ for n = [ 5, 10, 25, 50, 100 ]
 					  max_iterations, ...
 					  C, ...
 					  C');
-  x_sd = steepest_descent(g, x0, step_size, tolerance, max_iterations);
+  [x_sd, k] = steepest_descent(g, x0, step_size, tolerance, max_iterations);
 
-  ## Note: pcg() uses the 2-norm.
-  diff = abs(norm(g(x_pcg)) - norm(g(x_sd), 'inf'));
+  diff = norm(x_pcg - x_sd, 'inf');
   msg = sprintf("Our steepest descent agrees with Octave's pcg, n=%d.", n);
-  unit_test_equals(msg, true, diff <= tolerance);
+
+  ## There's no good way to choose the tolerance here, since each
+  ## individual algorithm terminates based on the (2,infinity)-norm of
+  ## the gradient.
+  unit_test_equals(msg, true, diff <= sqrt(tolerance));
 end