unit_test_equals("CGM works on an example", ...
true, ...
norm(diff) < 1e-6);
+
+
+# Let's test Octave's pcg() against our method on some easy matrices.
+max_iterations = 100000;
+tolerance = 1e-11;
+
+for n = [ 5, 10, 25, 50, 100 ]
+ A = random_positive_definite_matrix(5, 1000);
+
+ # Assumed by Octave's implementation when you don't supply a
+ # preconditioner.
+ x0 = zeros(5, 1);
+ b = unifrnd(-1000, 1000, 5, 1);
+ [o_x, o_flag, o_relres, o_iter] = pcg(A, b, tolerance, max_iterations);
+ [x, k] = conjugate_gradient_method(A, b, x0, tolerance, max_iterations);
+
+ diff = norm(o_x - x);
+ msg = sprintf("Our CGM agrees with Octave's, n=%d.", n);
+ unit_test_equals(msg, true, norm(diff) < 1e-10);
+end
unit_test_equals("PCGM agrees with Simple PCGM when M != I", ...
true, ...
norm(diff) < 1e-6);
+
+
+# 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);
+
+ # Assumed by Octave's implementation when you don't supply a
+ # preconditioner.
+ x0 = zeros(5, 1);
+ b = unifrnd(-1000, 1000, 5, 1);
+ [o_x, o_flag, o_relres, o_iter] = pcg(A, b, tolerance, max_iterations, C, C');
+ [x, k] = preconditioned_conjugate_gradient_method(A,
+ M,
+ b,
+ x0,
+ tolerance,
+ max_iterations);
+ diff = norm(o_x - x);
+ msg = sprintf("Our PCGM agrees with Octave's, n=%d.", n);
+ unit_test_equals(msg, true, norm(diff) < 1e-10);
+end