2,3,7];
M = eye(3);
-
b = [1;2;3];
-
x0 = [1;1;1];
-## Solved over the rationals.
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);
true, ...
norm(diff) < 1e-6);
+pcgm_simple = simple_preconditioned_cgm(A, M, b, x0, 1e-6, 1000);
+diff = norm(pcgm_simple - pcgm);
+
+unit_test_equals("PCGM agrees with SimplePCGM when M == I", ...
+ true, ...
+ norm(diff) < 1e-6);
## Needs to be symmetric!
M = [0.97466, 0.24345, 0.54850; ...
unit_test_equals("PCGM agrees with CGM when M != I", ...
true, ...
norm(diff) < 1e-6);
+
+
+pcgm_simple = simple_preconditioned_cgm(A, M, b, x0, 1e-6, 1000);
+diff = norm(pcgm_simple - pcgm);
+
+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