X-Git-Url: http://gitweb.michael.orlitzky.com/?p=octave.git;a=blobdiff_plain;f=optimization%2Fconjugate_gradient_method.m;h=2fab666b7794ccb1d583dcc56057e11362bb9307;hp=a6401e5ec21ff963883368576deb59ff0e1b36d0;hb=c40bbb2f7d9073193f107bbe252d4e9c8509a203;hpb=5ef96eb244b2a52886a31fc9873db479f52a6483

diff --git a/optimization/conjugate_gradient_method.m b/optimization/conjugate_gradient_method.m
index a6401e5..2fab666 100644
--- a/optimization/conjugate_gradient_method.m
+++ b/optimization/conjugate_gradient_method.m
@@ -35,15 +35,18 @@ function [x, k] = conjugate_gradient_method(A, b, x0, tolerance, max_iterations)
   %
   % All vectors are assumed to be *column* vectors.
   %
+  % The rather verbose name of this function was chosen to avoid
+  % conflicts with other implementations.
+  %
   n = length(x0);
   M = eye(n);
 
   % The standard CGM is equivalent to the preconditioned CGM is you
   % use the identity matrix as your preconditioner.
-  [x, k] = preconditioned_conjugate_gradient_method(A,
- 						    M,
-						    b,
-						    x0,
-						    tolerance,
+  [x, k] = preconditioned_conjugate_gradient_method(A, ...
+ 						    M, ...
+						    b, ...
+						    x0, ...
+						    tolerance, ...
 						    max_iterations);
 end