Put back the redundant step_length_positive_definite() parameter.

[octave.git] / optimization / step_length_cgm.m

diff --git a/optimization/step_length_cgm.m b/optimization/step_length_cgm.m
index 60f7cd491431b94cf183cb7caa375ebff19e929f..87062f3e2d0bdbb10804851c0a814a68e8cb6894 100644 (file)
--- a/optimization/step_length_cgm.m
+++ b/optimization/step_length_cgm.m
@@ -1,40 +1,40 @@
 function alpha = step_length_cgm(r, A, p)
-  ##
-  ## Compute the step length for the conjugate gradient method (CGM).
-  ## The CGM attempts to solve,
-  ##
-  ##   Ax = b
-  ##
-  ## or equivalently,
-  ##
-  ##   min[phi(x) = (1/2)<Ax,x> - <b,x>]
-  ##
-  ## where ``A`` is positive-definite. In the process, we need to
-  ## compute a number of search directions ``p`` and optimal step
-  ## lengths ``alpha``; i.e.,
-  ##
-  ##   x_{k+1} = x_{k} + alpha_{k}*p_{k}
-  ##
-  ## This function computes alpha_{k} in the formula above.
-  ##
-  ## INPUT:
-  ##
-  ##   - ``r`` -- The residual, Ax - b, at the current step.
-  ##
-  ##   - ``A`` -- The matrix ``A`` in the formulation above.
-  ##
-  ##   - ``p`` -- The current search direction.
-  ##
-  ## OUTPUT:
-  ##
-  ##   - ``alpha`` -- The minimizer of ``f(x) = x + alpha*p`` along ``p`.
-  ##
-  ## NOTES:
-  ##
-  ## All vectors are assumed to be *column* vectors.
-  ##
+  %
+  % Compute the step length for the conjugate gradient method (CGM).
+  % The CGM attempts to solve,
+  %
+  %   Ax = b
+  %
+  % or equivalently,
+  %
+  %   min[phi(x) = (1/2)<Ax,x> - <b,x>]
+  %
+  % where ``A`` is positive-definite. In the process, we need to
+  % compute a number of search directions ``p`` and optimal step
+  % lengths ``alpha``; i.e.,
+  %
+  %   x_{k+1} = x_{k} + alpha_{k}*p_{k}
+  %
+  % This function computes alpha_{k} in the formula above.
+  %
+  % INPUT:
+  %
+  %   - ``r`` -- The residual, Ax - b, at the current step.
+  %
+  %   - ``A`` -- The matrix ``A`` in the formulation above.
+  %
+  %   - ``p`` -- The current search direction.
+  %
+  % OUTPUT:
+  %
+  %   - ``alpha`` -- The minimizer of ``f(x) = x + alpha*p`` along ``p`.
+  %
+  % NOTES:
+  %
+  % All vectors are assumed to be *column* vectors.
+  %
 
-  ## A simple calculation should convince you that the gradient of
-  ## phi(x) above is Ax - b == r.
+  % A simple calculation should convince you that the gradient of
+  % phi(x) above is Ax - b == r.
   alpha = step_length_positive_definite(r, A, p);
 end