From: Michael Orlitzky <michael@orlitzky.com>
Date: Fri, 22 Mar 2013 01:50:23 +0000 (-0400)
Subject: Remove the step_size_positive_definite() function; it looks like it was added by... 
X-Git-Url: http://gitweb.michael.orlitzky.com/?p=octave.git;a=commitdiff_plain;h=18d145078e12710b195793fffb93afb9efe31a38;ds=sidebyside

Remove the step_size_positive_definite() function; it looks like it was added by accident and is superceded by step_length_positive_definite().
---

diff --git a/optimization/step_size_positive_definite.m b/optimization/step_size_positive_definite.m
deleted file mode 100644
index e32229c..0000000
--- a/optimization/step_size_positive_definite.m
+++ /dev/null
@@ -1,36 +0,0 @@
-function alpha = step_size_positive_definite(Q, b, x)
-  % Let,
-  %
-  %   f(x) = (1/2)<Qx,x> - <b,x> + a    (1)
-  %
-  % where Q is symmetric and positive definite.
-  %
-  % If we seek to minimize f; that is, to solve Qx = b, then we can do
-  % so using the method of steepest-descent. This function computes
-  % the optimal step size alpha for the steepest descent method, in
-  % the negative-gradient direction, at x.
-  %
-  % INPUT:
-  %
-  %   - ``Q`` -- the positive-definite matrix in the definition of f(x).
-  %
-  %   - ``b`` -- the known vector in the definition of f(x).
-  %
-  % OUTPUT:
-  %
-  %   - ``alpha`` -- the optimal step size in the negative gradient
-  %     direction.
-  %
-  % NOTES:
-  %
-  % It is possible to save one matrix-vector multiplication here, by
-  % taking d_k as a parameter. In fact, if the caller is specialized to
-  % our problem (1), we can avoid both matrix-vector multiplications here
-  % at the expense of some added roundoff error.
-  %
-
-  % The gradient of f(x) is Qx - b, and d_k is the negative gradient
-  % direction.
-  d_k = b - Q*x;
-  alpha = (d_k' * d_k) / (d_k' * Q * d_k);
-end