From 18d145078e12710b195793fffb93afb9efe31a38 Mon Sep 17 00:00:00 2001
From: Michael Orlitzky
Date: Thu, 21 Mar 2013 21:50:23 0400
Subject: [PATCH] Remove the step_size_positive_definite() function; it looks
like it was added by accident and is superceded by
step_length_positive_definite().

optimization/step_size_positive_definite.m  36 
1 file changed, 36 deletions()
delete mode 100644 optimization/step_size_positive_definite.m
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)  + 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 steepestdescent. This function computes
 % the optimal step size alpha for the steepest descent method, in
 % the negativegradient direction, at x.
 %
 % INPUT:
 %
 %  ``Q``  the positivedefinite 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 matrixvector multiplication here, by
 % taking d_k as a parameter. In fact, if the caller is specialized to
 % our problem (1), we can avoid both matrixvector 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

2.33.1