A CONJUGATE GRADIENT METHOD WITH STRONG WOLFE POWELL LINE SEARCH FOR UNCONSTRAINED OPTIMIZATION

Abstract

In this paper, a modified conjugate gradient method is presented for solving large-scale unconstrained optimization problems, which possesses the sufficient descent property with Strong Wolfe Powell line search. A global convergence result is proved when the (SWP) line search is used under some conditions. Computational Results, for a set consisting of 138 unconstrained optimization test problems, show that this new conjugate gradient algorithm seems to converge more stably and is superior to other similar methods in many situations