• A NEW CONJUGATE GRADIENT ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

Abstract

In this paper we develop a new class of conjugate gradient methods for unconstrained optimization, conjugate gradient methods are widely used for large scale unconstrained optimization problems. Most of conjugate gradient methods don’t always generate a descent search direction, so the descent condition is usually assumed in the analysis and implementation. The conjugate gradient method is a very useful technique for solving minimization problems and has wide applications in many fields. In addition to the performance of a modified Wolf line search rules related to CG-method type method with the results from standard Wolf line search rules are compared.