A new conjugate gradient method for unconstrained optimization with sufficient descent

Abstract

Conjugate gradient (CG) methods represent an important computational innovation in solving large-scaled unconstrained optimization problems. There are many different versions of CG methods. Although some methods are equivalent to each other, their performances are quite different. This paper presents a new CG method based on modification of the original CG methods. The important criteria of this new CG method are its global convergence properties. Numerical result shows that this new CG method performs better than the original CG methods.