A New Hybrid of DY and CGSD Conjugate Gradient Methods for Solving Unconstrained Optimization Problems

Abstract

In this article, we present a new hybrid conjugate gradient method for solving large Scale in unconstrained optimization problems. This method is a convex combination of Dai-Yuan conjugate gradient and Andrei- sufficient descent condition, satisfies the famous D-L conjugacy condition and in the same time solidarities with the newton direction with the suitable condition. The suggestion method always yields a descent search direction at each it iteration. Under strong wolfe powell(SWP) line search condition, the direction satisfy the global convergence of the proposed method is established. Finally, the results we achieved are good and it is show that our method is forceful and effective.