Abstract

A new hybrid conjugate gradient algorithm is proposed for solving unconstrained optimization problems, the conjugate gradient parameter β_kis computed as a convex combination of Hager& Zhang et Dai and Yuan , producing a descent direction at each iteration and globally converges provided the linear search meets the requirements of Wolfe. Numerical experiments are performed to test the effectiveness of the new method, which confirms the potential of this method.
Key Words : Unconstrained optimization, Hybrid conjugate gradient method, algorithm, global convergence, line search of Wolfe.

Information

Item Type:	Thesis
Divisions:	» Laboratory of Computer Science and Mathematics » Faculty of Science and Technology
ePrint ID:	2619
Date Deposited:	2021-07-08
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/2619

BibTex