Abstract

In this thesis we proved descent direction of the conjugate gradient method which is a combination convex of Fletcher and Reeves (FR) and Polak-Ribière and Polyak (PRP) method,
and present a new hybrid method to solve an nonlinear unconstrained optimization problem
by using conjugate gradient which is convex combination of Liu-Storey (LS) conjugate gradient
method and Hager-Zhang (HZ) conjugate gradient method. This method possesses the sufficient
descent property with Strong Wolfe-Powell line search and the global convergence with the
Strong Wolfe-Powell line search. In the end of this thesis, we illustrate our method by giving
some numerical examples.


Key words:
Conjugate gradient, Global convergence, Algorithm, Unconstrained optimization, Line search.

Information

Item Type:	Thesis
Divisions:	» Faculty of Science and Technology
ePrint ID:	2919
Date Deposited:	2022-03-14
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/2919

BibTex