Abstract

Conjugate gradient methods are among the most efficient methods
for solving optimization models. In this paper, a newly proposed conjugate
gradient method is proposed for solving optimization problems as a convex
combination of the Harger-Zhan and Dai-Yaun nonlinear conjugate gradient
methods, which is capable of producing the sufficient descent condition with
global convergence properties under the strong Wolfe conditions. The numerical
results demonstrate the efficiency of the proposed method with some
benchmark problems.

Information

Item Type:	Journal
Divisions:	» Laboratory of Computer Science and Mathematics
ePrint ID:	4861
Date Deposited:	2024-02-19
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/4861

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