A new hybrid conjugate gradient method of unconstrained optimization methods

被引：0
|
作者：
Nasreddine, Chenna [1 ]
Badreddine, Sellami [1 ]
Mohammed, Belloufi [1 ]
机构：
[1] Mohamed Cherif Messaadia Univ, Lab Informat & Math LIM, Souk Ahras 41000, Algeria
来源：
ASIAN-EUROPEAN JOURNAL OF MATHEMATICS | 2022年 / 15卷 / 04期
关键词：
Nonlinear optimization unconstrained; conjugate gradient; line search; global convergence; CONVEX COMBINATION; CONVERGENCE; DESCENT; FR;
D O I：
10.1142/S179355712250070X
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this paper, we present a new hybrid method to solve a nonlinear unconstrained optimization problem by using conjugate gradient, which is a 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 line search and the global convergence with the strong Wolfe line search. In the end of this paper, we illustrate our method by giving some numerical examples.
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页数：11
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