New hybrid conjugate gradient method as a convex combination of FR and BA methods

被引：5

作者：

Delladji, Sarra ^{[1
]}

Belloufi, Mohammed ^{[1
]}

Sellami, Badreddine ^{[1
]}

机构：

[1] Mohamed Cherif Messaadia Univ, Lab Informat & Math LiM, Soak Ahras 41000, Algeria

来源：

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES | 2021年 / 42卷 / 03期

关键词：

Unconstrained optimization; Hybrid conjugate gradient; Convex combination; Global convergence; GLOBAL CONVERGENCE;

D O I：

10.1080/02522667.2020.1778841

中图分类号：

G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];

学科分类号：

1205 ; 120501 ;

摘要：

Conjugate gradient method is the most used for solving the unconstrained optimization problem. In this paper we propose a new hybrid conjugate gradient method which is obtained from a convex combination of FR and BA. Under some conditions the given method have the sufficient descent and global convergence property. Numerical results and their performance support the effectiveness and robustness of our procedure.

引用

页码：591 / 602

页数：12

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