A class of one parameter conjugate gradient methods

被引：1

作者：

Yao, Shengwei ^{[1
]}

Lu, Xiwen ^{[2
]}

Ning, Liangshuo ^{[1
]}

Li, Feifei ^{[1
]}

机构：

[1] Guangxi Univ Finance & Econ, Sch Informat & Stat, Nanning 530003, Peoples R China

[2] E China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2015年 / 265卷

关键词：

Unconstrained optimization; Continuous optimization; Conjugate gradient method; Global convergence; Wolfe line search; CONVERGENCE PROPERTIES; DESCENT;

D O I：

10.1016/j.amc.2015.05.115

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper proposes a class of one parameter conjugate gradient methods, which can be regarded as some kinds of convex combinations of some modified form of PRP and HS methods. The scalar beta(k) has the form of phi(k)/phi(k-1) mu(k). The convergence of the given methods is analyzed by some unified tools which show the global convergence of the proposed methods. Numerical experiments with the CUTE collections show that the proposed methods are promising. (C) 2015 Elsevier Inc. All rights reserved.

引用

页码：708 / 722

页数：15

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