A modified Polak-Ribiere-Polyak descent method for unconstrained optimization

被引：3

作者：

Qu, Aiping ^{[1
,2
]}

Li, Min ^{[2
]}

Xiao, Yue ^{[3
]}

Liu, Juan ^{[1
]}

机构：

[1] Wuhan Univ, Sch Comp, Wuhan 430072, Peoples R China

[2] Huaihua Univ, Dept Math, Huaihua 418000, Peoples R China

[3] Huaihua Univ, Int Off, Huaihua 418000, Peoples R China

来源：

OPTIMIZATION METHODS & SOFTWARE | 2014年 / 29卷 / 01期

关键词：

unconstrained optimization; conjugate gradient method; sufficient descent property; global convergence; CONJUGATE-GRADIENT METHODS;

D O I：

10.1080/10556788.2012.755182

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

In this paper, a modified Polak-Ribiere-Polyak (MPRP) conjugate gradient method for smooth unconstrained optimization is proposed. This method produces at each iteration a descent direction, and this property is independent of the line search adopted. Under standard assumptions, we prove that the MPRP method using strong Wolfe conditions is globally convergent. The results of computational experiments are reported and show the effectiveness of the proposed method.

引用

页码：177 / 188

页数：12

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