A Dai-Yuan conjugate gradient algorithm with sufficient descent and conjugacy conditions for unconstrained optimization

被引：30

作者：

Andrei, Neculai ^{[1
]}

机构：

[1] Ctr Adv Modeling & Optimizat, Res Inst Informat, Bucharest 1, Romania

来源：

APPLIED MATHEMATICS LETTERS | 2008年 / 21卷 / 02期

关键词：

unconstrained optimization; conjugate gradient method; sufficient descent condition; conjugacy condition; numerical comparisons;

D O I：

10.1016/j.aml.2007.05.002

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

A modification of the Dai-Yuan conjugate gradient algorithm is proposed. Using exact line search, the algorithm reduces to the original version of the Dai and Yuan computational scheme. For inexact line search the algorithm satisfies both sufficient descent and conjugacy conditions. A global convergence result is proved when the Wolfe line search conditions are used. Computational results, for a set consisting of 750 unconstrained optimization test problems, show that this new conjugate gradient algorithm substantially outperforms the Dai-Yuan conjugate gradient algorithm and comes close to its hybrid variants. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：165 / 171

页数：7

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