A class of nonmonotone conjugate gradient methods for unconstrained optimization

被引：57

作者：

Liu, GH ^{[1
]}

Jing, LL

Han, LX

Han, D

机构：

[1] Northwestern Univ, Dept Ind Engn & Management Sci, Evanston, IL 60208 USA

[2] Beijing Forestry Univ, Coll Econ & Management, Beijing, Peoples R China

[3] Univ Connecticut, Dept Math, Storrs, CT USA

[4] CUNY City Coll, Dept Comp Sci, New York, NY 10031 USA

来源：

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 1999年 / 101卷 / 01期

关键词：

nonmonotone conjugate gradient method; nonmonotone line search; global convergence; unconstrained optimization;

D O I：

10.1023/A:1021723128049

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 [运筹学与控制论]; 12 [管理学]; 1201 [管理科学与工程]; 1202 [工商管理学]; 120202 [企业管理];

摘要：

In this paper, we introduce a class of nonmonotone conjugate gradient methods, which include the well-known Polak-Ribiere method and Hestenes-Stiefel method as special cases. This class of nonmonotone conjugate gradient methods is proved to be globally convergent when it is applied to solve unconstrained optimization problems with convex objective functions. Numerical experiments show that the nonmonotone Polak-Ribiere method and Hestenes-Stiefel method in this nonmonotone conjugate gradient class are competitive vis-g-vis their monotone counterparts.

引用

页码：127 / 140

页数：14

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