A nonlinear conjugate gradient method with a strong global convergence property

被引：962

作者：

Dai, YH ^{[1
]}

Yuan, Y ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Computat Math & Sci Engn Comp, State Key Lab Sci & Engn Comp, Beijing 100080, Peoples R China

来源：

SIAM JOURNAL ON OPTIMIZATION | 1999年 / 10卷 / 01期

关键词：

unconstrained optimization; new conjugate gradient method; Wolfe conditions; global convergence;

D O I：

10.1137/S1052623497318992

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Conjugate gradient methods are widely used for unconstrained optimization, especially large scale problems. The strong Wolfe conditions are usually used in the analyses and implementations of conjugate gradient methods. This paper presents a new version of the conjugate gradient method, which converges globally, provided the line search satisfies the standard Wolfe conditions. The conditions on the objective function are also weak, being similar to those required by the Zoutendijk condition.

引用

页码：177 / 182

页数：6

共 14 条

[1] DESCENT PROPERTY AND GLOBAL CONVERGENCE OF THE FLETCHER REEVES METHOD WITH INEXACT LINE SEARCH [J].