Convergence of memory gradient methods

被引：1

作者：

Shi, Zhen-Jun ^{[1
,2
]}

Guo, Jinhua ^{[2
]}

机构：

[1] Qufu Normal Univ, Coll Operat Res & Management, Rizhao 276826, Shandong, Peoples R China

[2] Univ Michigan, Dept Comp & Informat Sci, Dearborn, MI 48128 USA

来源：

INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS | 2008年 / 85卷 / 07期

基金：

美国国家科学基金会;

关键词：

unconstrained optimization; memory gradient method; global convergence;

D O I：

10.1080/00207160701466370

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper we present a new class of memory gradient methods for unconstrained optimization problems and develop some useful global convergence properties under some mild conditions. In the new algorithms, trust region approach is used to guarantee the global convergence. Numerical results show that some memory gradient methods are stable and efficient in practical computation. In particular, some memory gradient methods can be reduced to the BB method in some special cases.

引用

页码：1039 / 1053

页数：15

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