Convergence of implementable descent algorithms for unconstrained optimization

被引：9

作者：

Dussault, JP ^{[1
]}

机构：

[1] Univ Sherbrooke, Dept Math & Informat, Sherbrooke, PQ, Canada

来源：

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2000年 / 104卷 / 03期

关键词：

descent algorithms; global convergence;

D O I：

10.1023/A:1004602012151

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

Descent algorithms use sufficient descent directions combined with stepsize rules, such as the Armijo rule, to produce sequences of iterates whose cluster points satisfy some necessary optimality conditions. In this note, we present a proof that the whole sequence of iterates converges for quasiconvex objective functions.

引用

页码：739 / 745

页数：7

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