Subgradient Method for Nonconvex Nonsmooth Optimization

被引：29

作者：

Bagirov, A. M. ^{[1
]}

Jin, L. ^{[1
]}

Karmitsa, N. ^{[2
]}

Al Nuaimat, A. ^{[1
]}

Sultanova, N. ^{[1
]}

机构：

[1] Univ Ballarat, Sch Sci Informat Technol & Engn, Ballarat, Vic 3353, Australia

[2] Univ Turku, Dept Math, Turku 20014, Finland

来源：

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2013年 / 157卷 / 02期

关键词：

Nonsmooth optimization; Nonconvex optimization; Subgradient method; Bundle method; MEMORY BUNDLE METHOD; MINIMIZATION;

D O I：

10.1007/s10957-012-0167-6

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：

In this paper, we introduce a new method for solving nonconvex nonsmooth optimization problems. It uses quasisecants, which are subgradients computed in some neighborhood of a point. The proposed method contains simple procedures for finding descent directions and for solving line search subproblems. The convergence of the method is studied and preliminary results of numerical experiments are presented. The comparison of the proposed method with the subgradient and the proximal bundle methods is demonstrated using results of numerical experiments.

引用

页码：416 / 435

页数：20

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