A multi-step doubly stabilized bundle method for nonsmooth convex optimization

被引:3
|
作者
Tang, Chunming [1 ]
Liu, Shuai [2 ]
Jian, Jinbao [3 ]
Ou, Xiaomei [1 ]
机构
[1] Guangxi Univ, Coll Math & Informat Sci, Nanning 530004, Peoples R China
[2] South China Normal Univ, Sch Software, Nanhai Campus, Foshan 528225, Peoples R China
[3] Guangxi Univ Nationalities, Sch Math & Informat Sci, Nanning 530006, Peoples R China
基金
中国国家自然科学基金;
关键词
Nonsmooth optimization; Doubly stabilized bundle method; Multi-step scheme; Descent test criterion; Global convergence; DIRECTION METHOD; VERSION;
D O I
10.1016/j.amc.2020.125154
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, by incorporating a multi-step scheme into the doubly stabilized bundle method (DSBM) recently developed by Oliveira and Solodov (2016), we propose a multistep doubly stabilized bundle method (MDSBM) for solving nonsmooth convex optimization problems. In contrast to a single sequence generated by DSBM, the MDSBM generates three related iteration sequences. One is used to build the cutting-planes model of the objective function, another is served as the stability centers, and the third is the sequence of solutions to our new doubly stabilized subproblems. In addition, we present a new descent test criterion, aiming to take advantage of the multi-step scheme. We establish global convergence of the proposed method, and finally present some promising numerical results. (C) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页数:16
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