A QUASI-NEWTON BUNDLE METHOD BASED ON APPROXIMATE SUBGRADIENTS

被引：0

作者：

Shen Jie ^{[1
,2
]}

Pang Li-Ping ^{[1
]}

机构：

[1] Dalian Univ Technol, Dept Appl Math, CORA, Dalian 116024, Peoples R China

[2] Liaoning Normal Univ, Sch Math, Dalian 116029, Peoples R China

来源：

JOURNAL OF APPLIED MATHEMATICS AND COMPUTING | 2007年 / 23卷 / 1-2期

基金：

美国国家科学基金会;

关键词：

Nonsmooth convex optimization; Moreau-Yosida regularization; approximate subgradient; bundle method; quasi-Newton method;

D O I：

10.1007/BF02831983

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper we propose an implementable method for solving a nonsmooth convex optimization problem by combining Moreau-Yosida regularization, bundle and quasi-Newton ideas. The method we propose makes use of approximate subgradients of the objective function, which makes the method easier to implement. We also prove the convergence of the proposed method under some additional assumptions.

引用

页码：361 / 367

页数：7

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