Gradient trust region algorithm with limited memory BFGS update for nonsmooth convex minimization

被引：25

作者：

Yuan, Gonglin ^{[1
]}

Wei, Zengxin ^{[1
]}

Wang, Zhongxing ^{[1
]}

机构：

[1] Guangxi Univ, Dept Math & Informat Sci, Nanning 530004, Guangxi, Peoples R China

来源：

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2013年 / 54卷 / 01期

关键词：

Nonsmooth convex minimization; Trust region; L-BFGS update; Global convergence; QUASI-NEWTON ALGORITHM; CONVERGENCE ANALYSIS; NONDIFFERENTIABLE OPTIMIZATION; COMPLEMENTARITY; EQUATIONS; VERSION;

D O I：

10.1007/s10589-012-9485-8

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：

By means of a gradient strategy, the Moreau-Yosida regularization, limited memory BFGS update, and proximal method, we propose a trust-region method for nonsmooth convex minimization. The search direction is the combination of the gradient direction and the trust-region direction. The global convergence of this method is established under suitable conditions. Numerical results show that this method is competitive to other two methods.

引用

页码：45 / 64

页数：20

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