A customized proximal point algorithm for convex minimization with linear constraints

被引：57

作者：

He, Bingsheng ^{[1
,2
]}

Yuan, Xiaoming ^{[3
]}

Zhang, Wenxing ^{[4
]}

机构：

[1] Nanjing Univ, Int Ctr Management Sci & Engn, Nanjing 210093, Jiangsu, Peoples R China

[2] Nanjing Univ, Dept Math, Nanjing 210093, Jiangsu, Peoples R China

[3] Hong Kong Baptist Univ, Dept Math, Hong Kong, Hong Kong, Peoples R China

[4] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China

来源：

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2013年 / 56卷 / 03期

关键词：

Convex minimization; Proximal point algorithm; Resolvent operator; Augmented Lagrangian method; THRESHOLDING ALGORITHM; DECOMPOSITION;

D O I：

10.1007/s10589-013-9564-5

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：

This paper demonstrates a customized application of the classical proximal point algorithm (PPA) to the convex minimization problem with linear constraints. We show that if the proximal parameter in metric form is chosen appropriately, the application of PPA could be effective to exploit the simplicity of the objective function. The resulting subproblems could be easier than those of the augmented Lagrangian method (ALM), a benchmark method for the model under our consideration. The efficiency of the customized application of PPA is demonstrated by some image processing problems.

引用

页码：559 / 572

页数：14

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