Global convergence of proximal iteratively reweighted algorithm

被引：16

作者：

Sun, Tao ^{[1
]}

Jiang, Hao ^{[2
]}

Cheng, Lizhi ^{[1
,3
]}

机构：

[1] Natl Univ Def Technol, Coll Sci, Changsha 410073, Hunan, Peoples R China

[2] Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China

[3] Natl Univ Def Technol, State Key Lab High Performance Computat, Changsha 410073, Hunan, Peoples R China

来源：

JOURNAL OF GLOBAL OPTIMIZATION | 2017年 / 68卷 / 04期

基金：

美国国家科学基金会;

关键词：

Proximal iteratively reweighted algorithm; Kurdyka-Lojasiewicz function; Convergence analysis; Parallel splitting; Alternating updating; NONCONVEX; OPTIMIZATION; MINIMIZATION;

D O I：

10.1007/s10898-017-0507-z

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 investigate the convergence of the proximal iteratively reweighted algorithm for a class of nonconvex and nonsmooth problems. Such problems actually include numerous models in the area of signal processing and machine learning research. Two extensions of the algorithm are also studied. We provide a unified scheme for these three algorithms. With the Kurdyka-Lojasiewicz property, we prove that the unified algorithm globally converges to a critical point of the objective function.

引用

页码：815 / 826

页数：12

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