A regularized alternating direction method of multipliers for a class of nonconvex problems

被引：5
|
作者：
Jian, Jin Bao [1 ,2 ]
Zhang, Ye [1 ]
Chao, Mian Tao [1 ]
机构：
[1] Guangxi Univ, Coll Math & Informat Sci, Nanning, Peoples R China
[2] Guangxi Univ Nationalities, Coll Sci, Nanning, Peoples R China
来源：
JOURNAL OF INEQUALITIES AND APPLICATIONS | 2019年 / 2019卷 / 1期
基金：
中国国家自然科学基金;
关键词：
Nonconvex optimization problems; Alternating direction method of multipliers; Kurdyka-ojasiewicz property; Convergence; ITERATIVE ALGORITHMS; CONVERGENCE;
D O I：
10.1186/s13660-019-2145-0
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we propose a regularized alternating direction method of multipliers (RADMM) for a class of nonconvex optimization problems. The algorithm does not require the regular term to be strictly convex. Firstly, we prove the global convergence of the algorithm. Secondly, under the condition that the augmented Lagrangian function satisfies the Kurdyka-ojasiewicz property, the strong convergence of the algorithm is established. Finally, some preliminary numerical results are reported to support the efficiency of the proposed algorithm.
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收藏
页数：16
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