A nonconvex ADMM for a class of sparse inverse semidefinite quadratic programming problems

被引：6
作者：
Lu, Yue [1 ]
Huang, Ming [2 ]
Zhang, Yi [3 ]
Gu, Jian [4 ]
机构：
[1] Tianjin Normal Univ, Sch Math Sci, Tianjin, Peoples R China
[2] Dalian Maritime Univ, Dept Math, Dalian, Peoples R China
[3] East China Univ Sci & Technol, Sch Sci, Dept Math, Shanghai, Peoples R China
[4] Dalian Ocean Univ, Sch Sci, Dalian, Peoples R China
来源：
OPTIMIZATION | 2019年 / 68卷 / 06期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Sparse inverse semidefinite quadratic programming problems; alternating direction method of multiplier; Kurdyka-Lojasiewicz inequality; iteration-complexity; ALTERNATING DIRECTION METHOD; COMBINATORIAL OPTIMIZATION; DESCENT METHODS; CONVERGENCE; MINIMIZATION; ALGORITHMS;
D O I：
10.1080/02331934.2019.1576663
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 consider a class of sparse inverse semidefinite quadratic programming problems, in which a nonconvex alternating direction method of multiplier is investigated. Under mild conditions, we establish convergence results of our algorithm and the corresponding non-ergodic iteration-complexity is also considered under the assumption that the potential function satisfies the famous Kurdyka-Lojasiewicz property. Numerical results show that our algorithm is suitable to solve the given sparse inverse semidefinite quadratic programming problems.
引用
收藏
页码：1075 / 1105
页数：31
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