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
相关论文
共 50 条
  • [1] A Customized ADMM Approach for Large-Scale Nonconvex Semidefinite Programming
    Sun, Chuangchuang
    MATHEMATICS, 2023, 11 (21)
  • [2] A Method of Inertial Regularized ADMM for Separable Nonconvex Optimization Problems
    Chao, Miantao
    Geng, Yueqi
    Zhao, Yongxin
    SOFT COMPUTING, 2023, 27 (22) : 16741 - 16757
  • [3] AN ALTERNATING DIRECTION METHOD FOR SOLVING A CLASS OF INVERSE SEMI-DEFINITE QUADRATIC PROGRAMMING PROBLEMS
    Lu, Yue
    Ge, Ying-En
    Zhang, Li-Wei
    JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, 2016, 12 (01) : 317 - 336
  • [4] An Augmented Lagrangian Method for a Class of Inverse Quadratic Programming Problems
    Zhang, Jianzhong
    Zhang, Liwei
    APPLIED MATHEMATICS AND OPTIMIZATION, 2010, 61 (01) : 57 - 83
  • [5] Preconditioned ADMM for a Class of Bilinear Programming Problems
    Liang, Xiaobo
    Bai, Jianchao
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2018, 2018
  • [6] Inverse semidefinite quadratic programming problem with l1 norm measure
    Li, Lidan
    Zhang, Liwei
    Zhang, Hongwei
    JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2020, 376
  • [7] A Three-Operator Splitting Perspective of a Three-Block ADMM for Convex Quadratic Semidefinite Programming and Beyond
    Chen, Liang
    Chang, Xiaokai
    Liu, Sanyang
    ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH, 2020, 37 (04)
  • [8] A Modified Approach to Distributed Bregman ADMM for a Class of Nonconvex Consensus Problems
    Xue, Zhonghui
    Ma, Qianfeng
    Dang, Yazheng
    JOURNAL OF MATHEMATICS, 2025, 2025 (01)
  • [9] SparsePOP - A sparse semidefinite programming relaxation of polynomial optimization problems
    Waki, Hayato
    Kim, Sunyoung
    Kojima, Masakazu
    Muramatsu, Masakazu
    Sugimoto, Hiroshi
    ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE, 2008, 35 (02): : 1 - 13
  • [10] CONVERGENCE OF ADMM FOR OPTIMIZATION PROBLEMS WITH NONSEPARABLE NONCONVEX OBJECTIVE AND LINEAR CONSTRAINTS
    Guo, Ke
    Han, Deren
    Wu, Tingting
    PACIFIC JOURNAL OF OPTIMIZATION, 2018, 14 (03): : 489 - 506
12345