文献类型：期刊文献

英文题名：A nonconvex ADMM for a class of sparse inverse semidefinite quadratic programming problems

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

年份：2019

卷号：68

期号：6

起止页码：1075

外文期刊名：OPTIMIZATION

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000472116800001)】；

基金：The first author is supported by the National Natural Science Foundation of China [grant number 11601389] and the Doctoral Foundation of Tianjin Normal University [grant number 52XB1513]. The second author is supported by the National Natural Science Foundation of China [grant numbers 11626053 and 11701063], China Postdoctoral Science Foundation [grant number 2016M601296] and the Fundamental Research Funds for the Central Universities [grant numbers 3132016108 and 3132017052] and the Scientific Research Foundation Funds of DLMU [grant number 02501102]. The third author is supported by the National Natural Science Foundation of China [grant number 11401210].

语种：英文

外文关键词：Sparse inverse semidefinite quadratic programming problems; alternating direction method of multiplier; Kurdyka-Lojasiewicz inequality; iteration-complexity

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.