A nonconvex ADMM for a class of sparse inverse semidefinite quadratic programming problems. (3rd June 2019)

• Record Type: Journal Article
• Authors: Lu, Yue; Huang, Ming; Zhang, Yi; Gu, Jian
• Is Part Of: Optimization. Volume 68:Number 6(2019)
• Page Start: 1075
• Page End: 1105
• Publication Date: 2019-06-03
• Subjects: Sparse inverse semidefinite quadratic programming problems -- alternating direction method of multiplier -- Kurdyka–Łojasiewicz inequality -- iteration-complexity; 90C26 -- 65K05; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2019.1576663
• Languages: English
• ISSNs: 0233-1934

Abstract

ABSTRACT: 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–Łojasiewicz property. Numerical results show that our algorithm is suitable to solve the given sparse inverse semidefinite quadratic programming problems.