Generalized symmetric ADMM for separable convex optimization

• Published in: Computational optimization and applications Vol. 70; no. 1; pp. 129 - 170
• Main Authors: Bai, Jianchao, Li, Jicheng, Xu, Fengmin, Zhang, Hongchao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2018; Springer Nature B.V
• Subjects: Convergence; Convex analysis; Convex and Discrete Geometry; Convexity; Data management; Economic models; Integers; Lagrange multiplier; Management Science; Mathematics; Mathematics and Statistics; Numerical methods; Operations Research; Operations Research/Decision Theory; Optimization; Statistics; Variables; Separable convex programming; Alternating direction method of multipliers; Global convergence; 68W40; Parameter convergence domain; Statistical learning; Multiple blocks; 90C06; 65E05; 65C60; Complexity

The alternating direction method of multipliers (ADMM) has been proved to be effective for solving separable convex optimization subject to linear constraints. In this paper, we propose a generalized symmetric ADMM (GS-ADMM), which updates the Lagrange multiplier twice with suitable stepsizes, to so...