A symmetric version of the generalized alternating direction method of multipliers for two-block separable convex programming

• Main Authors: Jing Liu, Yongrui Duan, Min Sun
• Format: Article
• Language: English
• Published: SpringerOpen 2017-06-01
• Series: Journal of Inequalities and Applications
• Subjects: alternating direction method of multipliers; convex programming; mixed variational inequalities; compressed sensing
• Online Access: http://link.springer.com/article/10.1186/s13660-017-1405-0

Abstract This paper introduces a symmetric version of the generalized alternating direction method of multipliers for two-block separable convex programming with linear equality constraints, which inherits the superiorities of the classical alternating direction method of multipliers (ADMM), and which extends the feasible set of the relaxation factor α of the generalized ADMM to the infinite interval [ 1 , + ∞ ) $[1,+\infty)$ . Under the conditions that the objective function is convex and the solution set is nonempty, we establish the convergence results of the proposed method, including the global convergence, the worst-case O ( 1 / k ) $\mathcal{O}(1/k)$ convergence rate in both the ergodic and the non-ergodic senses, where k denotes the iteration counter. Numerical experiments to decode a sparse signal arising in compressed sensing are included to illustrate the efficiency of the new method.