An inexact ADMM with proximal-indefinite term and larger stepsize

Abstract

In this paper, an inexact Alternating Direction Method of Multipliers (ADMM) has been proposed for solving the two-block separable convex optimization problem subject to linear equality constraints. The first resulting subproblem is solved inexactly under relative error criterion, while another subproblem called regularization problem is solved inexactly by introducing an indefinite proximal term. Meanwhile, the dual variable is updated twice with relatively larger stepsizes since an indefinite proximal term is exploited. By reformulating the first-order optimality conditions of the involved subproblems as a monotone inclusion problem, the sublinear convergence rate of the proposed algorithm is established in the pointwise and ergodic sense. Numerical experiments on testing two sparse optimization problems from statistical learning and image restoration indicate that our inexact ADMM performs much better than several well-established algorithms.