Inexact indefinite proximal ADMMs for 2-block separable convex programs and applications to 4-block DNNSDPs

TL;DR

This paper introduces an inexact indefinite proximal ADMM with convergence guarantees for two-block convex problems, applicable to complex multi-block problems like DNNSDPs, showing competitive numerical performance.

Contribution

It proposes a novel inexact indefinite proximal ADMM with step-size tuning and convergence analysis, extending applicability to multi-block convex optimization problems.

Findings

The proposed method converges under mild conditions.

Numerical results show competitive performance with existing methods.

Applicable to DNNSDP problems with many constraints.

Abstract

This paper is concerned with two-block separable convex minimization problems with linear constraints, for which it is either impossible or too expensive to obtain the exact solutions of the subproblems involved in the proximal ADMM (alternating direction method of multipliers). Such structured convex minimization problems often arise from the two-block regroup settlement of three or four-block separable convex optimization problems with linear constraints, or from the constrained total-variation superresolution image reconstruction problems in image processing. For them, we propose an inexact indefinite proximal ADMM of step-size $\tau\in\!(0,\frac{\sqrt{5}+1}{2})$ with two easily implementable inexactness criteria to control the solution accuracy of subproblems, and establish the convergence under a mild assumption on indefinite proximal terms. We apply the proposed inexact indefinite proximal ADMMs to the three or four-block separable convex minimization problems with linear constraints, which are from the duality of the important class of doubly nonnegative semidefinite programming (DNNSDP) problems with many linear equality and/or inequality constraints. Numerical results indicate that the inexact indefinite proximal ADMM with the absolute error criterion has a comparable performance with the directly extended multi-block ADMM of step-size $\tau=1.618$ without convergence guarantee, whether in terms of the number of iterations or the computation time.