Inexact alternating direction methods of multipliers for separable convex optimization

Document Type

Article

Publication Date

5-15-2019

Abstract

Inexact alternating direction multiplier methods (ADMMs) are developed for solving general separable convex optimization problems with a linear constraint and with an objective that is the sum of smooth and nonsmooth terms. The approach involves linearized subproblems, a back substitution step, and either gradient or accelerated gradient techniques. Global convergence is established. The methods are particularly useful when the ADMM subproblems do not have closed form solution or when the solution of the subproblems is expensive. Numerical experiments based on image reconstruction problems show the effectiveness of the proposed methods.

Publication Source (Journal or Book title)

Computational Optimization and Applications

First Page

201

Last Page

235

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