Convergence of multi-block Bregman ADMM for nonconvex composite problems
Fenghui Wang, Wenfei Cao, Zongben Xu

TL;DR
This paper proves the convergence of a Bregman 3-block ADMM and its extension to N-blocks for nonconvex problems, supported by simulations and real-world applications, advancing nonconvex optimization methods.
Contribution
It introduces a Bregman modification of multi-block ADMM and establishes its convergence for a broad class of nonconvex functions, extending existing theory.
Findings
Convergence of 3-block Bregman ADMM for nonconvex functions.
Extension of convergence results to N-block ADMM ($N \\geq 3$).
Validation through simulations and real-world application.
Abstract
The alternating direction method with multipliers (ADMM) has been one of most powerful and successful methods for solving various composite problems. The convergence of the conventional ADMM (i.e., 2-block) for convex objective functions has been justified for a long time, and its convergence for nonconvex objective functions has, however, been established very recently. The multi-block ADMM, a natural extension of ADMM, is a widely used scheme and has also been found very useful in solving various nonconvex optimization problems. It is thus expected to establish convergence theory of the multi-block ADMM under nonconvex frameworks. In this paper we present a Bregman modification of 3-block ADMM and establish its convergence for a large family of nonconvex functions. We further extend the convergence results to the -block case (), which underlines the feasibility of…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Direction-of-Arrival Estimation Techniques · Advanced Adaptive Filtering Techniques
