A Distributed, Asynchronous and Incremental Algorithm for Nonconvex Optimization: An ADMM Based Approach

TL;DR

This paper introduces a novel asynchronous ADMM algorithm for nonconvex, nonsmooth optimization that operates in a distributed, incremental manner, tolerating high asynchrony levels and handling nonconvexity.

Contribution

It presents the first ADMM algorithm capable of managing both nonconvexity and high asynchrony simultaneously in a distributed setting.

Findings

Converges to stationary solutions under bounded asynchrony.

Handles heterogeneity and unreliable communication links.

Tolerates higher asynchrony than existing ADMM variants.

Abstract

The alternating direction method of multipliers (ADMM) has been popular for solving many signal processing problems, convex or nonconvex. In this paper, we study an asynchronous implementation of the ADMM for solving a nonconvex nonsmooth optimization problem, whose objective is the sum of a number of component functions. The proposed algorithm allows the problem to be solved in a distributed, asynchronous and incremental manner. First, the component functions can be distributed to different computing nodes, who perform the updates asynchronously without coordinating with each other. Two sources of asynchrony are covered by our algorithm: one is caused by the heterogeneity of the computational nodes, and the other arises from unreliable communication links. Second, the algorithm can be viewed as implementing an incremental algorithm where at each step the (possibly delayed) gradients of only a subset of component functions are update d. We show that when certain bounds are put on the level of asynchrony, the proposed algorithm converges to the set of stationary solutions (resp. optimal solutions) for the nonconvex (resp. convex) problem. To the best of our knowledge, the proposed ADMM implementation can tolerate the highest degree of asynchrony, among all known asynchronous variants of the ADMM. Moreover, it is the first ADMM implementation that can deal with nonconvexity and asynchrony at the same time.