An Accelerated Asynchronous Distributed Method for Convex Constrained Optimization Problems

TL;DR

This paper introduces an innovative asynchronous distributed primal-dual algorithm for multi-agent convex optimization with local constraints, achieving optimal convergence rates and enabling efficient consensus in networked systems.

Contribution

It presents the first asynchronous method with proven optimal convergence guarantees for multi-agent convex constrained problems.

Findings

Achieves an optimal convergence rate of O(1/K) for suboptimality, infeasibility, and consensus violation.

Handles local nonlinear convex constraints in a distributed asynchronous setting.

Provides theoretical guarantees for the proposed algorithm's performance.

Abstract

We consider a class of multi-agent cooperative consensus optimization problems with local nonlinear convex constraints where only those agents connected by an edge can directly communicate, hence, the optimal consensus decision lies in the intersection of these private sets. We develop an asynchronous distributed accelerated primal-dual algorithm to solve the considered problem. The proposed scheme is the first asynchronous method with an optimal convergence guarantee for this class of problems, to the best of our knowledge. In particular, we provide an optimal convergence rate of $\mathcal{O(1/K)}$ for suboptimality, infeasibility, and consensus violation.