A Proximal Dual Consensus ADMM Method for Multi-Agent Constrained Optimization

TL;DR

This paper introduces a new distributed optimization method for multi-agent networks with convex constraints, combining proximal minimization with ADMM to improve efficiency and robustness in solving complex subproblems.

Contribution

It develops a proximal dual consensus ADMM approach that handles polyhedral constraints efficiently and includes a randomized version resilient to communication issues.

Findings

Lower computational time compared to existing methods

Achieves an $ ext{O}(1/k)$ convergence rate

Effective in networks with unreliable communication

Abstract

This paper studies efficient distributed optimization methods for multi-agent networks. Specifically, we consider a convex optimization problem with a globally coupled linear equality constraint and local polyhedra constraints, and develop distributed optimization methods based on the alternating direction method of multipliers (ADMM). The considered problem has many applications in machine learning and smart grid control problems. Due to the presence of the polyhedra constraints, agents in the existing methods have to deal with polyhedra constrained subproblems at each iteration. One of the key issues is that projection onto a polyhedra constraint is not trivial, which prohibits from closed-form solutions or the use of simple algorithms for solving these subproblems. In this paper, by judiciously integrating the proximal minimization method with ADMM, we propose a new distributed optimization method where the polyhedra constraints are handled softly as penalty terms in the subproblems. This makes the subproblems efficiently solvable and consequently reduces the overall computation time. Furthermore, we propose a randomized counterpart that is robust against randomly ON/OFF agents and imperfect communication links. We analytically show that both the proposed methods have a worst-case $\mathcal{O}(1/k)$ convergence rate, where $k$ is the iteration number. Numerical results show that the proposed methods offer considerably lower computation time than the existing distributed ADMM method.