A Proximal Diffusion Strategy for Multi-Agent Optimization with Sparse Affine Constraints

TL;DR

This paper introduces a decentralized proximal primal-dual algorithm for multi-agent optimization with sparse affine constraints, improving convergence and efficiency by exploiting the problem's structure.

Contribution

It develops a novel decentralized solution that leverages the sparsity of constraints, achieving linear convergence in smooth cases and handling non-smooth terms.

Findings

Algorithm converges asymptotically under constant step-size.

Performance improves with increased sparsity of constraints.

Simulations demonstrate superior performance over traditional methods.

Abstract

This work develops a proximal primal-dual decentralized strategy for multi-agent optimization problems that involve multiple coupled affine constraints, where each constraint may involve only a subset of the agents. The constraints are generally sparse, meaning that only a small subset of the agents are involved in them. This scenario arises in many applications including decentralized control formulations, resource allocation problems, and smart grids. Traditional decentralized solutions tend to ignore the structure of the constraints and lead to degraded performance. We instead develop a decentralized solution that exploits the sparsity structure. Under constant step-size learning, the asymptotic convergence of the proposed algorithm is established in the presence of non-smooth terms, and it occurs at a linear rate in the smooth case. We also examine how the performance of the algorithm is influenced by the sparsity of the constraints. Simulations illustrate the superior performance of the proposed strategy.