DTAC-ADMM: Delay-Tolerant Augmented Consensus ADMM-based Algorithm for Distributed Resource Allocation

TL;DR

This paper introduces DTAC-ADMM, a distributed resource allocation algorithm that effectively handles communication delays in multi-agent networks, ensuring convergence for convex cost functions without requiring derivatives.

Contribution

The paper presents a novel delay-tolerant ADMM-based algorithm for distributed resource allocation that accommodates heterogeneous communication delays and general convex cost models.

Findings

Algorithm converges under network delays.

Works with non-differentiable convex costs.

Effective in multi-agent resource sharing scenarios.

Abstract

Latency is inherent in almost all real-world networked applications. In this paper, we propose a distributed allocation strategy over multi-agent networks with delayed communications. The state of each agent (or node) represents its share of assigned resources out of a fixed amount (equal to overall demand). Every node locally updates its state toward optimizing a global allocation cost function via received information of its neighbouring nodes even when the data exchange over the network is heterogeneously delayed at different links. The update is based on the alternating direction method of multipliers (ADMM) formulation subject to both sum-preserving coupling-constraint and local box-constraints. The solution is derivative-free and holds for general (not necessarily differentiable) convex cost models. We use the notion of augmented consensus over undirected networks to model delayed information exchange and for convergence analysis. We simulate our \textit{delay-tolerant} algorithm for