A Partition-Based Implementation of the Relaxed ADMM for Distributed Convex Optimization over Lossy Networks

TL;DR

This paper introduces a distributed, partition-based R-ADMM algorithm for convex optimization over lossy networks, demonstrating robustness and effectiveness through theoretical analysis and numerical simulations.

Contribution

It presents a novel partition-based R-ADMM implementation linked to R-PRS, robust against random packet losses, applicable to various distributed convex optimization problems.

Findings

Algorithm is provably robust to random packet losses.

Linked to the relaxed Peaceman-Rachford Splitting operator.

Effective in simulations over lossy random geometric graphs.

Abstract

In this paper we propose a distributed implementation of the relaxed Alternating Direction Method of Multipliers algorithm (R-ADMM) for optimization of a separable convex cost function, whose terms are stored by a set of interacting agents, one for each agent. Specifically the local cost stored by each node is in general a function of both the state of the node and the states of its neighbors, a framework that we refer to as `partition-based' optimization. This framework presents a great flexibility and can be adapted to a large number of different applications. We show that the partition-based R-ADMM algorithm we introduce is linked to the relaxed Peaceman-Rachford Splitting (R-PRS) operator which, historically, has been introduced in the literature to find the zeros of sum of functions. Interestingly, making use of non expansive operator theory, the proposed algorithm is shown to be provably robust against random packet losses that might occur in the communication between neighboring nodes. Finally, the effectiveness of the proposed algorithm is confirmed by a set of compelling numerical simulations run over random geometric graphs subject to i.i.d. random packet losses.