Online Distributed Optimization on Dynamic Networks

TL;DR

This paper introduces a distributed optimization algorithm for dynamic networks with uncertain costs and switching topologies, adapting link weights to improve cooperation among agents.

Contribution

It proposes a novel dual sub-gradient averaging algorithm that adjusts communication weights based on network reliability, with convergence analysis and simulation validation.

Findings

Algorithm effectively adapts to network changes

Convergence rate depends on network topology

Simulation results demonstrate improved cooperation

Abstract

This paper presents a distributed optimization scheme over a network of agents in the presence of cost uncertainties and over switching communication topologies. Inspired by recent advances in distributed convex optimization, we propose a distributed algorithm based on a dual sub-gradient averaging. The objective of this algorithm is to minimize a cost function cooperatively. Furthermore, the algorithm changes the weights on the communication links in the network to adapt to varying reliability of neighboring agents. A convergence rate analysis as a function of the underlying network topology is then presented, followed by simulation results for representative classes of sensor networks.