Weight Optimization for Consensus Algorithms with Correlated Switching Topology

TL;DR

This paper develops a convex weight optimization framework for consensus algorithms operating over networks with correlated random topologies, improving convergence performance through explicit rate expressions and global optimization.

Contribution

It introduces a convex optimization approach for weight design in consensus algorithms with correlated random topologies, including symmetric and asymmetric cases, and demonstrates significant performance gains.

Findings

Explicit MSE convergence rate as a function of link probabilities and correlations.

Convex, nonsmooth optimization of weights for symmetric networks.

Performance improvements shown through simulations.

Abstract

We design the weights in consensus algorithms with spatially correlated random topologies. These arise with: 1) networks with spatially correlated random link failures and 2) networks with randomized averaging protocols. We show that the weight optimization problem is convex for both symmetric and asymmetric random graphs. With symmetric random networks, we choose the consensus mean squared error (MSE) convergence rate as optimization criterion and explicitly express this rate as a function of the link formation probabilities, the link formation spatial correlations, and the consensus weights. We prove that the MSE convergence rate is a convex, nonsmooth function of the weights, enabling global optimization of the weights for arbitrary link formation probabilities and link correlation structures. We extend our results to the case of asymmetric random links. We adopt as optimization criterion the mean squared deviation (MSdev) of the nodes states from the current average state. We prove that MSdev is a convex function of the weights. Simulations show that significant performance gain is achieved with our weight design method when compared with methods available in the literature.