Asynchronous Vector Consensus over Matrix-Weighted Networks

TL;DR

This paper investigates asynchronous and synchronous vector consensus in multi-agent networks with matrix weights, providing convergence conditions for positive definite, negative definite, and mixed edge weights, including bipartite consensus.

Contribution

It introduces a novel asynchronous update model for matrix-weighted networks and establishes necessary and sufficient conditions for various consensus types, extending matrix convergence theory.

Findings

Agents converge to a common state with positive definite weights.

Bipartite consensus is achieved under specific negative definite conditions.

Convergence results apply to both asynchronous and synchronous updates.

Abstract

We study the distributed consensus of state vectors in a discrete-time multi-agent network with matrix edge weights using stochastic matrix convergence theory. We present a distributed asynchronous time update model wherein one randomly selected agent updates its state vector at a time by interacting with its neighbors. We prove that all agents converge to same state vector almost surely when every edge weight matrix is positive definite. We study vector consensus in cooperative-competitive networks with edge weights being either positive or negative definite matrices and present a necessary and sufficient condition to achieve bipartite vector consensus in such networks. We study the network structures on which agents achieve zero consensus. We also present a convergence result on nonhomogenous matrix products which is of independent interest in matrix convergence theory. All the results hold true for the synchronous time update model as well in which all agents update their states simultaneously.