Work

Constructing Stochastic Matrices for Weighted Averaging in Gossip Networks

Year: 2025

Type: article

Abstract: The convergence of the gossip process has been extensively studied; however, algorithms that generate a set of stochastic matrices, the infinite product of which converges to a rank-one matrix determined by a given weight vector, have been less explored. In this work, we propose an algorithm for constructing (local) stochastic matrices based on a given gossip network topology and a set of weights for averaging across different consensus clusters, ensuring that the gossip process converges to a finite limit set.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.07.049

Authors: Erkan Bayram, Mohamed-Ali Belabbas

Institution: University of Illinois Urbana-Champaign

Language: English

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Air Force Office of Scientific Research

Awards: Collaborative Research: CIF: Medium: Analysis and Geometry of Neural Dynamical Systems, G8066606347, G8179710648