Computable Convergence Rate Bound for Ratio Consensus Algorithms

The objective of the paper is to establish a computable upper bound for almost sure convergence rate class ratio consensus algorithms defined via column-stochastic matrices. Our result extends works Iutzeler et al. from 2013 on similar bounds that have been obtained in more restrictive setup with limited conclusions. present complements results by Gerencsér and 2022, identifying exact wide terms spectral gap, which is, however, not general. provided will be compared actual experimentally range modulated random geometric graphs local interactions.