Consensus Driven by the Geometric Mean

TL;DR

This paper introduces three new consensus protocols based on geometric mean averaging, demonstrating exponential convergence and connecting to applications like chemical kinetics and optimization, thus extending traditional average consensus models.

Contribution

It proposes and analyzes three novel geometric mean driven consensus protocols, establishing their convergence and linking them to practical network problems and variational principles.

Findings

All protocols achieve exponential convergence to consensus.

The entropic protocol converges to the weighted geometric mean.

Protocols are gradient flows of free energy in different metrics.

Abstract

Consensus networks are usually understood as arithmetic mean driven dynamical averaging systems. In applications, however, network dynamics often describe inherently non-arithmetic and non-linear consensus processes. In this paper, we propose and study three novel consensus protocols driven by geometric mean averaging: a polynomial, an entropic, and a scaling-invariant protocol, where terminology characterizes the particular non-linearity appearing in the respective differential protocol equation. We prove exponential convergence to consensus for positive initial conditions. For the novel protocols we highlight connections to applied network problems: The polynomial consensus system is structured like a system of chemical kinetics on a graph. The entropic consensus system converges to the weighted geometric mean of the initial condition, which is an immediate extension of the (weighted) average consensus problem. We find that all three protocols generate gradient flows of free energy on the simplex of constant mass distribution vectors albeit in different metrics. On this basis, we propose a novel variational characterization of the geometric mean as the solution of a non-linear constrained optimization problem involving free energy as cost function. We illustrate our findings in numerical simulations.