Geometric bounds for convergence rates of averaging algorithms - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Information and Computation Année : 2022

Geometric bounds for convergence rates of averaging algorithms

Résumé

We develop a generic method for bounding the convergence rate of an averaging algorithm running in a multi-agent system with a time-varying network, where the associated stochastic matrices have a time-independent Perron vector. This method provides bounds on convergence rates that unify and refine most of the previously known bounds. They depend on geomet-ric parameters of the dynamic communication graph such as the normalized diameter or the bottleneck measure. As corollaries of these geometric bounds, we show that the convergence rate of the Metropolis algorithm in a system of n agents is less than 1−1/4n2 with any communication graph that may vary in time, but is permanently connected and bidirectional. We prove a similar upper bound for the EqualNeighbor algorithm under the additional assumptions that the number of neighbors of each agent is constant and that the communication graph is not too irregular. Moreover our bounds offer improved convergence rates for several averaging algorithms and specific families of communication graphs.Finally we extend our methodology to a time-varying Perron vector and show how convergence times may dramatically degrade with even limited variations of Perron vectors.

Dates et versions

hal-03717768 , version 1 (08-07-2022)

Identifiants

Citer

Bernadette Charron-Bost. Geometric bounds for convergence rates of averaging algorithms. Information and Computation, 2022, 285, pp.104909. ⟨10.1016/j.ic.2022.104909⟩. ⟨hal-03717768⟩
30 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More