Improving Convergence Rate of Distributed Consensus Through Asymmetric Weights

Abstract

We propose a weight design method to increase the convergence rate of distributed consensus. Prior work has focused on symmetric weight design due to computational tractability. We show that with proper choice of asymmetric weights, the convergence rate can be improved significantly over even the symmetric optimal design. In particular, we prove that the convergence rate in a lattice graph can be made independent of the size of the graph with asymmetric weights. We then use a Sturm-Liouville operator to approximate the graph Laplacian of more general graphs. A general weight design method is proposed based on this continuum approximation. Numerical computations show that the resulting convergence rate with asymmetric weight design is improved considerably over that with symmetric optimal weights and Metropolis-Hastings weights.