Improving convergence rate of distributed consensus through asymmetric weights

We propose a weight design method to increase the convergence rate of distributed consensus. Prior works have 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. A Sturm-Liouville operator is used to approximate the graph Laplacian of more general graphs. Based on this continuum approximation, we propose a weight design method. 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.