New Results on Stochastic Consensus Networks

We revisit the general linear consensus problem and provide new conditions for asymptotic consensus on two types of stochastic versions of the algorithm, in discrete and continuous time. We show that our method unifies a number of proposed models in the literature as well as it extends and generalizes existing results.