Convergence of random sleep algorithms for optimal consensus

Abstract

In this paper, we propose a random sleep algorithm for a network to cooperatively find a point within the intersection of some convex sets, each of which is known only to a particular node. At each step, each node first chooses to project its own set or not at random by a Bernoulli decision independently. When a node has chosen to project its set, we assume that it can detect only the projection direction rather than the exact projection point, based on which the node obtains an estimate for the projection point. Then the agents update their states by averaging the estimates with their neighbors. Under directed and time-varying communication graph, sufficient and/or necessary stepsize conditions are presented for the considered algorithm converging to a consensus within the intersection set.