Distributed Average Consensus with Time-Varying Metropolis Weights ?
Lin Xiao, Stephen Boyd, Sanjay Lall · 2006
Given a network of processes where each node has an initial scalar value, we consider the problem of computing their average asymptotically using a distributed, linear iterative algorithm. At each iteration, each node replaces its own value with a weighted average of its previous value and the values of its neighbors. We introduce the Metropolis weights, a simple choice for the averaging weights used in each step. We show that with these weights, the values at every node converge to the average, provided the infinitely occurring communication graphs are jointly connected.