Distributed averaging via lifted Markov chains

Abstract--Motivated by applications of distributed linear estimation, distributed control, and distributed optimization, we consider the question of designing linear iterative algorithms for computing the average of numbers in a network. Specifically, our interest is in designing such an algorithm with the fastest rate of convergence given the topological constraints of the network. As the main result of this paper, we design an algorithm with the fastest possible rate of convergence using a nonreversible Markov chain on the given network graph. We construct such a Markov chain by transforming the standard Markov chain, which is obtained using the Metropolis

• Type: Journal
• Year: 2010
• Where: TIT
• Authors: Kyomin Jung, Devavrat Shah, Jinwoo Shin
• Keywords: Algorithm, Education, Markov Chain, TIT 2010, Topological Constraints