Distributed average consensus with least-mean-square deviation

TLDR: The problem of finding the (symmetric) edge weights that result in the least mean-square deviation in steady state is considered and it is shown that this problem can be cast as a convex optimization problem, so the global solution can be found efficiently.

About: This article is published in Journal of Parallel and Distributed Computing.The article was published on 2007-01-01 and is currently open access. It has received 1166 citations till now. The article focuses on the topics: Convex optimization.

Figures

Fig. 4. LMSC and FDLA edge weight for rings with n varying from 3 to 100.

Fig. 7. Average mean-square deviation ss/n (left) and convergence time (right) of stars with n nodes.

Fig. 5. Average mean-square deviation ss/n2 (left) and convergence time (right) of n× n grids.

Fig. 6. Average mean-square deviation ss/n2 (left) and convergence time (right) of n× n meshes.

Fig. 3. Average mean-square deviation ss/n (left) and convergence time (right) for rings with n nodes.

Fig. 1. Average mean-square deviation ss/n (left) and convergence time (right) for paths with n nodes.

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Distributed average consensus with least-mean-square deviation" ?

The authors consider a stochastic model for distributed average consensus, which arises in applications such as load balancing for parallel processors, distributed coordination of mobile autonomous agents, and network synchronization. The authors consider the problem of finding the ( symmetric ) edge weights that result in the least mean-square deviation in steady state. The authors show that this problem can be cast as a convex optimization problem, so the global solution can be found efficiently. The authors describe some computational methods for solving this problem, and compare the weights and the mean-square deviations obtained by this method and several other weight design methods.