Convergence rates of consensus algorithms in stochastic networks

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Convergence rates of consensus algorithms in stochastic networks
Abstract-- We study the convergence rate of average consensus algorithms in networks with stochastic communication failures. We show how the system dynamics can be modeled by a discrete-time linear system with multiplicative random coefficients. This formulation captures many types of random networks including networks with links failures, node failures, and network partitions. With this formulation, we use firstorder spectral perturbation analysis to analyze the meansquare convergence rate under various network conditions. Our analysis reveals that in large networks, the effect of communication failures on the convergence rate is similar to the effect of changing the weight assigned to the communication links. We also show that in large networks, when the probability of communication failure is small, correlation in communication failures plays a negligible role in the convergence rate of the algorithm.
Stacy Patterson, Bassam Bamieh
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where CDC
Authors Stacy Patterson, Bassam Bamieh
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