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arXiv · 1706.03807

Accelerated Consensus via Min-Sum Splitting

Patrick Rebeschini·Sekhar Tatikonda
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Abstract

We apply the Min-Sum message-passing protocol to solve the consensus problem in distributed optimization. We show that while the ordinary Min-Sum algorithm does not converge, a modified version of it known as Splitting yields convergence to the problem solution. We prove that a proper choice of the tuning parameters allows Min-Sum Splitting to yield subdiffusive accelerated convergence rates, matching the rates obtained by shift-register methods. The acceleration scheme embodied by Min-Sum Splitting for the consensus problem bears similarities with lifted Markov chains techniques and with multi-step first order methods in convex optimization.

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Patrick Rebeschini, Sekhar Tatikonda. 2017-06-12. Accelerated Consensus via Min-Sum Splitting. https://arxiv.org/abs/1706.03807

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