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arXiv · 1810.05067

Linearly Convergent Asynchronous Distributed ADMM via Markov Sampling

Suhail M. Shah·Konstantin E. Avrachenkov
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Abstract

We consider the consensual distributed optimization problem and propose an asynchronous version of the Alternating Direction Method of Multipliers (ADMM) algorithm to solve it. The `asynchronous' part here refers to the fact that only one node/processor is updated (i.e. performs a minimization step) at each iteration of the algorithm. The selection of the node to be updated is decided by simulating a Markov chain. The proposed algorithm is shown to have a linear convergence property in expectation for the class of functions which are strongly convex and continuously differentiable.

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Suhail M. Shah, Konstantin E. Avrachenkov. 2018-10-11. Linearly Convergent Asynchronous Distributed ADMM via Markov Sampling. https://arxiv.org/abs/1810.05067

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