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Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks
[article]
2020
arXiv
pre-print
This paper proposes a novel family of primal-dual-based distributed algorithms for smooth, convex, multi-agent optimization over networks that uses only gradient information and gossip communications. The algorithms can also employ acceleration on the computation and communications. We provide a unified analysis of their convergence rate, measured in terms of the Bregman distance associated to the saddle point reformation of the distributed optimization problem. When acceleration is employed,
arXiv:1910.10666v2
fatcat:sqnyrrvybzbz3nrxwjbpwiabwm