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An introduction to decentralized stochastic optimization with gradient tracking
[article]
2019
arXiv
pre-print
Decentralized solutions to finite-sum minimization are of significant importance in many signal processing, control, and machine learning applications. In such settings, the data is distributed over a network of arbitrarily-connected nodes and raw data sharing is prohibitive often due to communication or privacy constraints. In this article, we review decentralized stochastic first-order optimization methods and illustrate some recent improvements based on gradient tracking and variance
arXiv:1907.09648v2
fatcat:rizav6qrm5fh3ip63b6lg3tbse