Convergence of Distributed Stochastic Variance Reduced Methods Without Sampling Extra Data

Stochastic variance reduced methods have gained a lot of interest recently for empirical risk minimization due to its appealing run time complexity. When the data size is large and disjointly stored on different machines, it becomes imperative to distribute the implementation of such variance reduce...

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Vydané v:	IEEE transactions on signal processing Ročník 68; s. 3976 - 3989
Hlavní autori:	Cen, Shicong, Zhang, Huishuai, Chi, Yuejie, Chen, Wei, Liu, Tie-Yan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Convergence Distributed databases Distributed optimization Empirical analysis Homogeneity master/slave model Optimization Regularization Risk management Servers Signal processing algorithms Smoothness stochastic optimization Stochastic processes variance reduction
ISSN:	1053-587X, 1941-0476
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