A Hybrid Stochastic-Deterministic Minibatch Proximal Gradient Method for Efficient Optimization and Generalization

Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid stochastic-deterministic minibatch proximal gradie...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 44; číslo 10; s. 5933 - 5946
Hlavní autori:	Zhou, Pan, Yuan, Xiao-Tong, Lin, Zhouchen, Hoi, Steven C.H.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Catalysts Complexity Computational complexity Computational modeling Convex optimization Linear prediction online convex optimization Optimization precondition Prediction algorithms Signal processing algorithms Stochastic processes stochastic variance-reduced algorithm
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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