Stochastic Bigger Subspace Algorithms for Nonconvex Stochastic Optimization

It is well known that the stochastic optimization problem can be regarded as one of the most hard problems since, in most of the cases, the values of f and its gradient are often not easily to be solved, or the F(∙, ξ) is normally not given clearly and (or) the distribution function P is equivocal....

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Vydané v:	IEEE access Ročník 9; s. 1
Hlavní autori:	Yuan, Gonglin, Zhou, Yingjie, Wang, Liping, Yang, Qingyuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation algorithms Complexity Complexity analysis Complexity theory Convergence Convergence property Distribution functions Machine learning Machine learning algorithms Nonconvex function Optimization Quasi Newton methods Random variables Stochastic processes Stochastic subspace algorithm
ISSN:	2169-3536, 2169-3536
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