Stochastic Bigger Subspace Algorithms for Nonconvex Stochastic Optimization

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 <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> and its gradient are often not easily t...

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Bibliographic Details
Main Authors: Gonglin Yuan, Yingjie Zhou, Liping Wang, Qingyuan Yang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Stochastic subspace algorithm
nonconvex function
convergence property
machine learning
complexity analysis
Online Access:https://ieeexplore.ieee.org/document/9524602/

Internet

https://ieeexplore.ieee.org/document/9524602/

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