Stochastic Bigger Subspace Algorithms for Nonconvex Stochastic Optimization
Abstract
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. Then an effective optimization algorithm is successfully designed and used to solve this problem that is an interesting work. This paper designs...
Paper Details
Title
Stochastic Bigger Subspace Algorithms for Nonconvex Stochastic Optimization
Published Date
Jan 1, 2021
Journal
Volume
9
Pages
119818 - 119829
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History