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Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 2020, Volume 60, Number 7, Pages 1143–1150
DOI: https://doi.org/10.31857/S004446692007008X (Mi zvmmf11101) 		 
This article is cited in 3 scientific papers (total in 3 papers)

A heuristic adaptive fast gradient method in stochastic optimization problems

A. V. Ogal'tsov, A. I. Turin

State University – Higher School of Economics, Moscow, 101000 Russia
Citations (3)
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DOI: https://doi.org/10.31857/S004446692007008X
Abstract: A fast adaptive heuristic stochastic gradient descent method is proposed. It is shown that this algorithm has a higher convergence rate in practical problems than currently popular optimization methods. Furthermore, a justification of this method is given, and difficulties that prevent obtaining optimal estimates for the proposed algorithm are described.
Key words: fast gradient descent, stochastic optimization, adaptive optimization.
Funding agency Grant number
Russian Foundation for Basic Research 19-31-90062 Аспиранты
The work by A. I. Tyurin was supported by the Russian Foundation for Basic Research, project no. 19-31-90062 Aspiranty.
Received: 07.10.2019
Revised: 12.11.2019
Accepted: 10.03.2020
English version:
Computational Mathematics and Mathematical Physics, 2020, Volume 60, Issue 7, Pages 1108–1115
DOI: https://doi.org/10.1134/S0965542520070088
Bibliographic databases:
Document Type: Article
UDC: 519.85
Language: Russian
Citation: A. V. Ogal'tsov, A. I. Turin, “A heuristic adaptive fast gradient method in stochastic optimization problems”, Zh. Vychisl. Mat. Mat. Fiz., 60:7 (2020), 1143–1150; Comput. Math. Math. Phys., 60:7 (2020), 1108–1115
Citation in format AMSBIB
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    • This publication is cited in the following 3 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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    		Журнал вычислительной математики и математической физики Computational Mathematics and Mathematical Physics
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