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Matematicheskie Zametki, 2020, Volume 108, Issue 4, Pages 515–528
DOI: https://doi.org/10.4213/mzm12751 (Mi mzm12751) 		 
This article is cited in 7 scientific papers (total in 7 papers)

Accelerated and Unaccelerated Stochastic Gradient Descent in Model Generality

D. M. Dvinskikhabc, A. I. Turind, A. V. Gasnikovbcd, S. S. Omelchenkob

a Weierstrass Institute
b Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow Region
c Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Moscow
d National Research University "Higher School of Economics", Moscow
Full-text PDF (571 kB) Citations (7)
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DOI: https://doi.org/10.4213/mzm12751
Abstract: A new method for deriving estimates of the rate of convergence of optimal methods for solving problems of smooth (strongly) convex stochastic optimization is described. The method is based on the results of stochastic optimization derived from results on the convergence of optimal methods under the conditions of inexact gradients with small noises of nonrandom nature. In contrast to earlier results, all estimates in the present paper are obtained in model generality.
Keywords: stochastic optimization, accelerated gradient descent, model generality, composite optimization.
Funding agency Grant number
Russian Foundation for Basic Research 19-31-90062
18-31-20005 мол_а_вед
Ministry of Science and Higher Education of the Russian Federation 075-00337-20-03
The research of D. M. Dvinskikh in Sec. 1 was supported by the Ministry of Science and Higher Education of the Russian Federation under grant no. 075-00337-20-03, project no. 0714-2020-0005. The research of A. I. Tyurin in Sec. 3 was supported by the Russian Foundation for Basic Research under grant 19-31-90062. The research of A. V. Gasnikov in Sec. 2 was supported by the Russian Foundation for Basic Research under grant 18-31-20005 mol_a_ved.
Received: 11.04.2020
Revised: 20.05.2020
English version:
Mathematical Notes, 2020, Volume 108, Issue 4, Pages 511–522
DOI: https://doi.org/10.1134/S0001434620090230
Bibliographic databases:
Document Type: Article
UDC: 519.85
Language: Russian
Citation: D. M. Dvinskikh, A. I. Turin, A. V. Gasnikov, S. S. Omelchenko, “Accelerated and Unaccelerated Stochastic Gradient Descent in Model Generality”, Mat. Zametki, 108:4 (2020), 515–528; Math. Notes, 108:4 (2020), 511–522
Citation in format AMSBIB
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    • This publication is cited in the following 7 articles:
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