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JOURNALS // Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia // Archive

Dokl. RAN. Math. Inf. Proc. Upr., 2020 Volume 492, Pages 85–88 (Mi danma78)

This article is cited in 1 paper

INFORMATICS

Accelerated gradient sliding for minimizing a sum of functions

D. M. Dvinskikha, S. S. Omelchenkob, A. V. Gasnikova, A. I. Turinc

a Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Moscow, Russian Federation
b Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow Region, Russian Federation
c National Research University "Higher School of Economics", Moscow, Russian Federation

Abstract: We propose a new way of justifying the accelerated gradient sliding of G. Lan, which allows one to extend the sliding technique to a combination of an accelerated gradient method with an accelerated variance reduction method. New optimal estimates for the solution of the problem of minimizing a sum of smooth strongly convex functions with a smooth regularizer are obtained.

Keywords: accelerated gradient sliding of G. Lan, accelerated variance reduction methods, smooth strongly convex functions.

UDC: 519.853.62

Presented: Yu. G. Evtushenko
Received: 20.03.2020
Revised: 26.03.2020
Accepted: 03.04.2020

DOI: 10.31857/S268695432003008X


 English version:
Doklady Mathematics, 2020, 101:3, 244–246

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© Steklov Math. Inst. of RAS, 2025