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This article is cited in 1 scientific paper (total 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.
Citation:
D. M. Dvinskikh, S. S. Omelchenko, A. V. Gasnikov, A. I. Turin, “Accelerated gradient sliding for minimizing a sum of functions”, Dokl. RAN. Math. Inf. Proc. Upr., 492 (2020), 85–88; Dokl. Math., 101:3 (2020), 244–246
Linking options:
https://www.mathnet.ru/eng/danma78 https://www.mathnet.ru/eng/danma/v492/p85
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