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This article is cited in 16 scientific papers (total in 16 papers)
Algorithms of robust stochastic optimization based on mirror descent method
A. V. Nazina, A. S. Nemirovskyb, A. B. Tsybakovc, A. B. Juditskyd a Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia
b Georgia Institute of Technology, Atlanta, USA
c CREST, ENSAE, Paris, France
d Université Grenoble Alpes, Grenoble, France
Abstract:
We propose an approach to the construction of robust non-Euclidean iterative algorithms by convex composite stochastic optimization based on truncation of stochastic gradients. For such algorithms, we establish sub-Gaussian confidence bounds under weak assumptions about the tails of the noise distribution in convex and strongly convex settings. Robust estimates of the accuracy of general stochastic algorithms are also proposed.
Keywords:
robust iterative algorithms, stochastic optimization algorithms, convex composite stochastic optimization, mirror descent method, robust confidence sets.
Received: 18.07.2018 Revised: 03.09.2018 Accepted: 08.11.2018
Citation:
A. V. Nazin, A. S. Nemirovsky, A. B. Tsybakov, A. B. Juditsky, “Algorithms of robust stochastic optimization based on mirror descent method”, Avtomat. i Telemekh., 2019, no. 9, 64–90; Autom. Remote Control, 80:9 (2019), 1607–1627
Linking options:
https://www.mathnet.ru/eng/at15342 https://www.mathnet.ru/eng/at/y2019/i9/p64
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Abstract page: | 296 | Full-text PDF : | 55 | References: | 39 | First page: | 16 |
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