Email this article Gradient-free proximal methods with inexact oracle for convex stochastic nonsmooth optimization problems on the simplex
- Authors: Gasnikov A.V.1,2, Lagunovskaya A.A.1,3, Usmanova I.N.1,2, Fedorenko F.A.1
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Affiliations:
- Moscow Institute of Physics and Technology (State University)
- Institute for Information Transmission Problems (Kharkevich Institute)
- Keldysh Institute of Applied Mathematics
- Issue: Vol 77, No 11 (2016)
- Pages: 2018-2034
- Section: Stochastic Systems, Queueing Systems
- URL: https://journals.rcsi.science/0005-1179/article/view/150480
- DOI: https://doi.org/10.1134/S0005117916110114
- ID: 150480
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Abstract
In this paper we propose a modification of the mirror descent method for non-smooth stochastic convex optimization problems on the unit simplex. The optimization problems considered differ from the classical ones by availability of function values realizations. Our purpose is to derive the convergence rate of the method proposed and to determine the level of noise that does not significantly affect the convergence rate.
About the authors
A. V. Gasnikov
Moscow Institute of Physics and Technology (State University); Institute for Information Transmission Problems (Kharkevich Institute)
Author for correspondence.
Email: gasnikov@yandex.ru
Russian Federation, Moscow; Moscow
A. A. Lagunovskaya
Moscow Institute of Physics and Technology (State University); Keldysh Institute of Applied Mathematics
Email: gasnikov@yandex.ru
Russian Federation, Moscow; Moscow
I. N. Usmanova
Moscow Institute of Physics and Technology (State University); Institute for Information Transmission Problems (Kharkevich Institute)
Email: gasnikov@yandex.ru
Russian Federation, Moscow; Moscow
F. A. Fedorenko
Moscow Institute of Physics and Technology (State University)
Email: gasnikov@yandex.ru
Russian Federation, Moscow
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