Gradient-free proximal methods with inexact oracle for convex stochastic nonsmooth optimization problems on the simplex
- 作者: Gasnikov A.V.1,2, Lagunovskaya A.A.1,3, Usmanova I.N.1,2, Fedorenko F.A.1
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隶属关系:
- Moscow Institute of Physics and Technology (State University)
- Institute for Information Transmission Problems (Kharkevich Institute)
- Keldysh Institute of Applied Mathematics
- 期: 卷 77, 编号 11 (2016)
- 页面: 2018-2034
- 栏目: 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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详细
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.
作者简介
A. Gasnikov
Moscow Institute of Physics and Technology (State University); Institute for Information Transmission Problems (Kharkevich Institute)
编辑信件的主要联系方式.
Email: gasnikov@yandex.ru
俄罗斯联邦, Moscow; Moscow
A. Lagunovskaya
Moscow Institute of Physics and Technology (State University); Keldysh Institute of Applied Mathematics
Email: gasnikov@yandex.ru
俄罗斯联邦, Moscow; Moscow
I. Usmanova
Moscow Institute of Physics and Technology (State University); Institute for Information Transmission Problems (Kharkevich Institute)
Email: gasnikov@yandex.ru
俄罗斯联邦, Moscow; Moscow
F. Fedorenko
Moscow Institute of Physics and Technology (State University)
Email: gasnikov@yandex.ru
俄罗斯联邦, Moscow
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