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Gradient-free proximal methods with inexact oracle for convex stochastic nonsmooth optimization problems on the simplex

被引：14
|
作者：
Gasnikov, A. V. [1 ,2 ]
Lagunovskaya, A. A. [1 ,3 ]
Usmanova, I. N. [1 ,2 ]
Fedorenko, F. A. [1 ]
机构：
[1] State Univ, Moscow Inst Phys & Technol, Moscow, Russia
[2] Russian Acad Sci, Inst Informat Transmiss Problems, Kharkevich Inst, Moscow, Russia
[3] Russian Acad Sci, Keldysh Inst Appl Math, Moscow, Russia
来源：
AUTOMATION AND REMOTE CONTROL | 2016年 / 77卷 / 11期
基金：
俄罗斯基础研究基金会; 俄罗斯科学基金会;
关键词：
Stochastic systems;
D O I：
10.1134/S0005117916110114
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：2018 / 2034
页数：17
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