A. V. Gasnikov, A. A. Lagunovskaya, I. N. Usmanova, F. A. Fedorenko, “Gradient-free proximal methods with inexact oracle for convex stochastic nonsmooth optimization problems on the simplex”, Avtomat. i Telemekh., 2016, Issue 10,Pages <nobr>57

This article is cited in 20 papers

Stochastic Systems, Queuing Systems

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

A. V. Gasnikov^ab, A. A. Lagunovskaya^ca, I. N. Usmanova^ab, F. A. Fedorenko^a

^a Moscow Institute of Physics and Technology (State University), Moscow, Russia
^b Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Moscow, Russia
^c Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow, Russia

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.

Presented by the member of Editorial Board: P. S. Shcherbakov

Received: 12.03.2015