Gradient and gradient-free methods for stochastic convex optimization with inexact oracle

Alexander Gasnikov, Pavel Dvurechensky, Kamzolov Dmitry

Published 2015-02-22Version 1

In the paper we generalize universal gradient method (Yu. Nesterov) to strongly convex case and to Intermediate gradient method (Devolder-Glineur-Nesterov). We also consider possible generalizations to stochastic and online context. We show how these results can be generalized to gradient-free method and method of random direction search. But the main ingridient of this paper is assumption about the oracle. We considered the oracle to be inexact.