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arXiv · 1502.06259

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

Alexander Gasnikov·Pavel Dvurechensky·Dmitry Kamzolov
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Abstract

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.

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Alexander Gasnikov, Pavel Dvurechensky, Dmitry Kamzolov. 2015-02-22. Gradient and gradient-free methods for stochastic convex optimization with inexact oracle. https://arxiv.org/abs/1502.06259

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