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

2015/02/22 by Alexander Gasnikov, Gasnikov, Alexander, Pavel Dvurechensky +3
Computer Science · Engineering · Mathematics · #Advanced Optimization Algorithms Research #FOS: Mathematics #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #math.OC

paper · pdf · doi:10.48550/arxiv.1502.06259

9 pages, in Russian

openalex publication_date 2015/02/22 · arxiv created 2016/03/28 · arxiv updated 2016/03/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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