{
  "id": "1502.06259",
  "version": "v1",
  "published": "2015-02-22T18:47:03.000Z",
  "updated": "2015-02-22T18:47:03.000Z",
  "title": "Gradient and gradient-free methods for stochastic convex optimization with inexact oracle",
  "authors": [
    "Alexander Gasnikov",
    "Pavel Dvurechensky",
    "Kamzolov Dmitry"
  ],
  "comment": "in Russian, 9 pages",
  "categories": [
    "math.OC"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2015-02-22T18:47:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "stochastic convex optimization",
      "gradient-free method",
      "inexact oracle",
      "generalize universal gradient method",
      "random direction search"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 9,
      "language": "ru",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2015arXiv150206259G"
    }
  }
}