{
  "id": "1909.05207",
  "version": "v1",
  "published": "2019-09-07T19:06:23.000Z",
  "updated": "2019-09-07T19:06:23.000Z",
  "title": "Introduction to Online Convex Optimization",
  "authors": [
    "Elad Hazan"
  ],
  "comment": "arXiv admin note: text overlap with arXiv:1909.03550",
  "categories": [
    "cs.LG",
    "math.OC",
    "stat.ML"
  ],
  "abstract": "This manuscript portrays optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay out a comprehensive theoretical model and use classical algorithmic theory and mathematical optimization. It is necessary as well as beneficial to take a robust approach, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed. This view of optimization as a process has become prominent in varied fields and has led to some spectacular success in modeling and systems that are now part of our daily lives.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-09-07T19:06:23.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "online convex optimization",
      "introduction",
      "manuscript portrays optimization",
      "optimization method",
      "robust approach"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}