{
  "id": "1206.4897",
  "version": "v1",
  "published": "2012-06-21T14:32:16.000Z",
  "updated": "2012-06-21T14:32:16.000Z",
  "title": "Robust Eigenvector of a Stochastic Matrix with Application to PageRank",
  "authors": [
    "Anatoli Juditsky",
    "Boris Polyak"
  ],
  "categories": [
    "math.OC"
  ],
  "abstract": "We discuss a definition of robust dominant eigenvector of a family of stochastic matrices. Our focus is on application to ranking problems, where the proposed approach can be seen as a robust alternative to the standard PageRank technique. The robust eigenvector computation is reduced to a convex optimization problem. We also propose a simple algorithm for robust eigenvector approximation which can be viewed as a regularized power method with a special stopping rule.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2012-06-21T14:32:16.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "stochastic matrix",
      "application",
      "robust eigenvector approximation",
      "convex optimization problem",
      "robust dominant eigenvector"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2012arXiv1206.4897J"
    }
  }
}