{
  "id": "1812.00726",
  "version": "v1",
  "published": "2018-12-03T13:33:14.000Z",
  "updated": "2018-12-03T13:33:14.000Z",
  "title": "Averaging Principle and Shape Theorem for a Growth Model with Memory",
  "authors": [
    "Amir Dembo",
    "Pablo Groisman",
    "Ruojun Huang",
    "Vladas Sidoravicius"
  ],
  "comment": "33 pages",
  "categories": [
    "math.PR"
  ],
  "abstract": "We present a general approach to study a class of random growth models in $n$-dimensional Euclidean space. These models are designed to capture basic growth features which are expected to manifest at the mesoscopic level for several classical self-interacting processes originally defined at the microscopic scale. It includes once-reinforced random walk with strong reinforcement, origin-excited random walk, and few others, for which the set of visited vertices is expected to form a \"limiting shape\". We prove an averaging principle that leads to such shape theorem. The limiting shape can be computed in terms of the invariant measure of an associated Markov chain.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-12-03T13:33:14.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "60K35",
      "60K37",
      "82C22",
      "82C24"
    ],
    "keywords": [
      "shape theorem",
      "averaging principle",
      "capture basic growth features",
      "random growth models",
      "self-interacting processes"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 33,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}