{
  "id": "1611.05659",
  "version": "v1",
  "published": "2016-11-17T12:44:13.000Z",
  "updated": "2016-11-17T12:44:13.000Z",
  "title": "Cluster Monte Carlo and dynamical scaling for long-range interactions",
  "authors": [
    "Emilio Flores-Sola",
    "Martin Weigel",
    "Ralph Kenna",
    "Bertrand Berche"
  ],
  "comment": "submitted to Eur. Phys. J Spec. Topics",
  "categories": [
    "cond-mat.stat-mech",
    "hep-lat",
    "physics.comp-ph"
  ],
  "abstract": "Many spin systems affected by critical slowing down can be efficiently simulated using cluster algorithms. Where such systems have long-range interactions, suitable formulations can additionally bring down the computational effort for each update from O($N^2$) to O($N\\ln N$) or even O($N$), thus promising an even more dramatic computational speed-up. Here, we review the available algorithms and propose a new and particularly efficient single-cluster variant. The efficiency and dynamical scaling of the available algorithms are investigated for the Ising model with power-law decaying interactions.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-11-17T12:44:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "cluster monte carlo",
      "long-range interactions",
      "dynamical scaling",
      "algorithms",
      "dramatic computational speed-up"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}