{
  "annotation_id": "2a12dd89-3190-4084-87a6-d0ffb5f53941",
  "date_created": "2026-03-02T18:01:21.557000Z",
  "date_modified": "2026-03-02T18:01:21.557000Z",
  "file_hash": "7d84a4b455eb8db9af8fc3816c43553289a13f0bd9fbf052bb21f7c7ef949daf",
  "private": false,
  "record": {
    "abstract": "We determined scaling laws for the numerical effort to find the optimal\nconfigurations of a simple model potential energy surface (PES) with a perfect\nfunnel structure that reflects key characteristics of the protein interactions.\nGeneralized Monte-Carlo methods(MCM, STUN) avoid an enumerative search of the\nPES and thus provide a natural resolution of the Levinthal paradox. We find\nthat the computational effort grows with approximately the eighth power of the\nsystem size for MCM and STUN, while a genetic algorithm was found to scale\nexponentially. The scaling behaviour of a derived lattice model is also\nrationalized.",
    "arxiv_id": "physics/9810035",
    "authors": [
      "K. Hamacher",
      "W. Wenzel"
    ],
    "categories": [
      "physics.bio-ph"
    ],
    "doi": "10.1103/PhysRevE.59.938",
    "journal_ref": "Phys. Rev. E 59, 939 (1999)",
    "title": "The Scaling Behaviour of Stochastic Minimization Algorithms in a Perfect   Funnel Landscape",
    "url": "https://arxiv.org/abs/physics/9810035"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "699b7a68-1d7f-4a7c-a7e5-6466279ffbc5",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}