{
  "annotation_id": "2ffa804a-36ce-4eb0-8f63-fcdc84b09fd8",
  "date_created": "2026-03-02T18:00:29.263000Z",
  "date_modified": "2026-03-02T18:00:29.263000Z",
  "file_hash": "20a635dbcb5e1d9662afbc6466c75cb0d5efb323a2d309724457e45977e417c4",
  "private": false,
  "record": {
    "abstract": "A theoretical analysis is given of the equation of motion method, due to\nAlben et al., to compute the eigenvalue distribution (density of states) of\nvery large matrices. The salient feature of this method is that for matrices of\nthe kind encountered in quantum physics the memory and CPU requirements of this\nmethod scale linearly with the dimension of the matrix. We derive a rigorous\nestimate of the statistical error, supporting earlier observations that the\ncomputational efficiency of this approach increases with matrix size. We use\nthis method and an imaginary-time version of it to compute the energy and the\nspecific heat of three different, exactly solvable, spin-1/2 models and compare\nwith the exact results to study the dependence of the statistical errors on\nsample and matrix size.",
    "arxiv_id": "physics/0004016",
    "authors": [
      "Anthony Hams",
      "Hans De Raedt"
    ],
    "categories": [
      "physics.comp-ph",
      "physics.data-an",
      "quant-ph"
    ],
    "doi": "10.1103/PhysRevE.62.4365",
    "journal_ref": "Phys. Rev. E62 4365-4377 (2000)",
    "title": "Fast Algorithm for Finding the Eigenvalue Distribution of Very Large   Matrices",
    "url": "https://arxiv.org/abs/physics/0004016"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a1a1dde5-3da3-4622-b9b6-88d93672347e",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}