{
  "abstract": "The paper deals with the global minimization of a differentiable cost function mapping a ball of a finite dimensional Euclidean space into an interval of real numbers. It is established that a suitable random perturbation of the gradient method with a fixed parameter generates a bounded minimizing sequence and leads to a global minimum: the perturbation avoids convergence to local minima. The stated results suggest an algorithm for the numerical approximation of global minima: experiments are performed for the problem of fitting a sum of exponentials to discrete data and to a nonlinear system involving about 5000 variables. The effect of the random perturbation is examined by comparison with the purely deterministic gradient method.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Marc Pogu"
    },
    {
      "name": "José Souza de Cursi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eef14kgvjz763.bib",
  "cited_by": 49,
  "doi": "10.1007/bf01100691",
  "fulltext_url": null,
  "id": "eef14kgvjz763",
  "issns": [
    "0925-5001",
    "1573-2916"
  ],
  "landing_url": "https://hal.science/hal-05791640v1",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "hal",
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    [
      "openalex",
      "2612|Advanced Optimization Algorithms Research"
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  "openalex": "W2085145661",
  "pdf_url": "https://hal.science/hal-05791640/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": "Springer Verlag",
  "ris": "https://research.draconitic.com/paper/eef14kgvjz763.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:54Z",
      "native_id": "hal-05791640",
      "source": "hal",
      "url": "https://hal.science/hal-05791640v1"
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  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2612|Advanced Optimization Algorithms Research",
      "subfield": "numerical-analysis"
    }
  ],
  "title": "Global optimization by random perturbation of the gradient method with a fixed parameter",
  "title_text": "Global optimization by random perturbation of the gradient method with a fixed parameter",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:54Z",
  "url": "https://research.draconitic.com/paper/eef14kgvjz763",
  "venue": "Journal of Global Optimization",
  "versions": []
}