Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "15828"
  #_source: array:26 [
    "id" => "15828"
    "slug" => "15828-a-trust-region-framework-for-derivative-free-mixed-integer-optimization"
    "yearMonth" => "2024-09"
    "year" => "2024"
    "title" => "A trust-region framework for derivative-free mixed-integer optimization"
    "description" => "TORRES, J.J., NANNICINI, G., TRAVERSI, E. et WOLFLER CALVO, R. (2024). A trust-region framework for derivative-free mixed-integer optimization. <i>Mathematical Programming Computation</i>, 16(3), pp. 369-422."
    "authors" => array:4 [
      0 => array:3 [
        "name" => "TRAVERSI Emiliano"
        "bid" => "B00820417"
        "slug" => "traversi-emiliano"
      ]
      1 => array:1 [
        "name" => "Torres Juan J."
      ]
      2 => array:1 [
        "name" => "Nannicini Giacomo"
      ]
      3 => array:1 [
        "name" => "Wolfler Calvo Roberto"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:4 [
      0 => "Derivative-free optimization"
      1 => "Mixed-integer programming"
      2 => "Nonlinear programming"
      3 => "Trust-region methods"
    ]
    "updatedAt" => "2025-07-08 10:54:24"
    "publicationUrl" => "https://doi.org/10.1007/s12532-024-00260-0"
    "publicationInfo" => array:3 [
      "pages" => "369-422"
      "volume" => "16"
      "number" => "3"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "This paper overviews the development of a framework for the optimization of black-box mixed-integer functions subject to bound constraints. Our methodology is based on the use of tailored surrogate approximations of the unknown objective function, in combination with a trust-region method. To construct suitable model approximations, we assume that the unknown objective is locally quadratic, and we prove that this leads to fully-linear models in restricted discrete neighborhoods. We show that the proposed algorithm converges to a first-order mixed-integer stationary point according to several natural definitions of mixed-integer stationarity, depending on the structure of the objective function. We present numerical results to illustrate the computational performance of different implementations of this methodology in comparison with the state-of-the-art derivative-free solver NOMAD."
      "en" => "This paper overviews the development of a framework for the optimization of black-box mixed-integer functions subject to bound constraints. Our methodology is based on the use of tailored surrogate approximations of the unknown objective function, in combination with a trust-region method. To construct suitable model approximations, we assume that the unknown objective is locally quadratic, and we prove that this leads to fully-linear models in restricted discrete neighborhoods. We show that the proposed algorithm converges to a first-order mixed-integer stationary point according to several natural definitions of mixed-integer stationarity, depending on the structure of the objective function. We present numerical results to illustrate the computational performance of different implementations of this methodology in comparison with the state-of-the-art derivative-free solver NOMAD."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2025-12-06T07:21:43.000Z"
    "docTitle" => "A trust-region framework for derivative-free mixed-integer optimization"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/traversi-emiliano">TRAVERSI Emiliano</a>, Torres Juan J., Nannicini Giacomo, Wolfler Calvo Roberto"
    "docDescription" => "<span class="document-property-authors">TRAVERSI Emiliano, Torres Juan J., Nannicini Giacomo, Wolfler Calvo Roberto</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2024</span>"
    "keywordList" => "<a href="#">Derivative-free optimization</a>, <a href="#">Mixed-integer programming</a>, <a href="#">Nonlinear programming</a>, <a href="#">Trust-region methods</a>"
    "docPreview" => "<b>A trust-region framework for derivative-free mixed-integer optimization</b><br><span>2024-09 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1007/s12532-024-00260-0" target="_blank">A trust-region framework for derivative-free mixed-integer optimization</a>"
  ]
  +lang: "en"
  +"_score": 8.714207
  +"_ignored": array:2 [
    0 => "abstract.en.keyword"
    1 => "abstract.fr.keyword"
  ]
  +"parent": null
}