{
  "id": "2202.02725",
  "version": "v1",
  "published": "2022-02-06T07:49:19.000Z",
  "updated": "2022-02-06T07:49:19.000Z",
  "title": "Efficient primal heuristics for mixed-integer linear programs",
  "authors": [
    "Akang Wang",
    "Linxin Yang",
    "Sha Lai",
    "Xiaodong Luo",
    "Xiang Zhou",
    "Haohan Huang",
    "Shengcheng Shao",
    "Yuanming Zhu",
    "Dong Zhang",
    "Tao Quan"
  ],
  "comment": "This work will be published on the ML4CO NeurIPS 2021 Competition website (https://www.ecole.ai/2021/ml4co-competition/) in the proceedings section. A succinct version will appear in a special Proceedings of Machine Learning Research (PMLR) volume dedicated to the NeurIPS 2021 competitions",
  "categories": [
    "math.OC"
  ],
  "abstract": "This paper is a short report about our work for the primal task in the Machine Learning for Combinatorial Optimization NeurIPS 2021 Competition. For each dataset of our interest in the competition, we propose customized primal heuristic methods to efficiently identify high-quality feasible solutions. The computational studies demonstrate the superiority of our proposed approaches over the competitors'.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-02-06T07:49:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "mixed-integer linear programs",
      "efficient primal heuristics",
      "identify high-quality feasible solutions",
      "customized primal heuristic methods",
      "combinatorial optimization neurips"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}