{
  "abstract": "Feature-based newsvendor models use observable covariates to tailor inventory decisions, aiming to balance holding and shortage costs under demand uncertainty. However, high-dimensional feature sets often hinder interpretability and inflate data collection and implementation costs. This paper studies variable selection for the feature-based newsvendor problem under a hard cardinality constraint on the number of selected features. We formulate the resulting $\\ell_0$-constrained empirical newsvendor problem with $\\ell_2$-regularization, establish its computational hardness, and develop a mixed-integer second-order cone programming reformulation that strengthens the standard Big-$M$ formulation. To enable scalability beyond exact optimization, we develop a randomized-rounding algorithm with a bi-criteria guarantee and a greedy heuristic. Statistically, we provide theoretical analysis of the resulting sparse policy estimator, including finite-sample estimation error, out-of-sample risk bounds, and support recovery guarantees. Extensive experiments on both synthetic and real data illustrate the computational and statistical trade-offs among various baselines. Our results demonstrate that the proposed variable selection framework achieves competitive out-of-sample operational costs while using substantially fewer covariates.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.01544",
  "author_total": 2,
  "authors": [
    {
      "name": "Zhaoliang Yuan"
    },
    {
      "name": "Jie Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9s9ekkp46c8gm.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.01544",
  "fulltext_url": null,
  "id": "9s9ekkp46c8gm",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.01544",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1404|Supply Chain and Inventory Management"
    ]
  ],
  "openalex": "W7206207358",
  "pdf_url": "https://arxiv.org/pdf/2609.01544",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9s9ekkp46c8gm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:10Z",
      "native_id": "2609.01544",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.01544"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Variable Selection for Feature-Based Newsvendor",
  "title_text": "Variable Selection for Feature-Based Newsvendor",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:10Z",
  "url": "https://research.draconitic.com/paper/9s9ekkp46c8gm",
  "venue": "arXiv",
  "versions": []
}