{
  "abstract": "Many scientific and engineering applications require the global optimization of expensive black-box functions. In high-dimensional settings, however, achieving computational scalability is a major challenge. To address this challenge, we propose a distributed spatial branch-and-bound framework that exploits partially separable structure for the global optimization of box-constrained black-box problems. The proposed framework adopts a two-layer structure in which branching is performed only on a lower-dimensional set of coupling variables in an outer layer. Within each outer node, the remaining non-overlapping (private) variables, which appear independently across blocks, have their domains partitioned and explored in parallel within an inner layer, resulting in a naturally parallelizable framework that is well suited for implementation on multi-core CPU architectures. At each node, valid upper bounds are obtained via sampling, with optional refinement using a local solver, while lower bounds are computed using data-driven convex underestimators constructed over partitions of the private variables. A residual-based correction and finite-sample validity analysis provide conditions under which the partitionwise underestimators are valid lower bounds, while the proposed branching and refinement mechanism establishes convergence to an $ε$-global optimum. Numerical studies on benchmark and ODE-constrained parameter-estimation problems demonstrate improvements in both sampling efficiency and computational performance relative to monolithic approaches.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.20539",
  "author_total": 2,
  "authors": [
    {
      "name": "Damilola Fasiku"
    },
    {
      "name": "Wentao Tang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a317ez5abr0zf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.20539",
  "fulltext_url": null,
  "id": "a317ez5abr0zf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.20539",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2612|Advanced Optimization Algorithms Research"
    ]
  ],
  "openalex": "W7221975123",
  "pdf_url": "https://arxiv.org/pdf/2610.20539",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a317ez5abr0zf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:35Z",
      "native_id": "2610.20539",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.20539"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "A Decomposition-Based Data-Driven Branch-and-Bound Approach for Black-Box Global Optimization",
  "title_text": "A Decomposition-Based Data-Driven Branch-and-Bound Approach for Black-Box Global Optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:35Z",
  "url": "https://research.draconitic.com/paper/a317ez5abr0zf",
  "venue": "arXiv",
  "versions": []
}