{
  "abstract": "This paper presents a scalable optimal-transport-based framework for terminal distribution matching in multi-agent systems. While optimal transport provides a natural way to measure distributional mismatch and assign agents to a desired spatial distribution, global discrete transport can become computationally expensive for large-scale systems. We address this bottleneck by partitioning agents and target samples into spatially corresponding blocks and solving smaller local transport problems. Under a mass-balance condition, the resulting restricted coupling remains feasible for the global problem and provides an upper bound on the Wasserstein cost. The local assignments generate target locations for finite-horizon agent control, applicable to both linear and nonlinear dynamics. By alternating local assignment and control, we establish a cycle-to-cycle descent guarantee for the resulting transport surrogate. The proposed framework therefore enables scalable terminal distribution matching while retaining a rigorous connection to the Wasserstein objective. The technical soundness of the proposed results is validated through simulations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.38960",
  "author_total": 2,
  "authors": [
    {
      "name": "Kooktae Lee"
    },
    {
      "name": "Ruchika Singh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4yxxd7bnkqcah.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.38960",
  "fulltext_url": null,
  "id": "4yxxd7bnkqcah",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.38960",
  "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.MA"
    ],
    [
      "arxiv",
      "cs.RO"
    ],
    [
      "arxiv",
      "cs.SY"
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      "arxiv",
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  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.38960",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4yxxd7bnkqcah.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:49Z",
      "native_id": "2609.38960",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.38960"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Fast and Scalable Multi-Agent Distribution Matching via Partitioned Optimal Transport",
  "title_text": "Fast and Scalable Multi-Agent Distribution Matching via Partitioned Optimal Transport",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:49Z",
  "url": "https://research.draconitic.com/paper/4yxxd7bnkqcah",
  "venue": "arXiv",
  "versions": []
}