{
  "abstract": "We propose a subgradient-based block coordinate descent (BCD-SubG) framework for nonconvex optimization over products of probability simplices and a closed feasible set, motivated by semi-nonnegative independent component analysis (Semi-NNICA). Elementary column transformations are used to preserve simplex feasibility, while first-order information guides block and pair selection. We establish the global convergence of BCD-SubG using a constrained Lojasiewicz inequality; additional eventual-interiority conditions yield stationary limits and convergence rates. We then specialize BCD-SubG to the direct-fit and indirect-fit Semi-NNICA models and derive explicit updates that ensure global convergence without further assumptions. For both models, eventual interiority also yields stationary limits and convergence-rate estimates. Experiments on synthetic mixtures and simulated magnetic resonance spectroscopy data show generally lower recovery errors and shorter wall-clock times than the tested Semi-NNICA baselines. Joint diagonalization experiments further demonstrate lower final objective values.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16765",
  "author_total": 4,
  "authors": [
    {
      "name": "Zhenyu Cai"
    },
    {
      "name": "Wentao Ding"
    },
    {
      "name": "Jianze Li"
    },
    {
      "name": "Wen Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1rj7kr6rgg0er.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16765",
  "fulltext_url": null,
  "id": "1rj7kr6rgg0er",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16765",
  "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",
      "math.OC"
    ],
    [
      "openalex",
      "1711|Blind Source Separation Techniques"
    ]
  ],
  "openalex": "W7221775815",
  "pdf_url": "https://arxiv.org/pdf/2610.16765",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1rj7kr6rgg0er.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:15Z",
      "native_id": "2610.16765",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16765"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Globally Convergent Block Coordinate Descent Algorithms for Semi-Nonnegative Independent Component Analysis",
  "title_text": "Globally Convergent Block Coordinate Descent Algorithms for Semi-Nonnegative Independent Component Analysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:15Z",
  "url": "https://research.draconitic.com/paper/1rj7kr6rgg0er",
  "venue": "arXiv",
  "versions": []
}