{
  "abstract": "The logistic smooth-to-hard shrinker is the proximal map of an explicit saturating spectral penalty for general linear inverse problems. Its exact weak convexity yields a unique scaled update and proximal-gradient stationarity guarantees, while independent validation provides finite-grid parameter-selection control. We use MRI as an illustrative example.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.21664",
  "author_total": 3,
  "authors": [
    {
      "name": "Guanzhong Yang"
    },
    {
      "name": "Zi Wang"
    },
    {
      "name": "Guang Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4fbagnvqmy7r7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.21664",
  "fulltext_url": null,
  "id": "4fbagnvqmy7r7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.21664",
  "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.NA"
    ],
    [
      "arxiv",
      "eess.IV"
    ],
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      "arxiv",
      "eess.SP"
    ],
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      "arxiv",
      "math.NA"
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      "arxiv",
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      "openalex",
      "2206|Sparse and Compressive Sensing Techniques"
    ]
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  "openalex": "W7222710415",
  "pdf_url": "https://arxiv.org/pdf/2610.21664",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4fbagnvqmy7r7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:54Z",
      "native_id": "2610.21664",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.21664"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.NA",
      "subfield": "numerical-analysis"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Data-driven Smooth-to-hard Spectral Regularization for General Linear Inverse Problems",
  "title_text": "Data-driven Smooth-to-hard Spectral Regularization for General Linear Inverse Problems",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:54Z",
  "url": "https://research.draconitic.com/paper/4fbagnvqmy7r7",
  "venue": "arXiv",
  "versions": []
}