{
  "abstract": "In this paper, we propose a sparse descent direction derived from the Newton direction within a low-dimensional subspace for sparse optimization problems. Building upon this direction and incorporating hard thresholding and orthogonal projection, we develop a novel algorithm termed the sparse descent subspace-Newton thresholding (SDSNT) algorithm. We further establish its global convergence under the restricted isometry property $δ_{3k}\\leq \\sqrt{3/20}$ of the measurement matrix. Numerical experiments on random sparse vector recovery and audio signal reconstruction reveal that SDSNT achieves comparable performance to several state-of-the-art algorithms. Notably, relative to most of them, it delivers superior recovery at high sparsity levels and improved efficiency at low sparsity levels.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41318",
  "author_total": 3,
  "authors": [
    {
      "name": "Zhong-Feng Sun"
    },
    {
      "name": "Nan Meng"
    },
    {
      "name": "Yun-Bin Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eny1xw4067yw6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41318",
  "fulltext_url": null,
  "id": "eny1xw4067yw6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41318",
  "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",
      "cs.IT"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "arxiv",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41318",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/eny1xw4067yw6.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:20Z",
      "native_id": "2610.41318",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41318"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Sparse descent subspace-Newton thresholding algorithm for sparse optimization problems",
  "title_text": "Sparse descent subspace-Newton thresholding algorithm for sparse optimization problems",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:52Z",
  "url": "https://research.draconitic.com/paper/eny1xw4067yw6",
  "venue": "arXiv",
  "versions": []
}