{
  "abstract": "We present recent developments in Halpern Peaceman--Rachford (HPR) methods for solving large-scale convex composite quadratic programming problems. Within a unified HPR framework applied to the restricted Wolfe dual, we develop several algorithmic variants based on different choices of the semi-proximal operator $\\mathcal{T}_1$ for the joint dual subproblem. Specifically, an sGS-based construction leads to a sequential $w$--$y$--$w$ update scheme, while a block-linearized construction eliminates cross-block coupling and enables parallel dual updates. To improve the latter, we further introduce a coupling-aware construction that exploits a second spectral moment to obtain a tighter row majorizer. We extend the framework to convex quadratically constrained quadratic programming through factor-based second-order cone reformulations. Additional computational enhancements include equality Gram scaling and asynchronous CPU polishing. We establish convergence results under fixed metrics and discuss practical adaptive strategies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.01887",
  "author_total": 5,
  "authors": [
    {
      "name": "Kaihuang Chen"
    },
    {
      "name": "Defeng Sun"
    },
    {
      "name": "Yancheng Yuan"
    },
    {
      "name": "Guojun Zhang"
    },
    {
      "name": "Xinyuan Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7xnwbm7eedpve.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.01887",
  "fulltext_url": null,
  "id": "7xnwbm7eedpve",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.01887",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2611.01887",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7xnwbm7eedpve.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:10Z",
      "native_id": "2611.01887",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.01887"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Recent Developments in HPR Methods for Large-Scale Convex Composite Quadratic Programming",
  "title_text": "Recent Developments in HPR Methods for Large-Scale Convex Composite Quadratic Programming",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:22Z",
  "url": "https://research.draconitic.com/paper/7xnwbm7eedpve",
  "venue": "arXiv",
  "versions": []
}