{
  "abstract": "The cvxoptdes R-package provides a unified framework to perform convex optimal experiment design and online response optimization for linear, generalized linear and additive, nonlinear, and random intercept mixed models, using exclusively open-source solvers. It supports optimization and augmentation of weighted, penalized and convex constrained continuous designs across a discrete grid of candidate points and rounding to integer-valued designs using point-exchange algorithms or mixed-integer programming for orthogonally constrained designs, including the class of orthogonal minimally aliased response surface (OMARS) designs. Models and design constraints are defined and updated in an object-oriented context with the aim to facilitate the process of sequential (penalized) design augmentation analogous to online design procedures known from active learning and Bayesian optimization.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.14288",
  "author_total": 4,
  "authors": [
    {
      "name": "Joris Chau"
    },
    {
      "name": "Alexandre Segers"
    },
    {
      "name": "Martin Otava"
    },
    {
      "name": "Stephen Karanja"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ncq6tzvw16y1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.14288",
  "fulltext_url": null,
  "id": "8ncq6tzvw16y1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.14288",
  "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",
      "stat.CO"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1803|Optimal Experimental Design Methods"
    ]
  ],
  "openalex": "W7221831498",
  "pdf_url": "https://arxiv.org/pdf/2610.14288",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8ncq6tzvw16y1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:26Z",
      "native_id": "2610.14288",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.14288"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "cvxoptdes: convex optimal experiment design in R",
  "title_text": "cvxoptdes: convex optimal experiment design in R",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:26Z",
  "url": "https://research.draconitic.com/paper/8ncq6tzvw16y1",
  "venue": "arXiv",
  "versions": []
}