{
  "abstract": "This work explores the representation of univariate and multivariate functions as quantized tensor trains (QTT). It develops a constructive algorithm that employs expansions in orthogonal polynomial bases and Clenshaw evaluations to represent analytic and highly differentiable functions as QTT approximants. This approach provides a general framework for function composition in QTT form that generalizes efficiently to multidimensional settings. Without loss of generality, the presentation focuses on the Chebyshev basis, where the method demonstrates rapid convergence for highly differentiable functions, in agreement with classical theoretical predictions, and remains numerically stable at high polynomial orders. The performance of the algorithm is compared with that of tensor cross-interpolation (TCI) and multiscale interpolative constructions, demonstrating competitive performance and favorable scaling behavior in certain scenarios.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Fundamental Physics IFF-CSIC, Calle Serrano 113b, Madrid, 28006, Madrid, Spain"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Juan José Rodríguez-Aldavero",
      "orcid": "0009-0001-8942-4285"
    },
    {
      "affs": [
        0
      ],
      "name": "Paula García-Molina",
      "orcid": "0000-0001-6287-8343"
    },
    {
      "affs": [
        0
      ],
      "name": "Luca Tagliacozzo",
      "orcid": "0000-0002-5858-1587"
    },
    {
      "affs": [
        0
      ],
      "name": "Juan José García‐Ripoll",
      "orcid": "0000-0001-8993-4624"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4p6qnktma4cqd.bib",
  "cited_by": 14,
  "doi": "10.1016/j.laa.2026.08.022",
  "fulltext_url": null,
  "id": "4p6qnktma4cqd",
  "issns": [
    "0024-3795",
    "1873-1856"
  ],
  "landing_url": "https://doi.org/10.1016/j.laa.2026.08.022",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2605|Tensor decomposition and applications"
    ]
  ],
  "openalex": "W7203743279",
  "pdf_url": "https://www.sciencedirect.com/science/article/pii/S0024379526003411/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-19",
  "publisher": "Elsevier BV",
  "ris": "https://research.draconitic.com/paper/4p6qnktma4cqd.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:20:41Z",
      "native_id": "W7203743279",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.laa.2026.08.022"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2605|Tensor decomposition and applications",
      "subfield": "numerical-analysis"
    }
  ],
  "title": "Approximation and composition of functions in quantized tensor trains via orthogonal polynomial expansions",
  "title_text": "Approximation and composition of functions in quantized tensor trains via orthogonal polynomial expansions",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:00Z",
  "url": "https://research.draconitic.com/paper/4p6qnktma4cqd",
  "venue": "Linear Algebra and its Applications",
  "versions": []
}