Function reconstruction using a Jacobi-weighted quadratic enriched histopolation method

Federico Nudo

Mathematics and Computers in Simulation (MATCOM), 2026, vol. 245, issue C, 512-529

Abstract: Reconstructing functions based on their integral data is a fundamental task in computational science and engineering. Histopolation techniques provide a natural framework for this task, as they are specifically designed to reconstruct functions from averaged data. We introduce a new family of Jacobi-weighted enriched histopolation schemes that extend classical edge-based local histopolation by incorporating additional weighted linear functionals. The method combines quadratic polynomial enrichment with weighted linear functionals, enhancing its ability to capture complex features. We establish unisolvence of the enriched scheme and demonstrate, through extensive numerical experiments, its significant accuracy gains over standard histopolation methods. These results establish weighted enriched histopolation as a powerful and versatile tool for high-fidelity function reconstruction in computational science.

Keywords: Orthogonal polynomials; Function reconstruction; Jacobi orthogonal polynomials; Weighted histopolation method; Polynomial approximation (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475426000674
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:245:y:2026:i:c:p:512-529

DOI: 10.1016/j.matcom.2026.02.021

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().