@inproceedings{7c1f26811d114d688692b992b5d0bc65,
title = "Minimizing Gauss Quadrature Error Estimates by a New Taylor-Like Formula",
abstract = "In this article, we first derive an nth-order expansion formula. This generalizes our previous work on a first-order Taylor-like formula with reduced remainder compared to the one of the classical Taylor expansion. This generalization allows for greater flexibility in choosing the points and improves the accuracy of the approximation. We then determine the optimal distribution of points and weights that minimizes the remainder and apply this result to Gaussian quadrature error estimates. Finally, numerical experiments are provided to illustrate the effectiveness and practical advantages of the proposed approach.",
keywords = "Error estimate, Gauss quadrature, Taylor-like formula, Taylor{\textquoteright}s theorem",
author = "Joel Chaskalovic and Franck Assous",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 12th International Conference on New Trends in the Applications of Differential Equations in Sciences, NTADES 2025 ; Conference date: 01-09-2025 Through 04-09-2025",
year = "2026",
doi = "10.1007/978-3-032-17071-2\_18",
language = "אנגלית",
isbn = "9783032170705",
series = "Springer Proceedings in Mathematics and Statistics",
pages = "215--224",
editor = "Angela Slavova",
booktitle = "New Trends in the Applications of Differential Equations in Sciences - NTADES 2025",
}