An adaptive algorithm for determining the optimal degree of regression in constrained mock-Chebyshev least squares quadrature
| Title | An adaptive algorithm for determining the optimal degree of regression in constrained mock-Chebyshev least squares quadrature |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Authors | Dell'Accio, F, Di Tommaso, F, Francomano, E, Nudo, F |
| Journal | Dolomites Research Notes on Approximation |
| Volume | 15 |
| Issue | 4 |
| Pagination | 35-44 |
| Date Published | 12/2022 |
| Publisher | Padova University Press |
| Place Published | Padova, IT |
| ISSN Number | 2035-6803 |
| Abstract | In this paper we develop an adaptive algorithm for determining the optimal degree of regression in the constrained mock-Chebyshev least-squares interpolation of an analytic function to obtain quadrature formulas with high degree of exactness and accuracy from equispaced nodes. We numerically prove the effectiveness of the proposed algorithm by several examples. |
| URL | https://drna.padovauniversitypress.it/2022/4/4 |
| DOI | 10.14658/pupj-drna-2022-4-4 |
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