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.
Dell'Accio F., Di Tommaso F., Francomano E., Nudo F. (2022). An adaptive algorithm for determining the optimal degree of regression in constrained mock-Chebyshev least squares quadrature. DOLOMITES RESEARCH NOTES ON APPROXIMATION, 15(4), 35-44 [10.14658/pupj-drna-2022-4-4].
An adaptive algorithm for determining the optimal degree of regression in constrained mock-Chebyshev least squares quadrature
Francomano E.;
2022-12-01
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.
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