GEOMETRIC MEAN DERIVATIVE-BASED OPEN NEWTON-COTES QUADRATURE RULES.
Published In: NED University Journal of Research, 2025, v. 22, n. 4. P. 248 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Mahesar, Sara; Shaikh, Muhammad Mujtaba; Memon, Kashif; Chandio, Muhammad Saleem 3 of 3
Abstract
A novel family of open Newton-Cotes formulas, termed GMDONC, is proposed and designed to enhance the accuracy of evaluating definite integrals through numerical integrators that are polynomial interpolatory in nature. By incorporating the geometric mean in the even-order derivatives of the integrand within the interval [a,b], the GMDONC methods exhibit a significant two-order accuracy improvement over traditional ONC approaches. Theorems on degree of precision, order of accuracy, and error terms are derived validating the theoretical advancements. Computational analyses confirm the superior performance of GMDONC through assessments of computational cost, CPU time, and error reductions across various integrals. Comparative evaluations with Gauss-Legendre methods highlight the effectiveness of GMDONC in handling integrals with diverse characteristics, including regular, oscillatory, periodic, and singular integrals. The proposed rules demonstrate computational and time efficiency in the global context compared to the existing polynomial ONC and Gauss-Legendre rules with the same number of functional nodes. The scope of the present improvement is restricted only to the context of polynomial interpolatory quadrature, not in the sense of spline/semi-interpolatory quadrature. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:NED University Journal of Research. 2025/12, Vol. 22, Issue 4, p248
- Document Type:Article
- Subject Area:Mathematics
- Publication Date:2025
- ISSN:2304-716X
- DOI:10.35453/NEDJR-ASCN013.R6
- Accession Number:191124887
- Copyright Statement:Copyright of NED University Journal of Research is the property of NED University of Engineering & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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