Derivation of Romberg's Rule Using N-Subdivisions and Applications of Romberg's Rule

By using a large number of subdivisions n to obtain good accuracy in approximating the integral  we note that an increase in the value of n does not necessarily mean an increase in accuracy in the result or even an improvement in the result. In addition, increasing the number n means an increase in the number of times the function values are calculated. The importance of Romberg's rule lies in improving the results obtained using the previous rules and obtaining high accuracy in arriving at approximate results faster and with a small amount of error. The reason for this is the high rank of the Romberg rule compared to the trapezoid rule, for example, which is not accurate in its results because the cutting error for this rule is of the order only.

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Publication Details

Journal
Mağallaẗ al-tarbiyaẗ wa-al-ʻilm
Published
2025-04-01
DOI
https://doi.org/10.33899/edusj.2025.155630.1520
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Derivation of Romberg's Rule Using N-Subdivisions and Applications of Romberg's Rule

mustafa Salim
Mağallaẗ al-tarbiyaẗ wa-al-ʻilm
Neural Networks and Applications
article

Derivation of Romberg's Rule Using N-Subdivisions and Applications of Romberg's Rule

mustafa Salim
article en

Abstract

By using a large number of subdivisions n to obtain good accuracy in approximating the integral  we note that an increase in the value of n does not necessarily mean an increase in accuracy in the result or even an improvement in the result. In addition, increasing the number n means an increase in the number of times the function values are calculated. The importance of Romberg's rule lies in improving the results obtained using the previous rules and obtaining high accuracy in arriving at approximate results faster and with a small amount of error. The reason for this is the high rank of the Romberg rule compared to the trapezoid rule, for example, which is not accurate in its results because the cutting error for this rule is of the order only.

Mağallaẗ al-tarbiyaẗ wa-al-ʻilmVol. 34(2)
University of Mosul (IQ)
University of Mosul
Openalex Percentile: Top 97%
Neural Networks and Applications
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