Convergence of Bisection Method
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2022.13.2.14Keywords:
Bisection method, convergence, stopping tolerance, error, percentage error, computer program, iterations.Dimensions Badge
Issue
Section
License
Copyright (c) 2022 The Scientific Temper
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Fourth roots of the natural numbers from 1 to 30 have been calculated by Bisection method in the interval [0, 3] using stopping tolerance 0f 0.00001. Calculated roots have been compared with the actual values of roots to obtain error and percentage error in the calculated roots. Numerical rate of convergence has also been calculated in the determination of each fourthroot. The highest numerical rate of convergence of Bisection method has been observed in the calculation of fourth root of 2 and is equal to 1.754385964912. The lowest numerical rate of convergence of Bisection method has been observed in the calculation of fourth roots of 1, 3, -8, 10, 12 and is equal to 1.333333333333. Average error, average percentage error and average numerical rate of convergence of Bisection method have been found to be 0.000000062635, 0.000003048055 and 1.458082183940 respectivelyAbstract
How to Cite
Downloads
Similar Articles
- Sreenath M.V. Reddy, D. Annapurna, Anand Narasimhamurthy, Influence node analysis based on neighborhood influence vote rank method in social network , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- J. Fathima Fouzia, M. Mohamed Surputheen, M. Rajakumar, Hybrid pigeon optimization-based feature selection and modified multi-class semantic segmentation for skin cancer detection (HPO-MMSS) , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- Varsha Sharma, Krishna Kumar Gupta, Comparative accuracy of IOL power calculation formulas in nanophthalmic eyes undergoing cataract surgery , The Scientific Temper: Vol. 16 No. 07 (2025): The Scientific Temper
- Sathya R., Balamurugan P, Classification of glaucoma in retinal fundus images using integrated YOLO-V8 and deep CNN , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- P. Susai Raj, A. Edward William Benjamin, Evaluating the effectiveness of academic resilience intervention for at-risk students at higher secondary level , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- S. Nagarani, Amalraj P., Lakshay Phor, Nishank S. Pimple, Banashree Sen, Ramaprasad Maiti, Vikas S. Jadhav, Innovative technological advancements in solving real quadratic equations: Pioneering the frontier of mathematical innovation , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- B. S. E. Zoraida, J. Jasmine Christina Magdalene, Smart grid precision: Evaluating machine learning models for forecasting of energy consumption from a smart grid , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Engida Admassu, Classifying enset based on their disease tolerance using deep learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- K. Vani, S. Sujatha, Fault tolerance systems in open source cloud computing environments–A systematic review , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- V. Infine Sinduja, P. Joesph Charles, A hybrid approach using attention bidirectional gated recurrent unit and weight-adaptive sparrow search optimization for cloud load balancing , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Lavkush Pandey, Trilokinath, Convergence of the Method of False Position , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
