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
- K. Sreenivasulu, Sampath S, Arepalli Gopi, Deepak Kartikey, S. Bharathidasan, Neelam Labhade Kumar, Advancing device and network security for enhanced privacy , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Shiny Bridgette I, Rexlin Jeyakumari S, An optimal fuzzy inventory model for rice farming using lagrangean method , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Manikant Tripathi, Sukriti Pathak, Ranjan Singh, Pankaj Singh, Pradeep K. Singh, Nivedita Prasad, Sadanand Maurya, Awadhesh Kumar Shukla, Adsorptive remediation of hexavalent chromium using agro-waste rice husk: Optimization of process parameters and functional groups characterization using FTIR analysis , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Y. Mohammed Iqbal, M. Mohamed Surputheen, S. Peerbasha, Swarm intelligence-driven HC2NN model for optimized COVID-19 detection using lung imaging , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- Aishwarya Jha, Jyoti Gangta, Neha Kapur, Comparison of anterior corneal aberrometry, keratometry and pupil size with Scheimpflug tomography and ray tracing aberrometer in moderate and high refractive error , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Maheshbhai R. Jakhotra, Sanjay Gupta, A Study on the Design and Effectiveness of a Spoken English Program for Gujarati Medium Secondary School Students (Aged 14–15) , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Kumari Neha, Amrita ., Quantum programming: Working with IBM’S qiskit tool , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- J. Fathima Fouzia, M. Mohamed Surputheen, M. Rajakumar, A Unified Consistency-Calibrated Boundary-Aware Framework for Generalizable Skin Cancer Detection , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Ganga Gudi, Mallamma V Reddy, Hanumanthappa M, Enhancing Kannada text-to-speech and braille conversion with deep learning for the visually impaired , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Merlin Sofia S, D. Ravindran, G. Arockia Sahaya Sheela, Clean Balance-Ensemble CHD: A Balanced Ensemble Learning Framework for Accurate Coronary Heart Disease Prediction , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
<< < 9 10 11 12 13 14 15 16 17 18 > >>
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
