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
- Kumari Neha, Amrita ., Quantum programming: Working with IBM’S qiskit tool , The Scientific Temper: Vol. 14 No. 01 (2023): 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
- 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
- A. Jafar Ali, Dr.G. Ravi, D.I. George Amalarethinam, AI-Driven Swarm-Optimized Adaptive Routing Using Quantum-Inspired Neural Scheduling with Homomorphic Encryption , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
- S. Udhaya Priya, M. Parveen, ETPPDMRL: A novel approach for prescriptive analytics of customer reviews via enhanced text parsing and reinforcement learning , The Scientific Temper: Vol. 16 No. 05 (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
- Abhishek K Pandey, Amrita Sahu, Ajay K Harit, Manoj Singh, Nutritional composition of the wild variety of edible vegetables consumed by the tribal community of Raipur, Chhattisgarh, India , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Shaik Khaleel Ahamed, Neerav Nishant, Ayyakkannu Selvaraj, Nisarg Gandhewar, Srithar A, K.K.Baseer, Investigating privacy-preserving machine learning for healthcare data sharing through federated learning , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Rasheedha A, Santhosh B, Archana N, Sandhiya A, Foot sens - foot pressure monitoring systems , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Sanjeev Kumar, Saurabh Charaya, Rachna Mehta, Multi-Metric Evaluation Framework for Machine Learning-Based Load Prediction in e-Governance Systems , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
<< < 10 11 12 13 14 15 16 17 18 19 > >>
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

