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
- L. Amudavalli, K. Muthuramalingam, Energy-efficient location-based routing protocol for wireless sensor networks using teaching-learning soccer league optimization (TLSLO) , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- N. Suresh Kumar, S.N.Md. Assarudeen, Solving neutrosophic multi-objective linear fractional programming problem using central measures , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Sruthy M.S, R. Suganya, An efficient key establishment for pervasive healthcare monitoring , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Adedotun Adedayo F, Odusanya Oluwaseun A, Adesina Olumide S, Adeyiga J. A, Okagbue, Hilary I, Oyewole O, Prediction of automobile insurance fraud claims using machine learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- V. Karthikeyan, C. Jayanthi, Improving image quality assessment with enhanced denoising autoencoders and optimization methods , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Sivakumar S, Rajasekaran Kondareddy, Kalyani Ayyemperumal, Building SaaS solutions using microsoft azure for achieving safe and secure tax related software , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- V. Karthikeyan, C. Jayanthi, Advancements in image quality assessment: a comparative study of image processing and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Kirti Gupta, Parul Goyal, Modified-multi objective firefly optimization algorithm for object oriented applications test suites optimization , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- S. Vanaja, Hari Ganesh S, Application of data mining and machine learning approaches in the prediction of heart disease – A literature survey , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- A. Rukmani, C. Jayanthi, Trust and security in wireless sensor networks: A literature review of approaches for malicious node detection , The Scientific Temper: Vol. 15 No. spl-1 (2024): 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