Convergence of the Method of False Position
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2022.13.2.13Keywords:
Method of false position, rate of convergence, percentage error, trend, algorithm, accuracy, 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.
The method of false position has been applied to calculate the fourth roots of the natural numbers from 1 to 30 in the interval [0, 3] with the stopping tolerance of 0.00001 using C++ computer program. The minimum error 0.000000029282 and minimum percentage error 0.000001251170 have been obtained in the determination of fourth roots of 30. The maximum error 0.000002324581 and maximum percentage error 0.000232458100 have been obtained in the determination of fourth roots of 1. The average value of the error is 0.000000392037 and the average value of percentage error is 0.000027500512. Minimum, maximum and average values the numerical rate of convergence have been found to be 0.239808153477, 1.851851851852 and 1.197514787730 respectively.Abstract
How to Cite
Downloads
Similar Articles
- 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
- Sharada C, T N Ravi, S Panneer Arokiara, Lancaster sliced regressive keyword extraction based semantic analytics on social media documents , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Jhankar Moolchandani, Kulvinder Singh, English language analysis using pattern recognition and machine learning , The Scientific Temper: Vol. 14 No. 03 (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
- Engida Admassu, Classifying enset based on their disease tolerance using deep learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Nilesh M. Patil, P M. Krishna, G. Deena, C Harini, R.K. Gnanamurthy, Romala V. Srinivas, Exploring real-time patient monitoring and data analytics with IoT-based smart healthcare monitoring , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- S. Dhivya, S. Prakash, Power quality assessment in solar-connected smart grids via hybrid attention-residual network for power quality (HARN-PQ) , The Scientific Temper: Vol. 15 No. 04 (2024): 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
- B Supraja, B Ramachandra, N Venkatasubba Naidu, Analytical Method Development and Validation Analysis for Quantitative Assessment of Thifluzamide by HPLC Procedure , The Scientific Temper: Vol. 13 No. 01 (2022): The Scientific Temper
- P. N. Malleswari, P. V. S. Gupta, S. V. M. Vardhan, D. Ramachandran, Quantitative estimation of ethanol content in eribulin mesylate injection using headspace gas chromatographic with flame ionization detector [HS-GC-FID] , The Scientific Temper: Vol. 15 No. 02 (2024): 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 Bisection Method , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper

