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
- D. Jayaprasanth, J. Arul Melissa, Extended Kalman filter-based prognostic of actuator degradation in two tank system , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- K. Mohamed Arif Khan, A.R. Mohamed Shanavas, Optimizing IoT application deployment with fog - cloud paradigm: A resource-aware approach , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Showkat Ahmad Shah, Netsanet Gizaw, Impact of selected macroeconomic variables on economic growth in Ethiopia: A time series analysis , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Vijetna Singh, Alka Thakur, ECOLOGICAL ENGINEERING OF MICROALGAE FOR ENHANCED ENERGY PRODUCTION , The Scientific Temper: Vol. 8 No. 1&2 (2017): The Scientific Temper
- G. Vijayalakshmi, M. V. Srinath, Student’s Academic Performance Improvement Using Adaptive Ensemble Learning Method , The Scientific Temper: Vol. 16 No. 11 (2025): The Scientific Temper
- V Babydeepa, K. Sindhu, A hybrid feature selection and generative adversarial network for lung and uterus cancer prediction with big data , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- R. Sakthiraman, L. Arockiam, RRFSE: RNN biased random forest and SVM ensemble for RPL DDoS in IoT-WSN environment , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- R. Sakthiraman, L. Arockiam, RFSVMDD: Ensemble of multi-dimension random forest and custom-made support vector machine for detecting RPL DDoS attacks in an IoT-based WSN environment , The Scientific Temper: Vol. 16 No. 03 (2025): The Scientific Temper
- N. Sasirekha, R. Anitha, Vanathi T, Umarani Balakrishnan, Automatic liver tumor segmentation from CT images using random forest algorithm , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- 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
<< < 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 Bisection Method , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
