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
- Dimpal Khambhati, Chirag Patel, Analyzing cardiac physiology: ECG ensemble averaging and morphological features under treadmill-induced stress in LabVIEW , The Scientific Temper: Vol. 16 No. 07 (2025): The Scientific Temper
- G. Hemamalini, V. Maniraj, Enhanced otpmization based support vector machine classification approach for the detection of knee arthritis , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- A. Kamatchi, Dr. V. Maniraj, An early classification of Alzheimer’s Disease with deep Features using Advanced Deep Learning Method (Graph Convolutional Neural Networks) , The Scientific Temper: Vol. 17 No. 03 (2026): The Scientific Temper
- Vimala S, G. Arockia Sahaya Sheela, Label-Aware Imputation with Cluster Refinement for Smartphone Usage Analytics in Educational Institutions , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- N. Saranya, M. Kalpana Devi, A. Mythili, Summia P. H, Data science and machine learning methods for detecting credit card fraud , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Shaik Abdulla P., Abdul Razak T., Retrieval-Based Inception V3-Net Algorithm and Invariant Data Classification using Enhanced Deep Belief Networks for Content-Based Image Retrieval , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- R. Rita Jenifer, V. Sinthu Janita, Energy-aware Security Optimized Elliptic Curve Digital Signature Algorithm for Universal IoT Networks , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- Anita M, Shakila S, Stochastic kernelized discriminant extreme learning machine classifier for big data predictive analytics , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Shaik Chanbasha, N. Jayakumar, N. Bupesh Kumar, A self-regulating optimization algorithm for locating and sizing a local power generation source for a radial structured distribution system in deregulated environment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Neerav Nishant, Nisha Rathore, Vinay Kumar Nassa, Vijay Kumar Dwivedi, Thulasimani T, Surrya Prakash Dillibabu, Integrating machine learning and mathematical programming for efficient optimization of electric discharge machining technique , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
<< < 3 4 5 6 7 8 9 10 11 12 > >>
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
