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
- 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
- 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
- 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
- P S Renjeni, B Senthilkumaran, Ramalingam Sugumar, L. Jaya Singh Dhas, Gaussian kernelized transformer learning model for brain tumor risk factor identification and disease diagnosis , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- M. A. Shanti, Optimizing predictive accuracy: A comparative study of feature selection strategies in the healthcare domain , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Aishwarya Jha, Jyoti Gangta, Neha Kapur, Comparison of anterior corneal aberrometry, keratometry and pupil size with Scheimpflug tomography and ray tracing aberrometer in moderate and high refractive error , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Kanthalakshmi S, Nikitha M. S, Pradeepa G, Classification of weld defects using machine vision using convolutional neural network , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Nikhil Kumar, Namita Kumar, Numerical Response of Campoletis chloridae Uchida (Hymenoptera: Ichneumonidae), a parasitoid of Heliothis armigera (Hubner) (Lepidoptera : Noctuidae) , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
- Nisha Patil, Archana Bhise, Rajesh K. Tiwari, Fusion deep learning with pre-post harvest quality management of grapes within the realm of supply chain management , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Rajesh Kumar Singh, Abhishek Kumar Mishra, Ramapati Mishra, Hand Gesture Identification for Improving Accuracy Using Convolutional Neural Network(CNN) , The Scientific Temper: Vol. 13 No. 02 (2022): 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
