A New Algorithm for Finding the Roots of Nonlinear Algebraic Equations
DOI:
https://doi.org/10.21123/bsj.2024.7481Keywords:
Genetic algorithms, Nonlinear equations, Objective function, Optimizations, SGD algorithmAbstract
In this paper, the algorithm (Stochastic Gradient Descent) SGD, which is one of the most famous optimization algorithms, was hybridized with genetic algorithms in finding the roots of non-linear equations, which is one of the most important mathematical problems due to its application in all sciences. Genetic algorithms are used here to find the optimal primary root of SGD algorithm and its application in reducing the studied objective function. Some famous algorithms need initial point to reach the solution in terms of stability. The proposed algorithm is tested on several standard functions and the results are compared with the famous algorithms, and the results show the efficiency of the proposed algorithm through tables and figures.
Received 01/06/2022
Revised 25/02/2023
Accepted 27/02/2023
Published Online First 20/08/2024
References
Ricceri B A. class of equtions with three solutions. Mathematics. 2020;8(478): 1-8. https://doi.org/10.3390/math8040478.
Lu C, Shi J. Relative density prediction of additively manufactured Inconel 718: a study on genetic algorithm optimized neural network models. Rapid Prototyp J. 2022; 28(8): 1425-1436. https://doi.org/10.1007/s00170-021-08388-2 .
Ide N. A New Modified of McDougall-Wotherspoon Method for Solving Nonlinear Equations by Using Geometric Mean Concept. Comput. Appl Math Sci. 2019; 4(2): 35-38.
Rafiq N, Shams M, Ahmad B. Inverse Numerical Iterative Technique for Finding all Roots of Nonlinear Equations with Engineering Applications. J Math. 2021; (2): 1-10. https://doi.org/10.1155/2021/6643514.
Salman NK, Mustafa MM. Numerical Solution of Fractional Volterra-Fredholm Integro-Differential and Equation Using Lagrange Polynomials. Baghdad Sci J. 2020; 17(4): 1234-1240. http://dx.doi.org/10.21123/bsj.2020.17.4.1234
Qin X, Liu T, Li Q. An optimal fourth-order family of modified Cauchy methods for finding solutions of nonlinear equations and their dynamical behavior. Open Math. 2019; 17(1): 1567-1598. https://doi.org/10.1515/math-2019-0122.
Faez H, Riyam N. Using Evolving Algorithms to Cryptanalysis Nonlinear Cryptosystems. Baghdad Sci J. 2020; 17(2): 682-688. http://dx.doi.org/10.21123/bsj.2020.17.2(SI).0682
Holland JH. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. 2nd edition. USA: MIT Press; 1992. p. 211. https://ieeexplore.ieee.org/servlet/opac?bknumber=6267401.
Xie Z, Sato I, Sugiyama M. A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes from Sharp Minima Exponentially Fast. arXiv preprint arXiv:2002.03495. 2020 Feb 10; 1-28. https://openreview.net/pdf?id=wXgk_iCiYGo.
Leclerc G, Madry A. The Two Regimes of Deep Network Training. arXiv preprint arXiv:2002.10376. 2020 Feb 24; 1-14. https://arxiv.org/pdf/2002.10376.
Frontini M, Sormani E. Some variant of Newton’s method with third-order convergence. Appl Math Comput. 2003; 140: 419-426. https://doi.org/10.1016/S0096-3003(02)00238-2
Glisovic N, Ralevic NM, Cebic D. A variant of McDougall- Wotherspoon method for finding simple roots of nonlinear equations. Scientific Publications of the State University of Novi Pazar Ser A. Appl Math Inform Mech. 2018; 10(1): 55-61. https://doi.org/10.5937/SPSUNP1801055G
Li Y, Kou J, Wang X. A modification of Newton method with third-order convergence. Appl Math Comput. 2006; 181: 1106–1111. https://doi.org/10.1016/j.amc.2006.01.076.
Zalinescu C. On Berinde’s Method for Comparing Iterative Processes. Fixed Point Theory Algorithm Sci Eng. 2019; 10 https://doi.org/10.48550/arXiv.1812.00958
Downloads
Issue
Section
License
Copyright (c) 2024 Ahmad yousef Alrazzo, Nasr Al Din Ide, Mohammad Assaad
This work is licensed under a Creative Commons Attribution 4.0 International License.