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  3. Vol. 11, No. 3, August 2026 (Article in Progress)
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Vol. 11, No. 3, August 2026 (Article in Progress)

Issue Published : Jun 4, 2026
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Accuracy Comparison of Multivariate Newton-Raphson, Newton-Kantorovich, and Levenberg–Marquardt Methods for Solving Nonlinear Systems Using Numerical Simulation

https://doi.org/10.22219/kinetik.v11i3.2603
Syaharuddin Syaharuddin
Universitas Muhammadiyah Mataram
Hendi Hidayah
Universitas Muhammadiyah Mataram
Vera Mandailina
Universitas Muhammadiyah Mataram
Saba Mehmood
University of Management and Technology
Wasim Raza
Universidade Federal Do Rio de Janeiro

Corresponding Author(s) : Syaharuddin Syaharuddin

syaharuddin.ntb@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 3, August 2026 (Article in Progress)
Article Published : Jun 7, 2026

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Abstract

Multivariable nonlinear equation systems often appear in engineering, physics, economics, and artificial intelligence modeling, but often do not have closed analytical solutions. Therefore, accurate, efficient, and stable numerical methods are needed. This study aims to comparatively evaluate three iterative methods, namely Multivariate Newton-Raphson, Newton-Kantorovich, and Levenberg–Marquardt, in solving identical high-complexity multivariable nonlinear systems. Simulations were performed using MATLAB with an error tolerance of 0.001 and a maximum iteration limit of 100. The test system consisted of a combination of trigonometric, exponential, and polynomial functions, resulting in nonlinear interactions that were challenging for each method. The simulation results show that Levenberg–Marquardt excelled with only 6 iterations and a final error of 3.246 × 10⁻¹⁰, indicating high stability and efficiency, followed by Multivariate Newton-Raphson with 13 iterations and an error of 4.606 × 10⁻⁹, while Newton-Kantorovich requires 27 iterations with an error of 5.770 × 10⁻⁷, reflecting slower semi-local corrections.Three-dimensional visualization shows the intersection point of the surface as a solution, providing an intuitive understanding of the iteration trajectory characteristics of each method. The novelty of this research lies in the integrated numerical simulation framework that allows direct quantitative comparison of the three methods on identical systems with the same initial conditions, tolerance, and iteration limits. These findings provide important empirical references for selecting efficient and stable iterative methods for multivariable nonlinear systems, as well as practical guidance for numerical applications in engineering, physics, and scientific computing.

Keywords

Numerical Methods Multivariate Newton Rapshon Newton Kontrovich Levenberg-Marquardt Nonlinear Equation Systems
Syaharuddin, S., Hidayah, H., Mandailina, V., Mehmood, S., & Raza, W. (2026). Accuracy Comparison of Multivariate Newton-Raphson, Newton-Kantorovich, and Levenberg–Marquardt Methods for Solving Nonlinear Systems Using Numerical Simulation. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(3). https://doi.org/10.22219/kinetik.v11i3.2603
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References


M. Shakeel, S. T. Mohyud-Din, and M. A. Iqbal, “Modified extended exp-function method for a system of nonlinear partial differential equations defined by seismic sea waves,” Pramana - J. Phys., vol. 91, no. 2, pp. 1–8, 2018. DOI: 10.1007/s12043-018-1601-6

J. Ríos-Ocampo and M. S. Gary, “Using analytical equations to represent nonlinear relationships,” Syst. Dyn. Rev., vol. 38, no. 4, pp. 354–370, 2022. https://doi.org/10.1002/sdr.1718

L. Zakaria, A. Eka, and D. Aziz, “Penyelesaian Sistem Persamaan Fully Fuzzy Non Linear Menggunakan Metode Newton Raphson Ganda,” J. Math. Theory Appl., vol. 5, no. 2, pp. 67–73, 2023. https://doi.org/10.31605/jomta.v5i2.2876

M. Putri and S. Syaharuddin, “Implementations of Open and Closed Method Numerically: A Non-linear Equations Solution Convergence Test,” IJECA (International J. Educ. Curric. Appl., vol. 2, no. 2, p. 1, 2019. https://doi.org/10.31764/ijeca.v2i2.2041

P. Batarius, J. San Juan, and P. Kupang, “Perbandingan Metode Newton-Raphson Modifikasi Dan Metode Secant Modifikasi Dalam Penentuan Akar Persamaan,” Semin. Nas. Ris. dan Teknol. Terap., vol. 8, no. 2, pp. 53–63, 2018. https://doi.org/10.21067/pmej.v1i3.2784

M. Adriana, “on the Convergence of the Newton-Raphson Method and Some of Its Generalizations,” Bull. Transilv. Univ. Brasov, Ser. III Math. Comput. Sci., vol. 4, no. 2, pp. 215–224, 2024. https://doi.org/10.31926/but.mif.2024.4.66.2.13

Inderjeet and R. Bhardwaj, “A new Iterative Newton Raphson technique for the numerical simulation of Nonlinear Equations,” J. Integr. Sci. Technol., vol. 13, no. 4, pp. 1–6, 2025. https://doi.org/10.62110/sciencein.jist.2025.v13.1080

R. Behl, “Highly efficient family of iterative methods for solving nonlinear models,” J. Comput. Appl. Math., vol. 346, no. 2, pp. 110–132, 2019. https://doi.org/10.1016/j.cam.2018.06.042

M. Mohammad Ali, “Numerical differential continuation approach for systems of nonlinear equations with singular Jacobian,” AUT J. Math. Comput., vol. 3, no. 1, pp. 53–58, 2022. https://doi.org/10.22060/ajmc.2021.20487.1068

A. Hasanudin, “Calculation of Loan Amount When Cooperatives Do Not Make Profits with Non-Linear Equation Method Using Secant and Its Implementation with MATLAB,” J. Penelit. Mat. dan Pendidik. Mat., vol. 8, no. 1, pp. 17–22, 2023. https://doi.org/10.26486/jm.v8i1.4020

S. Regmi, I. K. Argyros, S. George, and M. I. Argyros, “Developments on the Convergence Analysis of Newton-Kantorovich Method for Solving Nonlinear Equations,” Eur. J. Math. Anal., vol. 3, no. 11, p. 15, 2023. https://doi.org/10.28924/ada/ma.3.15

A. Heuermann, P. Hannebohm, M. Schäfer, and B. Bachmann, “Accelerating the simulation of equation-based models by replacing non-linear algebraic loops with error-controlled machine learning surrogates,” Proc. 15th Int. Model. Conf., vol. 204, no. 11, pp. 275–284, 2023. https://doi.org/10.3384/ecp204275

L. Zheng, L. Chen, and Y. Ma, “A variant of the Levenberg-Marquardt method with adaptive parameters for systems of nonlinear equations,” Mathematics, vol. 7, no. 1, pp. 1241–1256, 2021. doi: 10.3934/math.2022073

J. Ritonga and D. Suryana, “Perbandingan Kecepatan Konvergensi Akar Persamaan Non Linier Metode Titik Tetap dengan Metode Newton Raphson Menggunakan Matlab,” Inf. (Jurnal Inform. dan Sist. Informasi), vol. 11, no. 2, pp. 51–64, 2019. https://doi.org/10.37424/informasi.v11i2.17

Y. Han and S. Rui, “SS symmetry A New Adaptive Levenberg – Marquardt Method for Nonlinear Equations and Its Convergence Rate under the Hölderian Local,” symmetry S Artic., vol. 16, no. 6, pp. 1–19, 2024. https://doi.org/10.3390/sym16060674

A. K. Venkatesan and S. K. Natarajan, “Stability Enhancement of PV Powered Microgrid using Levenberg-Marquardt Algorithm Based Intelligent Controller Under Grid-connected Mode,” Distrib. Gener. Altern. Energy J., vol. 37, no. 2, pp. 361–380, 2021. https://doi.org/10.13052/dgaej2156-3306.37214

H. Mustafidah, “Performance of Levenberg-Marquardt Algorithm in Backpropagation Network Based on the Number of Neurons in Hidden Layers and Learning Rate,” JUITA J. Inform. e-ISSN, vol. 8, no. 1, pp. 29–35, 2020. https://doi.org/10.30595/juita.v8i1.7150

Yudhi, Devitriani, Mariatul Kiftiah, “Analisis Metode Newton-Raphson Ganda Orde Konvergensi Empat Dalam Menyelesaikan Sistem Persamaan Nonlinear,” Bimaster Bul. Ilm. Mat. Stat. dan Ter., vol. 8, no. 2, pp. 213–220, 2019. https://doi.org/10.26418/bbimst.v8i2.31648

E. Sharma, S. Panday, S. K. Mittal, D. M. Joița, L. L. Pruteanu, and L. Jäntschi, “Derivative-Free Families of With- and Without-Memory Iterative Methods for Solving Nonlinear Equations and Their Engineering Applications,” Mathematics, vol. 11, no. 21, pp. 1–13, 2023. https://doi.org/10.3390/math11214512

O. Paulina Maure and H. Tulan, “Studi Komparasi Beberapa Metode Numerik Dalam Mengaproksimasi Akar-Akar Persamaan Non Linear,” Asimtot J. Kependidikan Mat., vol. 6, no. 01, pp. 1–12, 2024. https://doi.org/10.30822/asimtot.v6i01.4008

N. K. Vitanov, “Simple Equations Method (SEsM): An Effective Algorithm for Obtaining Exact Solutions of Nonlinear Differential Equations,” Entropy, vol. 24, no. 11, pp. 1–55, 2022. https://doi.org/10.3390/e24111653

H. A. H. Abugirda, K. S. Al-Yasiri, and M. K. Abdullah, “Newton-Kantorovich Method for Solving One of the Non-Linear Sturm-Liouville Problems,” Baghdad Sci. J., vol. 20, no. 3, pp. 2036–2041, 2023. https://doi.org/10.21123/bsj.2023.7543

S. Regmi, I. K. Argyros, S. George, and J. Warden, “A Unified Kantorovich-type Convergence Analysis of Newton-like Methods for Solving Generalized Equations under the Aubin Property,” Eur. J. Math. Anal., vol. 4, no. 3, pp. 1-10. 2024. https://doi.org/10.28924/ada/ma.4.3

A. G. Kamel, E. H. Haraz, and S. N. Hanna, “Numerical Simulation of Channel Flow Over a Skewed Equilateral Cavity,” J. Appl. Math. Comput. Mech., vol. 19, no. 3, pp. 29–43, 2020. DOI: 10.17512/jamcm.2020.3.03

M. Herlina, F. Tukan, O. P. Maure, K. Trisnawati, and D. Ina, “Analisis keakuratan metode numerik dalam menyelesaikan turunan persamaan nonlinier,” Leibniz J. Mat., vol. 04, no. 2, pp. 10–22, 2024. https://doi.org/10.59632/leibniz.v4i02.446

A. Fischer, A. F. Izmailov, and M. V Solodov, “The Levenberg – Marquardt method : An overview of modern convergence theories and more,” Comput. Optim. Appl., vol. 89, no. 1, pp. 33–42, 2020. DOI: 10.1007/s10589-024-00589-1

N. U. Qadir, “Influence of Principal Component Analysis as a Data Conditioning Approach for Training Multilayer Feedforward Neural Networks with Exact Form of Levenberg-Marquardt Algorithm,” Global Journal of Technology & Optimization., vol. 11, no. 1, pp. 1–13, 2020. DOI: 10.37421/gjto.2020.11.239

S. Regmi, I. K. Argyros, S. George, and J. Warden, “A Unified Kantorovich-type Convergence Analysis of Newton-like Methods for Solving Generalized Equations under the Aubin Property,” Eur. J. Math. Anal., vol. 4, no. 3, pp. 1–10, 2024.https://doi.org/10.28924/ada/ma.4.3

F. Ahmad et al., “A preconditioned iterative method for solving systems of nonlinear equations having unknown multiplicity,” Algorithms, vol. 10, no. 1, pp. 1–9, 2017. https://doi.org/10.3390/a10010017

M. Farman, A. Akgül, N. Alshaikh, M. Azeem, and J. Asad, “Fractional-Order Newton–Raphson Method for Nonlinear Equation With Convergence and Stability Analyses,” Fractals, vol. 31, no. 10, pp. 1–12, 2023. https://doi.org/10.1142/S0218348X23400790

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