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Accuracy Comparison of Multivariate Newton-Raphson, Newton-Kantorovich, and Levenberg–Marquardt Methods for Solving Nonlinear Systems Using Numerical Simulation
Corresponding Author(s) : Syaharuddin Syaharuddin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 3, August 2026 (Article in Progress)
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.
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https://doi.org/10.1007/s40304-017-0117-6
A. O. Umar, “On damping parameters of Levenberg-Marquardt algorithm for nonlinear least square problems On damping parameters of Levenberg-Marquardt algorithm for nonlinear least square problems,” Electrochem. Soc., vol. 20, no. 1, pp. 1–10, 2021. DOI: 10.1088/1742-6596/1734/1/012018
M. A. S. Tabbu and A. Makkawaru, “Prediction of Sediment Yield Using the Algoritma Prediction of Sediment Yield Using the Algoritma Lavenberg-Marquardt,” J. Phys. Conf. Ser. Pap., vol. 21, no. 1, pp. 1–8, 2021. DOI: 10.1088/1742-6596/2123/1/012037
J. R. Sharma and H. Arora, “Improved Newton-like methods for solving systems of nonlinear equations,” SeMA J., vol. 74, no. 2, pp. 147–163, 2017. https://doi.org/10.1515/nleng-2017-0105
F. Ahmad, “Multi-step derivative-free preconditioned Newton method for solving systems of nonlinear equations,” SeMA J., vol. 75, no. 1, pp. 45–56, 2018, DOI: 10.1007/s40324-017-0120-6
V. Mandailina, D. Pramita, M. Ibrahim, and H. R. Perwira, “DESIMAL : JURNAL MATEMATIKA Wilkinson Polynomials : Accuracy Analysis Based on Numerical Methods of the Taylor Series Derivative,” Desimal J. Mat., vol. 3, no. 2, pp. 155–160, 2020. https://doi.org/10.24042/djm.v3i2.6134
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