e-ISSN 2231-8534
ISSN 0128-7702
Yeong Lin Koay, Hong Seng Sim, Yong Kheng Goh and Sing Yee Chua
Pertanika Journal of Social Science and Humanities, Volume 30, Issue 3, July 2022
DOI: https://doi.org/10.47836/pjst.30.3.05
Keywords: Jacobian, log-determinant norm, nonlinear systems, optimization, spectral gradient method
Published on: 25 May 2022
Solving a system of non-linear equations has always been a complex issue whereby various methods were carried out. However, most of the methods used are optimization-based methods. This paper has modified the spectral gradient method with the backtracking line search technique to solve the non-linear systems. The efficiency of the modified spectral gradient method is tested by comparing the number of iterations, the number of function calls, and computational time with some existing methods. As a result, the proposed method shows better performance and gives more stable results than some existing methods. Moreover, it can be useful in solving some non-linear application problems. Therefore, the proposed method can be considered an alternative for solving non-linear systems.
Abubakar, A. B., Kumam, P., & Mohammad, H. (2020). A note on the spectral gradient projection method for nonlinear monotone equations with applications. Computational and Applied Mathematics, 39(2), 1-35. https://doi.org/10.1007/s40314-020-01151-5
Andrei, N. (2008). An unconstrained optimization test functions collection. Advanced Modeling and Optimization, 10(1), 147-161.
Antonelli, L., De Simone, V., & Di Serafino, D. (2016). On the application of the spectral projected gradient method in image segmentation. Journal of Mathematical Imaging and Vision, 54(1), 106-116. https://doi.org/10.1007/s10851-015-0591-y
Barzilai, J., & Borwein, J. M. (1988). Two-point step size gradient methods. IMA Journal of Numerical Analysis, 8(1), 141-148. https://doi.org/10.1093/imanum/8.1.141
Biglari, F., & Solimanpur, M. (2013). Scaling on the spectral gradient method. Journal of Optimization Theory and Applications, 158(2), 626-635. https://doi.org/10.1007/s10957-012-0265-5
Broyden, C. G. (1965). A class of methods for solving nonlinear simultaneous equations. Mathematics of Computation, 19(92), 577-593.
Buzzi-Ferraris, G., & Manenti, F. (2013). Nonlinear systems and optimization for the chemical engineer: Solving numerical problems. John Wiley & Sons.
Byrd, R. H., & Nocedal, J. (1989). A tool for the analysis of quasi-Newton methods with application to unconstrained minimization. SIAM Journal of Numerical Analysis, 26, 727-739. https://doi.org/10.1137/0726042
Cauchy, A. (1847). Méthode générale pour la résolution des systemes d’équations simultanées [General method for solving systems of simultaneous equations]. Comptes rendus de l’Académie des Sciences, 25(1847), 536-538.
Chen, X., Liu, Y., Zhou, W., & Peng, X. (2017). Simplex-fruit fly optimization algorithm for solving systems of non-linear equations. In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (pp. 615-620). IEEE Publishing. https://doi.org/10.1109/FSKD.2017.8393341
Cheng, W. Y., & Li, D. H. (2010). Spectral scaling BFGS method. Journal of Optimization Theory and Applications, 146(2), 305-319. https://doi.org/10.1007/s10957-010-9652-y
Cruz, W. L., & Raydan, M. (2003). Nonmonotone spectral methods for large-scale nonlinear systems. Optimization Methods and Software, 18(5), 583-599. https://doi.org/10.1080/10556780310001610493
Dai, Y. H., Hager, W. W., Schittkowski, K., & Zhang, H. (2006). The cyclic Barzilai-Borwein method for unconstrained optimization. IMA Journal of Numerical Analysis, 26(3), 604-627. https://doi.org/10.1093/imanum/drl006
De Asmundis, R., di Serafino, D., Riccio, F., & Toraldo, G. (2013). On spectral properties of steepest descent methods. IMA Journal of Numerical Analysis, 33(4), 1416-1435. https://doi.org/10.1093/imanum/drs056
Dolan, E. D., & Moré, J. J. (2002). Benchmarking optimization software with performance profiles. Mathematical Programming, 91(2), 201-213. https://doi.org/10.1007/s101070100263
Fang, X., Ni, Q., & Zeng, M. (2018). A modified quasi-Newton method for nonlinear equations. Journal of Computational and Applied Mathematics, 328, 44-58. https://doi.org/10.1016/j.cam.2017.06.024
Grosan, C., & Abraham, A. (2008). A new approach for solving nonlinear equations systems. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 38(3), 698-714. https://doi.org/10.1109/TSMCA.2008.918599
Hestenes, M. R., & Stiefel, E. (1952). Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards, 49(6), 409-436.
Ibrahim, S. M., Yakubu, U. A., & Mamat, M. (2020). Application of spectral conjugate gradient methods for solving unconstrained optimization problems. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 10(2), 198-205. https://doi.org/10.11121/ijocta.01.2020.00859
Luenberger, D. G., & Ye, Y. (1984). Linear and nonlinear programming (Vol. 2). Addison-Wesley.
Marini, F. (2009). Neural networks. In R. Tauler & B. Walczak (Eds.), Comprehensive Chemometrics: Chemical and Biochemical Data Analysis (pp. 477-505). Elsevier. https://doi.org/10.1016/B978-044452701-1.00128-9
Martinez, J. M. (2000). Practical quasi-Newton methods for solving nonlinear systems. Journal of Computational and Applied Mathematics, 124(1-2), 97-121. https://doi.org/10.1016/S0377-0427(00)00434-9
Raydan, M. (1993). On the Barzilai and Borwein choice of steplength for the gradient method. IMA Journal of Numerical Analysis, 13(3), 321-326. https://doi.org/10.1093/imanum/13.3.321
Raydan, M. (1997). The Barzilai and Borwein gradient method for the large scale unconstrained minimization problem. SIAM Journal on Optimization, 7(1), 26-33. https://doi.org/10.1137/S1052623494266365
Raydan, M., & Svaiter, B. F. (2002). Relaxed steepest descent and Cauchy-Barzilai-Borwein method. Computational Optimization and Applications, 21(2), 155-167. https://doi.org/10.1023/A:1013708715892
Sim, H. S., Leong, W. J., & Chen, C. Y. (2019). Gradient method with multiple damping for large-scale unconstrained optimization. Optimization Letters, 13(3), 617-632. https://doi.org/10.1007/s11590-018-1247-9
Simpson, T. (1740). Essays on several curious and useful subjects, in speculative and mix’d mathematics. London, 1740, Article 81.
Solodov, M. V., & Svaiter, B. F. (1998). A globally convergent inexact Newton method for systems of monotone equations. In Reformulation: Nonsmooth, piecewise smooth, semismooth and smoothing methods (pp. 355-369). Springer. https://doi.org/10.1007/978-1-4757-6388-1_18
Turgut, O. E., Turgut, M. S., & Coban, M. T. (2014). Chaotic quantum behaved particle swarm optimization algorithm for solving nonlinear system of equations. Computers & Mathematics with Applications, 68(4), 508-530. https://doi.org/10.1016/j.camwa.2014.06.013
Wallis, J. (1095). A treatise of algebra, both historical and practical. Philosophical Transactions of the Royal Society of London, 15(173), 1095-1106. https://doi.org/10.3931/e-rara-8842
Xiao, Y., Wang, Q., & Wang, D. (2010). Notes on the Dai–Yuan–Yuan modified spectral gradient method. Journal of Computational and Applied Mathematics, 234(10), 2986-2992. https://doi.org/10.1016/j.cam.2010.04.012
Yuan, G., & Lu, X. (2008). A new backtracking inexact BFGS method for symmetric nonlinear equations. Computers & Mathematics with Applications, 55(1), 116-129. https://doi.org/10.1016/j.camwa.2006.12.081
Zhang, L., & Zhou, W. (2006). Spectral gradient projection method for solving nonlinear monotone equations. Journal of Computational and Applied Mathematics, 196(2), 478-484. https://doi.org/10.1016/j.cam.2005.10.002
ISSN 0128-7702
e-ISSN 2231-8534
Recent Articles