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    Impact on stability by the use of memory in Traub-type schemes

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    [EN] In this work, two Traub-type methods with memory are introduced using accelerating parameters. To obtain schemes with memory, after the inclusion of these parameters in Traub's method, they have been designed using linear approximations or the Newton's interpolation polynomials. In both cases, the parameters use information from the current and the previous iterations, so they define a method with memory. Moreover, they achieve higher order of convergence than Traub's scheme without any additional functional evaluations. The real dynamical analysis verifies that the proposed methods with memory not only converge faster, but they are also more stable than the original scheme. The methods selected by means of this analysis can be applied for solving nonlinear problems with a wider set of initial estimations than their original partners. This fact also involves a lower number of iterations in the process.This research was partially supported by Ministerio de Ciencia, Innovacion y Universidades under grants PGC2018-095896-B-C22 (MCIU/AEI/FEDER/UE).Chicharro, FI.; Cordero Barbero, A.; Garrido, N.; Torregrosa Sánchez, JR. (2020). Impact on stability by the use of memory in Traub-type schemes. Mathematics. 8(2):1-16. https://doi.org/10.3390/math8020274S11682Shacham, M. (1989). An improved memory method for the solution of a nonlinear equation. Chemical Engineering Science, 44(7), 1495-1501. doi:10.1016/0009-2509(89)80026-0Balaji, G. V., & Seader, J. D. (1995). Application of interval Newton’s method to chemical engineering problems. Reliable Computing, 1(3), 215-223. doi:10.1007/bf02385253Shacham, M. (1986). Numerical solution of constrained non-linear algebraic equations. International Journal for Numerical Methods in Engineering, 23(8), 1455-1481. doi:10.1002/nme.1620230805Shacham, M., & Kehat, E. (1973). Converging interval methods for the iterative solution of a non-linear equation. Chemical Engineering Science, 28(12), 2187-2193. doi:10.1016/0009-2509(73)85008-0Amat, S., Busquier, S., & Plaza, S. (2010). Chaotic dynamics of a third-order Newton-type method. Journal of Mathematical Analysis and Applications, 366(1), 24-32. doi:10.1016/j.jmaa.2010.01.047Argyros, I. K., Cordero, A., Magreñán, Á. A., & Torregrosa, J. R. (2017). Third-degree anomalies of Traub’s method. Journal of Computational and Applied Mathematics, 309, 511-521. doi:10.1016/j.cam.2016.01.060Chicharro, F., Cordero, A., Gutiérrez, J. M., & Torregrosa, J. R. (2013). Complex dynamics of derivative-free methods for nonlinear equations. Applied Mathematics and Computation, 219(12), 7023-7035. doi:10.1016/j.amc.2012.12.075Chicharro, F., Cordero, A., & Torregrosa, J. (2015). Dynamics and Fractal Dimension of Steffensen-Type Methods. Algorithms, 8(2), 271-279. doi:10.3390/a8020271Scott, M., Neta, B., & Chun, C. (2011). Basin attractors for various methods. Applied Mathematics and Computation, 218(6), 2584-2599. doi:10.1016/j.amc.2011.07.076Steffensen, J. F. (1933). Remarks on iteration. Scandinavian Actuarial Journal, 1933(1), 64-72. doi:10.1080/03461238.1933.10419209Wang, X., & Zhang, T. (2012). A new family of Newton-type iterative methods with and without memory for solving nonlinear equations. Calcolo, 51(1), 1-15. doi:10.1007/s10092-012-0072-2Džunić, J., & Petković, M. S. (2014). On generalized biparametric multipoint root finding methods with memory. Journal of Computational and Applied Mathematics, 255, 362-375. doi:10.1016/j.cam.2013.05.013Petković, M. S., Neta, B., Petković, L. D., & Džunić, J. (2014). Multipoint methods for solving nonlinear equations: A survey. Applied Mathematics and Computation, 226, 635-660. doi:10.1016/j.amc.2013.10.072Campos, B., Cordero, A., Torregrosa, J. R., & Vindel, P. (2015). A multidimensional dynamical approach to iterative methods with memory. Applied Mathematics and Computation, 271, 701-715. doi:10.1016/j.amc.2015.09.056Chicharro, F. I., Cordero, A., & Torregrosa, J. R. (2019). Dynamics of iterative families with memory based on weight functions procedure. Journal of Computational and Applied Mathematics, 354, 286-298. doi:10.1016/j.cam.2018.01.019Chicharro, F. I., Cordero, A., Torregrosa, J. R., & Vassileva, M. P. (2017). King-Type Derivative-Free Iterative Families: Real and Memory Dynamics. Complexity, 2017, 1-15. doi:10.1155/2017/2713145Magreñán, Á. A., Cordero, A., Gutiérrez, J. M., & Torregrosa, J. R. (2014). Real qualitative behavior of a fourth-order family of iterative methods by using the convergence plane. Mathematics and Computers in Simulation, 105, 49-61. doi:10.1016/j.matcom.2014.04.006Blanchard, P. (1984). Complex analytic dynamics on the Riemann sphere. Bulletin of the American Mathematical Society, 11(1), 85-141. doi:10.1090/s0273-0979-1984-15240-6Magreñán, Á. A. (2014). A new tool to study real dynamics: The convergence plane. Applied Mathematics and Computation, 248, 215-224. doi:10.1016/j.amc.2014.09.061Chicharro, F. I., Cordero, A., & Torregrosa, J. R. (2013). Drawing Dynamical and Parameters Planes of Iterative Families and Methods. The Scientific World Journal, 2013, 1-11. doi:10.1155/2013/78015
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