Abstract
Nonlinear phenomena occur in various fields of science, business, and engineering. Research in the area of computational science is constantly growing, with the development of new numerical schemes or with the modification of existing ones. However, such numerical schemes, objectively need to be computationally inexpensive with a higher order of convergence. Taking into account these demanding features, this article attempted to develop a new three-step numerical scheme to solve nonlinear scalar and vector equations. The scheme was shown to have ninth order convergence and requires six function evaluations per iteration. The efficiency index is approximately 1.4422, which is higher than the Newton’s scheme and several other known optimal schemes. Its dependence on the initial estimates was studied by using real multidimensional dynamical schemes, showing its stable behavior when tested upon some nonlinear models. Based on absolute errors, the number of iterations, the number of function evaluations, preassigned tolerance, convergence speed, and CPU time (sec), comparisons with well-known optimal schemes available in the literature showed a better performance of the proposed scheme. Practical models under consideration include open-channel flow in civil engineering, Planck’s radiation law in physics, the van der Waals equation in chemistry, and the steady-state of the Lorenz system in meteorology.
Highlights
Introduction with regard to jurisdictional claims in Applied mathematics, biological sciences, and engineering disciplines are full of systems and processes whose response is not proportional to the input, thereby demanding numerical schemes with a high order of convergence and a reasonable computational cost
When someone thinks of solving a nonlinear equation with a numerical scheme, the first scheme that comes to mind is the classical Newton–Raphson [5] which has optimal
The coefficient of n shows the number of function evaluations, the coefficient of n2 shows the number of first-order derivatives, the coefficient of n3 shows the number of second-order derivatives, and the coefficient of n4 is for the number of third-order derivatives; one per each iteration step
Summary
Ninth-Order Root-Finding Method with Error Analysis and Basins of Attraction. Mathematics 2021, 9, 1996.
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