On a new three-step class of methods and its acceleration for nonlinear equations

关于一种新的三步法及其在非线性方程求解中的加速方法

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Abstract

A class of derivative-free methods without memory for approximating a simple zero of a nonlinear equation is presented. The proposed class uses four function evaluations per iteration with convergence order eight. Therefore, it is an optimal three-step scheme without memory based on Kung-Traub conjecture. Moreover, the proposed class has an accelerator parameter with the property that it can increase the convergence rate from eight to twelve without any new functional evaluations. Thus, we construct a with memory method that increases considerably efficiency index from 8(1/4) ≈ 1.681 to 12(1/4) ≈ 1.861. Illustrations are also included to support the underlying theory.

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