An efficient family of Steffensen-type methods with memory for solving systems of nonlinear equations

IF 0.9 Q3 MATHEMATICS, APPLIED Computational and Mathematical Methods Pub Date : 2021-09-22 DOI:10.1002/cmm4.1192
Mona Narang, Saurabh Bhatia, Vinay Kanwar
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引用次数: 0

Abstract

The article discusses derivative-free algorithms with and without memory for solving numerically nonlinear systems. We proposed a family of fifth- and sixth-order schemes and extended them to algorithms with memory. We further discuss the convergence and computational efficiency of these algorithms. Numerical examples of mixed Hammerstein integral equation, discrete nonlinear ordinary differential equation, and Fisher's partial differential equation with Neumann's boundary conditions are discussed to demonstrate the convergence and efficiency of these schemes. Finally, some numerical results are included to examine the performance of the developed methods.

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一类求解非线性方程组的具有记忆的steffensen型有效方法
本文讨论了求解数值非线性系统的有内存和无内存的无导数算法。我们提出了一组五阶和六阶格式,并将它们扩展到具有内存的算法中。进一步讨论了这些算法的收敛性和计算效率。讨论了具有Neumann边界条件的混合Hammerstein积分方程、离散非线性常微分方程和Fisher偏微分方程的数值算例,证明了这些格式的收敛性和有效性。最后,给出了一些数值结果来检验所开发方法的性能。
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