非线性方程系统最优四阶收敛的类似牛顿的两点法

IF 1.8 2区 数学 Q1 MATHEMATICS
Harmandeep Singh , Janak Raj Sharma
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引用次数: 0

摘要

本文提出了一种类似牛顿的两步法来高效求解非线性方程组。所提出的迭代方案将牛顿方案扩展到下一步,即加权牛顿迭代,显示出最佳的第四阶收敛性。制定该方法的首要目标是保持尽可能高的计算效率。在此背景下,我们采用系统方法对效率分析进行了深入研究,并将新方法的效率指数与复杂度相当的现有方法进行了比较。通过数值实验研究了所开发方法的计算效率。结果表明,与现有方法相比,新方法具有更高的效率和数值精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A two-point Newton-like method of optimal fourth order convergence for systems of nonlinear equations
A two-step Newton-like method is proposed to efficiently solve the systems of nonlinear equations. Extending Newton scheme to a next step as weighted-Newton iteration, the proposed iteration scheme shows optimal fourth order of convergence. The primary objective in formulating the method is to keep the computational efficiency as high as possible. In this context, the efficiency analysis is thoroughly examined using a systematic approach, wherein the efficiency index of the new method is compared with those of existing methods of comparable complexity. Numerical experimentation is performed to investigate the computational efficacy of the developed method. Results indicate higher efficiency and numerical precision in comparison to the existing counterparts.
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来源期刊
Journal of Complexity
Journal of Complexity 工程技术-计算机:理论方法
CiteScore
3.10
自引率
17.60%
发文量
57
审稿时长
>12 weeks
期刊介绍: The multidisciplinary Journal of Complexity publishes original research papers that contain substantial mathematical results on complexity as broadly conceived. Outstanding review papers will also be published. In the area of computational complexity, the focus is on complexity over the reals, with the emphasis on lower bounds and optimal algorithms. The Journal of Complexity also publishes articles that provide major new algorithms or make important progress on upper bounds. Other models of computation, such as the Turing machine model, are also of interest. Computational complexity results in a wide variety of areas are solicited. Areas Include: • Approximation theory • Biomedical computing • Compressed computing and sensing • Computational finance • Computational number theory • Computational stochastics • Control theory • Cryptography • Design of experiments • Differential equations • Discrete problems • Distributed and parallel computation • High and infinite-dimensional problems • Information-based complexity • Inverse and ill-posed problems • Machine learning • Markov chain Monte Carlo • Monte Carlo and quasi-Monte Carlo • Multivariate integration and approximation • Noisy data • Nonlinear and algebraic equations • Numerical analysis • Operator equations • Optimization • Quantum computing • Scientific computation • Tractability of multivariate problems • Vision and image understanding.
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