求解有内存和无内存非线性方程的高效参数迭代方案

IF 1.8 2区 数学 Q1 MATHEMATICS
Raziyeh Erfanifar, Masoud Hajarian
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引用次数: 0

摘要

许多实际问题,如马尔萨斯人口增长模型、矩阵的特征值计算以及范德瓦耳斯理想气体方程的求解,本质上都涉及非线性问题。本文首先介绍了收敛阶数为 2 的双参数迭代方案。在此基础上,提出了收敛阶数为四的三参数方案。然后,我们利用牛顿插值法将这些方案扩展为具有内存的高阶方案,实现了 7.8874813≈1.99057 的效率指数上限。最后,我们通过求解各种数值和实际例子验证了新方案,证明与现有方法相比,它们在计算成本、CPU 时间和精度方面都具有更高的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-efficiency parametric iterative schemes for solving nonlinear equations with and without memory

Many practical problems, such as the Malthusian population growth model, eigenvalue computations for matrices, and solving the Van der Waals' ideal gas equation, inherently involve nonlinearities. This paper initially introduces a two-parameter iterative scheme with a convergence order of two. Building on this, a three-parameter scheme with a convergence order of four is proposed. Then we extend these schemes into higher-order schemes with memory using Newton's interpolation, achieving an upper bound for the efficiency index of 7.88748131.99057. Finally, we validate the new schemes by solving various numerical and practical examples, demonstrating their superior efficiency in terms of computational cost, CPU time, and accuracy compared to existing methods.

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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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