计算伯努利多项式的矩阵生成函数对向量的作用,并应用于非局部边值问题

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED
Lidia Aceto, Luca Gemignani
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引用次数: 0

摘要

当a是一个大而稀疏的矩阵时,本文讨论了计算伯努利多项式的矩阵生成函数\(q(\tau ,A)\)在向量上的作用的有效数值方法。在求解一些非局部边值问题时,会出现这种问题。基于\(q(\tau ,w)\)的傅里叶展开的方法已经在科学文献中得到了解决。本文的贡献是双重的。首先,我们将这些方法置于加速傅里叶级数的Krylov-Lanczos(多项式-有理)技术的经典框架中。这允许我们将在这种情况下得到的收敛结果应用到我们的函数中。其次,我们设计了一种新的加速方案。数值结果表明了所提算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computing the action of the matrix generating function of Bernoulli polynomials on a vector with an application to non-local boundary value problems

This paper deals with efficient numerical methods for computing the action of the matrix generating function of Bernoulli polynomials, say \(q(\tau ,A)\), on a vector when A is a large and sparse matrix. This problem occurs when solving some non-local boundary value problems. Methods based on the Fourier expansion of \(q(\tau ,w)\) have already been addressed in the scientific literature. The contribution of this paper is twofold. First, we place these methods in the classical framework of Krylov-Lanczos (polynomial-rational) techniques for accelerating Fourier series. This allows us to apply the convergence results developed in this context to our function. Second, we design a new acceleration scheme. Some numerical results are presented to show the effectiveness of the proposed algorithms.

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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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