Finding roots of complex analytic functions via generalized colleague matrices

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED
H. Zhang, V. Rokhlin
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引用次数: 0

Abstract

We present a scheme for finding all roots of an analytic function in a square domain in the complex plane. The scheme can be viewed as a generalization of the classical approach to finding roots of a function on the real line, by first approximating it by a polynomial in the Chebyshev basis, followed by diagonalizing the so-called “colleague matrices.” Our extension of the classical approach is based on several observations that enable the construction of polynomial bases in compact domains that satisfy three-term recurrences and are reasonably well-conditioned. This class of polynomial bases gives rise to “generalized colleague matrices,” whose eigenvalues are roots of functions expressed in these bases. In this paper, we also introduce a special-purpose QR algorithm for finding the eigenvalues of generalized colleague matrices, which is a straightforward extension of the recently introduced structured stable QR algorithm for the classical cases (see Serkh and Rokhlin 2021). The performance of the schemes is illustrated with several numerical examples.

通过广义同事矩阵寻找复解析函数的根
我们提出了一种在复平面的方域中寻找解析函数所有根的方法。该方案可以看作是对实线上函数根的经典求法的推广,即首先用切比雪夫基的多项式对其进行逼近,然后对所谓的 "同事矩阵 "进行对角。我们对经典方法的扩展基于一些观察结果,这些观察结果使我们能够在紧凑域中构建满足三项递归且条件合理的多项式基。这类多项式基产生了 "广义同事矩阵",其特征值是用这些基表达的函数的根。在本文中,我们还引入了一种特殊用途的 QR 算法,用于寻找广义同事矩阵的特征值,它是最近引入的经典情况下结构稳定 QR 算法的直接扩展(见 Serkh 和 Rokhlin,2021 年)。我们用几个数值示例来说明这些方案的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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