Accurate bidiagonal decomposition of Lagrange–Vandermonde matrices and applications

IF 1.8 3区 数学 Q1 MATHEMATICS
A. Marco, José‐Javier Martínez, Raquel Viaña
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引用次数: 1

Abstract

Lagrange–Vandermonde matrices are the collocation matrices corresponding to Lagrange‐type bases, obtained by removing the denominators from each element of a Lagrange basis. It is proved that, provided the nodes required to create the Lagrange‐type basis and the corresponding collocation matrix are properly ordered, such matrices are strictly totally positive. A fast algorithm to compute the bidiagonal decomposition of these matrices to high relative accuracy is presented. As an application, the problems of eigenvalue computation, linear system solving and inverse computation are solved in an efficient and accurate way for this type of matrices. Moreover, the proposed algorithms allow to solve fastly and to high relative accuracy some of the cited problems when the involved matrices are collocation matrices corresponding to the standard Lagrange basis, although such collocation matrices are not totally positive. Numerical experiments illustrating the good performance of our approach are also included.
拉格朗日-范德蒙德矩阵的精确双对角分解及其应用
拉格朗日-范德蒙德矩阵是与拉格朗日型基对应的搭配矩阵,通过去除拉格朗日基的每个元素的分母得到。证明了如果创建拉格朗日型基所需的节点和相应的搭配矩阵是适当有序的,则该矩阵是严格全正的。本文提出了一种计算这些矩阵的双对角分解的快速算法,使其具有较高的相对精度。作为一种应用,对这类矩阵的特征值计算、线性系统求解和逆计算等问题进行了有效而准确的求解。此外,当所涉及的矩阵是与标准拉格朗日基对应的搭配矩阵时,所提出的算法可以快速且相对精度高地解决一些被引用的问题,尽管这种搭配矩阵并不完全是正的。数值实验也证明了该方法的良好性能。
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
3.40
自引率
2.30%
发文量
50
审稿时长
12 months
期刊介绍: Manuscripts submitted to Numerical Linear Algebra with Applications should include large-scale broad-interest applications in which challenging computational results are integral to the approach investigated and analysed. Manuscripts that, in the Editor’s view, do not satisfy these conditions will not be accepted for review. Numerical Linear Algebra with Applications receives submissions in areas that address developing, analysing and applying linear algebra algorithms for solving problems arising in multilinear (tensor) algebra, in statistics, such as Markov Chains, as well as in deterministic and stochastic modelling of large-scale networks, algorithm development, performance analysis or related computational aspects. Topics covered include: Standard and Generalized Conjugate Gradients, Multigrid and Other Iterative Methods; Preconditioning Methods; Direct Solution Methods; Numerical Methods for Eigenproblems; Newton-like Methods for Nonlinear Equations; Parallel and Vectorizable Algorithms in Numerical Linear Algebra; Application of Methods of Numerical Linear Algebra in Science, Engineering and Economics.
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