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arXiv math/0508099math.NAcs.NAmath-phmath.MP
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从谱数据重构三对角矩阵

Reconstruction of tridiagonal matrices from spectral data

Ricardo S. Leite, Nicolau C. Saldanha, Carlos Tomei

AI总结:

提出基于双对角坐标的三对角矩阵重构方法,作为de Boor-Golub算法的替代,在速度和精度上表现良好。

摘要:

Jacobi矩阵由其特征值和规范化常数(归一化特征向量的第一坐标)参数化:该坐标系在可约三对角矩阵处失效。具有给定简单谱的实对称三对角矩阵集合是一个紧流形,它有一个由开稠密集${\cal U}^π_Λ$构成的开覆盖,这些开集以对角矩阵$Λ^π$为中心,其中$π$遍历所有排列。双对角坐标是规范化常数的一种变体,它用欧几里得空间参数化每个开集${\cal U}^π_Λ$。从逆数据重构Jacobi矩阵通常由de Boor和Golub引入的算法执行。本文提出了一种基于双对角坐标的重构过程,并展示了如何将其用作de Boor-Golub算法的替代方案。逆双对角算法在速度和精度方面表现良好。

英文摘要:

Jacobi matrices are parametrized by their eigenvalues and norming constants (first coordinates of normalized eigenvectors): this coordinate system breaks down at reducible tridiagonal matrices. The set of real symmetric tridiagonal matrices with prescribed simple spectrum is a compact manifold, admitting an open covering by open dense sets ${\cal U}^π_Λ$ centered at diagonal matrices $Λ^π$, where $π$ spans the permutations. {\it Bidiagonal coordinates} are a variant of norming constants which parametrize each open set ${\cal U}^π_Λ$ by the Euclidean space. The reconstruction of a Jacobi matrix from inverse data is usually performed by an algorithm introduced by de Boor and Golub. In this paper we present a reconstruction procedure from bidiagonal coordinates and show how to employ it as an alternative to the de Boor-Golub algorithm. The inverse bidiagonal algorithm rates well in terms of speed and accuracy.

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