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arXiv 1208.4906math.NAcs.NA
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高相对精度下对称三对角矩阵特征向量小元素的计算

Evaluation of small elements of the eigenvectors of certain symmetric tridiagonal matrices with high relative accuracy

Yale University(耶鲁大学)

Andrei Osipov

AI总结:

本文研究在特定条件下,对称三对角矩阵特征向量中极小坐标(如10^{-50})可高相对精度计算的条件,提出改进算法并进行误差分析。

摘要:

对称三对角矩阵特征向量的计算是数值线性代数中最基本的任务之一。众所周知,在特征值分离良好的情况下,特征向量可以高相对精度计算。然而,通常特征向量的每个坐标只能达到高$绝对$精度。特别是,那些量级低于机器精度的坐标预计无法以任何精度计算。 事实证明,在应用中经常遇到的某些条件下,对称三对角矩阵特征向量的极小(例如$10^{-50}$)坐标可以高$相对$精度计算。在本文中,我们研究了这些条件,进行了分析,并描述了由此产生的数值方案。虽然我们的方案可以看作是对现有(且众所周知)数值算法的修改,但相关的误差分析似乎是新的。 我们的结果通过几个数值例子进行了说明。

英文摘要:

Evaluation of the eigenvectors of symmetric tridiagonal matrices is one of the most basic tasks in numerical linear algebra. It is a widely known fact that, in the case of well separated eigenvalues, the eigenvectors can be evaluated with high relative accuracy. Nevertheless, in general, each coordinate of the eigenvector is evaluated with only high $absolute$ accuracy. In particular, those coordinates whose magnitude is below the machine precision are not expected to be evaluated with any accuracy whatsoever. It turns out that, under certain conditions, frequently ecountered in applications, small (e.g. $10^{-50}$) coordinates of eigenvectors of symmetric tridiagonal matrices can be evaluated with high $relative$ accuracy. In this paper, we investigate such conditions, carry out the analysis, and describe the resulting numerical schemes. While our schemes can be viewed as a modification of already existing (and well known) numerical algorithms, the related error analysis appears to be new. Our results are illustrated via several numerical examples.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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