偏对称微分qd算法

Sanja Singer, Saša Singer
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引用次数: 3

摘要

带移位的微分qd (dqd)算法可能是目前已知的计算对称三对角矩阵特征值最快的算法,具有较高的相对精度。在本文中,我们将构造一个计算偏对称矩阵特征值的类似算法,该算法基于QR分解和辛QR分解的隐式使用。将此算法应用于三对角线偏对称矩阵,得到偏对称dqd算法。该算法具有较高的相对稳定性。然而,在对称情况下,结合位移要困难得多,而且还没有实现。最后,计算三对角线偏对称矩阵特征值的标准算法也可以在偏对称dqd算法的背景下解释。(©2005 WILEY-VCH Verlag GmbH &KGaA公司,Weinheim)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Skew–Symmetric Differential qd Algorithm

Differential qd (dqd) algorithm with shifts is probably the fastest known algorithm which computes eigenvalues of symmetric tridiagonal matrices with high relative accuracy.

In this paper we will construct a similar algorithm for computing eigenvalues of skew-symmetric matrices, which is based on implicit usage of both the QR and the symplectic QR factorizations. If we apply this algorithm to tridiagonal skew-symmetric matrices, we obtain the skew-symmetric dqd algorithm. This algorithm also enjoys high relative stability. However, incorporation of shifts is much harder then in the symmetric case, and yet to be implemented.

Finally, the standard algorithm for computing the eigenvalues of tridiagonal skew-symmetric matrices can also be interpreted in the context of the skew-symmetric dqd algorithm. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

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