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New Numerical Algorithm for Deflation of Infinite and Zero Eigenvalues and Full Solution of Quadratic Eigenvalue Problems

机译:无限和零特征值通气的新数值算法及二次特征值问题的完整解

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This article presents a new method for computing all eigenvalues and eigenvectors of quadratic matrix pencil Q(lambda) = lambda M-2 + lambda C + K. It is an upgrade of the quadeig algorithm by Hammarlinget al., which attempts to reveal and remove by deflation a certain number of zero and infinite eigenvalues before QZ iterations. Proposed modifications of the quadeig framework are designed to enhance backward stability and to make the process of deflating infinite and zero eigenvalues more numerically robust. In particular, careful preprocessing allows scaling invariant/component-wise backward error and thus a better condition number. Further, using an upper triangular version of the Kronecker canonical form enables deflating additional infinite eigenvalues, in addition to those inferred from the rank of M. Theoretical analysis and empirical evidence from thorough testing of the software implementation confirm superior numerical performances of the proposed method.
机译:本文介绍了计算二次矩阵Q(Lambda)= Lambda M-2 + Lambda C + K的所有特征值和特征向量的新方法。它是Hammarlinget al的Quadeig算法的升级,这试图揭示和删除通过通货紧缩在QZ迭代之前的一定数量的零和无限的特征值。拟议的Quadeig框架的修改旨在增强向后稳定性,并使流动无限和零特征值更加鲁棒的过程。特别地,仔细的预处理允许缩放不变/分量 - 方向误差,因此是更好的条件号。此外,除了从M.从M.推断的那些推断出从彻底测试软件实施的理论分析和经验证据的那些,使用额外的无限特征值,使用额外的无限特征值,可以使用额外的无限特征值。

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