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Memory and complexity reduction in parahermitian matrix manipulations of PEVD algorithms

机译:PEVD算法的准herheritian矩阵处理中的内存和复杂性降低

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摘要

A number of algorithms for the iterative calculation of a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is a generalisation of the ordinary EVD and will diagonalise a parahermitian matrix via paraunitary operations. This paper addresses savings — both computationally and in terms of memory use — that exploit the parahermitian structure of the matrix being decomposed, and also suggests an implicit trimming approach to efficiently curb the polynomial order growth usually observed during iterations of the PEVD algorithms. We demonstrate that with the proposed techniques, both storage and computations can be significantly reduced, impacting on a number of broadband multichannel problems.
机译:引入了许多用于迭代计算多项式矩阵特征值分解(PEVD)的算法。 PEVD是普通EVD的概括,它将通过超unit运算对角化一个准Hermitian矩阵。本文探讨了在计算和内存使用方面的节余,这些节余利用了要分解的矩阵的准herheritian结构,并提出了一种隐式修整方法来有效地抑制通常在PEVD算法迭代期间观察到的多项式阶数增长。我们证明,利用所提出的技术,可以显着减少存储和计算量,从而影响许多宽带多通道问题。

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