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Iterative Approximation of Analytic Eigenvalues of a Parahermitian Matrix EVD

机译:帕尔米特矩阵EVD的解析特征值的迭代逼近

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We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix. Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost association between bins via a maximum likelihood sequence detection driven by a smoothness criterion. An outer iteration continues until a desired accuracy for the approximation of the extracted eigenvalues has been achieved. The approach is compared to existing algorithms.
机译:我们提出了一种算法,该算法从准herheritian矩阵中提取解析特征值。在离散傅立叶变换域中操作,内部迭代通过由平滑度准则驱动的最大似然序列检测,重新建立仓之间的丢失关联。外部迭代将继续进行,直到达到所提取特征值近似值的所需精度为止。该方法与现有算法进行了比较。

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