Roden J. A. David

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We present an efficient implementation of the multi-shift QR algorithm for computing the eigenvalues of a unitary matrix. The algorithm can perform QR iterations of arbitrary degree, it is conceptually simple, and it is backward stable. 1. Introduction. We consider the eigenvalue problem for a unitary matrix U ∈ C n×n that is upper Hessenberg, i.e. u ij = 0(More)
481 signals and SNR's of as low as 09 dB (or less), for the individual sinusoids. As one may expect, the algorithm performance deteriorates as the noise level increases. In particular, we note that at low SNR's, the algorithm convergence slows down and the variation of the ALE parameters become more erratic, and at some stage, it may fail unless its step(More)
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