Support estimation of analytic eigenvectors of parahermitian matrices

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Abstract

Extracting analytic eigenvectors from parahermitian matrices relies on phase smoothing in the discrete Fourier transform (DFT) domain as its most expensive algorithmic component. Some algorithms require an a priori estimate of the eigenvector support and therefore the DFT length, while others iteratively increase the DFT. Thus in this document, we aim to complement the former and to reduce the computational load of the latter by estimating the time-domain support of eigenvectors. The proposed approach is validated via an ensemble of eigenvectors of known support, which the estimated support accurately matches.
Original languageEnglish
Pages1-6
Number of pages6
Publication statusPublished - 20 Oct 2022
EventInternational Conference on Recent Advances in Electrical Engineering and Computer Sciences - Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan
Duration: 18 Oct 202220 Oct 2022
http://raeecs22.pieas.edu.pk/

Conference

ConferenceInternational Conference on Recent Advances in Electrical Engineering and Computer Sciences
Abbreviated titleRAEE
Country/TerritoryPakistan
CityIslamabad
Period18/10/2220/10/22
Internet address

Keywords

  • discrete Fourier transform (DFT)
  • eigenvectors
  • algorithms
  • time-domain support
  • computational load

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