Support estimation of analytic eigenvectors of parahermitian matrices

Faizan Khattak; Ian K. Proudler; Stephan Weiss

Support estimation of analytic eigenvectors of parahermitian matrices

Faizan Khattak, Ian K. Proudler, Stephan Weiss

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

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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 language	English
Pages	1-6
Number of pages	6
Publication status	Published - 20 Oct 2022
Event	International Conference on Recent Advances in Electrical Engineering and Computer Sciences - Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan Duration: 18 Oct 2022 → 20 Oct 2022 http://raeecs22.pieas.edu.pk/

Conference

Conference	International Conference on Recent Advances in Electrical Engineering and Computer Sciences
Abbreviated title	RAEE
Country/Territory	Pakistan
City	Islamabad
Period	18/10/22 → 20/10/22
Internet address	http://raeecs22.pieas.edu.pk/

Keywords

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

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Khattak-etal-ICRAEECS-2022-Support-estimation-of-analytic-eigenvectors-of-parahermitian-matricesAccepted author manuscript, 738 KBLicence: Strath_1

Cite this

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title = "Support estimation of analytic eigenvectors of parahermitian matrices",

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.",

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TY - CONF

T1 - Support estimation of analytic eigenvectors of parahermitian matrices

AU - Khattak, Faizan

AU - Proudler, Ian K.

AU - Weiss, Stephan

PY - 2022/10/20

Y1 - 2022/10/20

N2 - 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.

AB - 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.

KW - discrete Fourier transform (DFT)

KW - eigenvectors

KW - algorithms

KW - time-domain support

KW - computational load

M3 - Paper

SP - 1

EP - 6

T2 - International Conference on Recent Advances in Electrical Engineering and Computer Sciences

Y2 - 18 October 2022 through 20 October 2022

ER -

Support estimation of analytic eigenvectors of parahermitian matrices

Abstract

Conference

Keywords

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