Memory and complexity reduction in parahermitian matrix manipulations of PEVD algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

4 Citations (Scopus)

Abstract

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.
LanguageEnglish
Title of host publication2016 24th European Signal Processing Conference
Place of PublicationPiscataway
PublisherIEEE
Pages1633-1637
Number of pages5
ISBN (Print)9780992862657
DOIs
Publication statusPublished - 1 Dec 2016
Event24th European Signal Processing Conference - Hilton Budapest, Budapest, Hungary
Duration: 29 Aug 20162 Sep 2016
http://www.eusipco2016.org/

Publication series

Name
ISSN (Electronic)2076-1465

Conference

Conference24th European Signal Processing Conference
Abbreviated titleEUSIPCO'16
CountryHungary
CityBudapest
Period29/08/162/09/16
Internet address

Fingerprint

Polynomials
Data storage equipment
Curbs
Trimming
Decomposition

Keywords

  • iterative calculation
  • polynomial matrices
  • polynomial matrix eigenvalue decomposition
  • parahermitian matrix
  • broadband multichannel problems
  • narrowband
  • diagonalization
  • diagonalisation

Cite this

Coutts, F. K., Corr, J., Thompson, K., Weiss, S., Proudler, I. K., & McWhirter, J. G. (2016). Memory and complexity reduction in parahermitian matrix manipulations of PEVD algorithms. In 2016 24th European Signal Processing Conference (pp. 1633-1637). Piscataway: IEEE. https://doi.org/10.1109/EUSIPCO.2016.7760525
Coutts, Fraser K. ; Corr, Jamie ; Thompson, Keith ; Weiss, Stephan ; Proudler, Ian K. ; McWhirter, John G. . / Memory and complexity reduction in parahermitian matrix manipulations of PEVD algorithms. 2016 24th European Signal Processing Conference. Piscataway : IEEE, 2016. pp. 1633-1637
@inproceedings{232227b0e63a421094d65e933ac5e535,
title = "Memory and complexity reduction in parahermitian matrix manipulations of PEVD algorithms",
abstract = "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.",
keywords = "iterative calculation, polynomial matrices, polynomial matrix eigenvalue decomposition, parahermitian matrix, broadband multichannel problems, narrowband, diagonalization, diagonalisation",
author = "Coutts, {Fraser K.} and Jamie Corr and Keith Thompson and Stephan Weiss and Proudler, {Ian K.} and McWhirter, {John G.}",
note = "{\circledC} 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
year = "2016",
month = "12",
day = "1",
doi = "10.1109/EUSIPCO.2016.7760525",
language = "English",
isbn = "9780992862657",
publisher = "IEEE",
pages = "1633--1637",
booktitle = "2016 24th European Signal Processing Conference",

}

Coutts, FK, Corr, J, Thompson, K, Weiss, S, Proudler, IK & McWhirter, JG 2016, Memory and complexity reduction in parahermitian matrix manipulations of PEVD algorithms. in 2016 24th European Signal Processing Conference. IEEE, Piscataway, pp. 1633-1637, 24th European Signal Processing Conference, Budapest, Hungary, 29/08/16. https://doi.org/10.1109/EUSIPCO.2016.7760525

Memory and complexity reduction in parahermitian matrix manipulations of PEVD algorithms. / Coutts, Fraser K.; Corr, Jamie; Thompson, Keith; Weiss, Stephan; Proudler, Ian K.; McWhirter, John G. .

2016 24th European Signal Processing Conference. Piscataway : IEEE, 2016. p. 1633-1637.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

TY - GEN

T1 - Memory and complexity reduction in parahermitian matrix manipulations of PEVD algorithms

AU - Coutts, Fraser K.

AU - Corr, Jamie

AU - Thompson, Keith

AU - Weiss, Stephan

AU - Proudler, Ian K.

AU - McWhirter, John G.

N1 - © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2016/12/1

Y1 - 2016/12/1

N2 - 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.

AB - 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.

KW - iterative calculation

KW - polynomial matrices

KW - polynomial matrix eigenvalue decomposition

KW - parahermitian matrix

KW - broadband multichannel problems

KW - narrowband

KW - diagonalization

KW - diagonalisation

UR - http://ieeexplore.ieee.org/servlet/opac?punumber=1801907

UR - http://www.eusipco2016.org/

U2 - 10.1109/EUSIPCO.2016.7760525

DO - 10.1109/EUSIPCO.2016.7760525

M3 - Conference contribution book

SN - 9780992862657

SP - 1633

EP - 1637

BT - 2016 24th European Signal Processing Conference

PB - IEEE

CY - Piscataway

ER -