Memory and complexity reduction in parahermitian matrix manipulations of PEVD algorithms

Fraser K. Coutts, Jamie Corr, Keith Thompson, Stephan Weiss, Ian K. Proudler, John G. McWhirter

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

5 Citations (Scopus)
50 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference
Place of PublicationPiscataway
Number of pages5
ISBN (Print)9780992862657
Publication statusPublished - 1 Dec 2016
Event24th European Signal Processing Conference - Hilton Budapest, Budapest, Hungary
Duration: 29 Aug 20162 Sep 2016

Publication series

ISSN (Electronic)2076-1465


Conference24th European Signal Processing Conference
Abbreviated titleEUSIPCO'16
Internet address


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


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