Complexity and search space reduction in cyclic-by-row PEVD algorithms

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

Research output: Contribution to conferencePaperpeer-review

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In recent years, several 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 uses paraunitary operations to diagonalise a parahermitian matrix. This paper addresses potential computational savings that can be applied to existing cyclic-by-row approaches for the PEVD. These savings are found during the search and rotation stages, and do not significantly impact on algorithm accuracy. We demonstrate that with the proposed techniques, computations can be significantly reduced. The benefits of this are important for a number of broadband multichannel problems.
Original languageEnglish
Number of pages5
Publication statusAccepted/In press - 19 Jul 2016
Event50th Asilomar Conference on Signals, Systems and Computers - Asilomar Conference Ground, Pacific Grove, CA, United States
Duration: 6 Nov 20169 Nov 2016


Conference50th Asilomar Conference on Signals, Systems and Computers
Abbreviated titleAsilomar 2016
Country/TerritoryUnited States
CityPacific Grove, CA
Internet address


  • polynomial matrix eigenvalue decomposition
  • PEVD
  • space-time covariance matrices
  • algorithmic cost reductions
  • SMDCbR
  • sequential matrix diagonalisation


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