Divide-and-conquer sequential matrix diagonalisation for parahermitian matrices

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

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A number of algorithms capable of iteratively calculating 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. Inspired by the existence of low complexity divide-and-conquer solutions to eigenproblems, this paper addresses a divide-and-conquer approach to the PEVD utilising the sequential matrix diagonalisation (SMD) algorithm. We demonstrate that with the proposed techniques, encapsulated in a novel algorithm titled divide-and-conquer sequential matrix diagonalisation (DC-SMD), algorithm complexity can be significantly reduced. This reduction impacts on a number of broadband multichannel problems, including those involving large arrays.
Original languageEnglish
Number of pages5
Publication statusAccepted/In press - 6 Sept 2017
EventIEEE Sensor Signal Processing in Defence Conference - London, London, United Kingdom
Duration: 6 Dec 20177 Dec 2017


ConferenceIEEE Sensor Signal Processing in Defence Conference
Abbreviated titleSSPD'17
Country/TerritoryUnited Kingdom
Internet address


  • polynomial matrices
  • polynomial matrix eigenvalue decomposition
  • parahermitian matrices


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