Restricted update sequential matrix diagonalisation for parahermitian matrices

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

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Abstract

A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is an extension of the ordinary EVD to polynomial matrices and will diagonalise a parahermitian matrix using paraunitary operations. This paper introduces a novel restricted update approach for the sequential matrix diagonalisation (SMD) PEVD algorithm, which can be implemented with minimal impact on algorithm accuracy and convergence. We demonstrate that by using the proposed restricted update SMD (RU-SMD) algorithm instead of SMD, PEVD complexity and execution time can be significantly reduced. This reduction impacts on a number of broadband multichannel problems.
Original languageEnglish
Number of pages5
Publication statusPublished - 10 Dec 2017
EventIEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Curacao, Netherlands Antilles
Duration: 10 Dec 201713 Dec 2017
http://www.cs.huji.ac.il/conferences/CAMSAP17/

Conference

ConferenceIEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated titleCAMSAP
CountryNetherlands Antilles
CityCuracao
Period10/12/1713/12/17
Internet address

Keywords

  • polynomial matrix eigenvalue decompositions
  • PEVD
  • polynomial matrices
  • parahermitian matrices
  • broadband multichannel problems

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    Coutts, F. K., Thompson, K., Proudler, I. K., & Weiss, S. (2017). Restricted update sequential matrix diagonalisation for parahermitian matrices. Paper presented at IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Curacao, Netherlands Antilles.