Cyclic-by-row approximation of iterative polynomial EVD algorithms

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

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

4 Citations (Scopus)
70 Downloads (Pure)

Abstract

A recent class of sequential matrix diagonalisation (SMD) algorithms have been demonstrated to provide a fast converging solution to iteratively approximating the polynomial eigenvalue decomposition of a parahermitian matrix. However, the calculation of an EVD, and the application of a full unitary matrix to every time lag of the parahermitian matrix in the SMD algorithm results in a high numerical cost. In this paper, we replace the EVD with a limited number of Givens rotations forming a cyclic-by-row Jacobi sweep. Simulations indicate that a considerable reduction in computational complexity compared to SMD can be achieved with a negligible sacrifice in diagonalisation performance, such that the benefits in applying the SMD are maintained.
Original languageEnglish
Title of host publicationSensor Signal Processing for Defence (SSPD), 2014
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Print)978-1-4799-5294-6
DOIs
Publication statusPublished - Sep 2014
Event2014 Sensor Signal Processing for Defence - Scotland, Edinburgh, United Kingdom
Duration: 8 Sep 20149 Sep 2014

Conference

Conference2014 Sensor Signal Processing for Defence
CountryUnited Kingdom
CityEdinburgh
Period8/09/149/09/14

Keywords

  • computational complexity reduction
  • Jacobi sweep
  • sequential matrix diagonalisation algorithms
  • eigenvalues and eigenfunctions
  • iterative methods
  • signal processing

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