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

5 Citations (Scopus)
71 Downloads (Pure)


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
Number of pages5
ISBN (Print)978-1-4799-5294-6
Publication statusPublished - Sept 2014
Event2014 Sensor Signal Processing for Defence - Scotland, Edinburgh, United Kingdom
Duration: 8 Sept 20149 Sept 2014


Conference2014 Sensor Signal Processing for Defence
Country/TerritoryUnited Kingdom


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


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