Multiple shift maximum element sequential matrix diagonalisation for parahermitian matrices

Author

Corr, Jamie ; Thompson, Keith ; Weiss, Steven ; McWhirter, John G. ; Redif, Soydan ; Proudler, Ian K.

Author_Institution

Dept. of Electron. & Electr. Eng., Univ. of Strathclyde, Glasgow, UK

fYear

2014

fDate

June 29 2014-July 2 2014

Firstpage

312

Lastpage

315

Abstract

A polynomial eigenvalue decomposition of paraher-mitian matrices can be calculated approximately using iterative approaches such as the sequential matrix diagonalisation (SMD) algorithm. In this paper, we present an improved SMD algorithm which, compared to existing SMD approaches, eliminates more off-diagonal energy per step. This leads to faster convergence while incurring only a marginal increase in complexity. We motivate the approach, prove its convergence, and demonstrate some results that underline the algorithm´s performance.

Keywords

convergence of numerical methods; eigenvalues and eigenfunctions; iterative methods; matrix decomposition; polynomial matrices; Parahermitian polynomial matrices; SMD algorithm; convergence; iterative approaches; multiple shift maximum element sequential matrix diagonalisation; off-diagonal energy per step; polynomial eigenvalue decomposition; Approximation algorithms; Convergence; Educational institutions; Jacobian matrices; Matrix decomposition; Polynomials; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing (SSP), 2014 IEEE Workshop on

Conference_Location

Gold Coast, VIC

Type

conf

DOI

10.1109/SSP.2014.6884638

Filename

6884638