Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition

Abstract: 

The Second-order Sequential Best Rotation (SBR2) algorithm, used for Eigenvalue Decomposition (EVD) on para-Hermitian polynomial matrices typically encountered in wideband signal processing applications like multichannel Wiener filtering and channel coding, involves a series of delay and rotation operations to achieve diagonalisation. In this paper, we proposed the use of Householder transformations to reduce polynomial matrices to tridiagonal form before zeroing the dominant element with rotation. Similar to performing Householder reduction on conventional matrices, our method enables SBR2 to converge in fewer iterations with smaller order of polynomial matrix factors because more off-diagonal Frobenius norm (F-norm) could be transferred to the main diagonal at every iteration. A reduction in the number of iterations by 12.35% and 0.1% improvement in reconstruction error is achievable.

up
0 users have voted:

Paper Details

Authors:
Vincent W. Neo, Patrick A. Naylor
Submitted On:
10 May 2019 - 12:30pm
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Vincent W. Neo
Paper Code:
2886
Document Year:
2019
Cite

Document Files

SBR2HT_Neo2019.pdf

(16)

Subscribe

[1] Vincent W. Neo, Patrick A. Naylor, "Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4356. Accessed: Jul. 22, 2019.
@article{4356-19,
url = {http://sigport.org/4356},
author = {Vincent W. Neo; Patrick A. Naylor },
publisher = {IEEE SigPort},
title = {Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition},
year = {2019} }
TY - EJOUR
T1 - Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition
AU - Vincent W. Neo; Patrick A. Naylor
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4356
ER -
Vincent W. Neo, Patrick A. Naylor. (2019). Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition. IEEE SigPort. http://sigport.org/4356
Vincent W. Neo, Patrick A. Naylor, 2019. Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition. Available at: http://sigport.org/4356.
Vincent W. Neo, Patrick A. Naylor. (2019). "Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition." Web.
1. Vincent W. Neo, Patrick A. Naylor. Second order sequential best rotation algorithm with Householder reduction for polynomial matrix eigenvalue decomposition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4356