Projects per year
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.
Original language | English |
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Pages | 312-315 |
Number of pages | 4 |
DOIs | |
Publication status | Published - Jul 2014 |
Event | 2014 IEEE Workshop on Statistical Signal Processing (SSP) - Australia, Gold Coast, United Kingdom Duration: 29 Jun 2014 → 2 Jul 2014 |
Conference
Conference | 2014 IEEE Workshop on Statistical Signal Processing (SSP) |
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Country/Territory | United Kingdom |
City | Gold Coast |
Period | 29/06/14 → 2/07/14 |
Keywords
- parahermitian polynomial matrices
- SMD algorithm
- polynomial eigenvalue decomposition
Fingerprint
Dive into the research topics of 'Multiple shift maximum element sequential matrix diagonalisation for parahermitian matrices'. Together they form a unique fingerprint.Projects
- 1 Finished
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Signal Processing Solutions for the Networked Battlespace
Soraghan, J. (Principal Investigator) & Weiss, S. (Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/04/13 → 31/03/18
Project: Research
Activities
- 1 Participation in conference
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2014 IEEE Statistical Signal Processing Workshop
Corr, J. (Participant)
29 Jun 2014 → 2 Jul 2014Activity: Participating in or organising an event types › Participation in conference