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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.
|Number of pages||4|
|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||2014 IEEE Workshop on Statistical Signal Processing (SSP)|
|Period||29/06/14 → 2/07/14|
- parahermitian polynomial matrices
- SMD algorithm
- polynomial eigenvalue decomposition
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- 1 Finished
Soraghan, J. & Weiss, S.
1/04/13 → 31/03/18