Abstract
| Language | English |
|---|---|
| Number of pages | 5 |
| Publication status | Accepted/In press - 19 Jul 2016 |
| Event | 50th Asilomar Conference on Signals, Systems and Computers - Asilomar Conference Ground, Pacific Grove, CA, United States Duration: 6 Nov 2016 → 9 Nov 2016 http://www.asilomarsscconf.org/ |
Conference
| Conference | 50th Asilomar Conference on Signals, Systems and Computers |
|---|---|
| Abbreviated title | Asilomar 2016 |
| Country | United States |
| City | Pacific Grove, CA |
| Period | 6/11/16 → 9/11/16 |
| Internet address |
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Keywords
- polynomial matrix eigenvalue decomposition
- PEVD
- space-time covariance matrices
- algorithmic cost reductions
- SMDCbR
- sequential matrix diagonalisation
Cite this
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Complexity and search space reduction in cyclic-by-row PEVD algorithms. / Coutts, Fraser K.; Corr, Jamie; Thompson, Keith; Weiss, Stephan; Proudler, Ian K.; McWhirter, John G. .
2016. Paper presented at 50th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States.Research output: Contribution to conference › Paper
TY - CONF
T1 - Complexity and search space reduction in cyclic-by-row PEVD algorithms
AU - Coutts, Fraser K.
AU - Corr, Jamie
AU - Thompson, Keith
AU - Weiss, Stephan
AU - Proudler, Ian K.
AU - McWhirter, John G.
PY - 2016/7/19
Y1 - 2016/7/19
N2 - In recent years, several algorithms for the iterative calculation of a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is a generalisation of the ordinary EVD and uses paraunitary operations to diagonalise a parahermitian matrix. This paper addresses potential computational savings that can be applied to existing cyclic-by-row approaches for the PEVD. These savings are found during the search and rotation stages, and do not significantly impact on algorithm accuracy. We demonstrate that with the proposed techniques, computations can be significantly reduced. The benefits of this are important for a number of broadband multichannel problems.
AB - In recent years, several algorithms for the iterative calculation of a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is a generalisation of the ordinary EVD and uses paraunitary operations to diagonalise a parahermitian matrix. This paper addresses potential computational savings that can be applied to existing cyclic-by-row approaches for the PEVD. These savings are found during the search and rotation stages, and do not significantly impact on algorithm accuracy. We demonstrate that with the proposed techniques, computations can be significantly reduced. The benefits of this are important for a number of broadband multichannel problems.
KW - polynomial matrix eigenvalue decomposition
KW - PEVD
KW - space-time covariance matrices
KW - algorithmic cost reductions
KW - SMDCbR
KW - sequential matrix diagonalisation
UR - http://www.asilomarsscconf.org/
M3 - Paper
ER -