Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS | Approximation algorithm,Mathematical optimization,Polynomial matrix,Generalization,Matrix (mathematics),Computer science,Matrix decomposition,Algorithm,Eigendecomposition of a matrix,Sparse matrix,Computation |
DocType | ISSN | Citations |
Conference | 1058-6393 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fraser K. Coutts | 1 | 8 | 4.31 |
| Jamie Corr | 2 | 11 | 2.78 |
| Keith Thompson | 3 | 7 | 3.31 |
| Weiss, Stephan | 4 | 209 | 33.25 |
| Ian K. Proudler | 5 | 63 | 12.78 |
| John G. McWhirter | 6 | 214 | 28.77 |