Name
Abstract | ||
|---|---|---|
As an extension of the ordinary EVD to polynomial matrices, the polynomial matrix eigenvalue decomposition (PEVD) will generate paraunitary matrices that diagonalise a parahermitian matrix. Frequency-based PEVD algorithms have shown promise for the decomposition of problems of finite order, but require a priori knowledge of the length of the decomposition. This paper presents a novel iterative frequency-based PEVD algorithm which can compute an accurate decomposition without requiring this information. We demonstrate through the use of simulations that the algorithm can achieve superior performance over existing iterative PEVD methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACSSC.2018.8645226 | ACSSC |
Field | DocType | Citations |
Approximation algorithm,Applied mathematics,Mathematical optimization,Polynomial,Polynomial matrix,Matrix (mathematics),Computer science,Matrix decomposition,A priori and a posteriori,Eigendecomposition of a matrix | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fraser K. Coutts | 1 | 8 | 4.31 |
| Keith Thompson | 2 | 7 | 3.31 |
| Ian K. Proudler | 3 | 63 | 12.78 |
| Weiss, Stephan | 4 | 209 | 33.25 |