Add to ORKGIn 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
In this paper, we show that the paraunitary (PU) matrices that arise from the polynomial eigenvalue ...
In this paper, we show that the paraunitary (PU) matrices that arise from the polynomial eigenvalue ...
A variety of algorithms have been developed to compute an approximate polynomial matrix eigenvalue d...
In recent years, several algorithms for the iterative calculation of a polynomial matrix eigenvalue ...
A number of algorithms for the iterative calculation of a polynomial matrix eigenvalue decomposition...
A recent class of sequential matrix diagonalisation (SMD) algorithms have been demonstrated to provi...
A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decompositi...
A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decompositi...
A number of algorithms are capable of iteratively calculating a polynomial matrix eigenvalue decompo...
In broadband array processing applications, an extension of the eigenvalue decomposition (EVD) to pa...
A variety of algorithms have been developed to compute an approximate polynomial matrix eigenvalue d...
The Multiple Shift Maximum Element Sequential Matrix Diagonalisation (MSME-SMD) algorithm is a power...
Polynomial parahermitian matrices can accurately and elegantly capture the space-time covariance in ...
This paper extends the analysis of the recently introduced row-shift corrected truncation method for...
For parahermitian polynomial matrices, which can be used, for example, to characterise space-time co...
In this paper, we show that the paraunitary (PU) matrices that arise from the polynomial eigenvalue ...
In this paper, we show that the paraunitary (PU) matrices that arise from the polynomial eigenvalue ...
A variety of algorithms have been developed to compute an approximate polynomial matrix eigenvalue d...
In recent years, several algorithms for the iterative calculation of a polynomial matrix eigenvalue ...
A number of algorithms for the iterative calculation of a polynomial matrix eigenvalue decomposition...
A recent class of sequential matrix diagonalisation (SMD) algorithms have been demonstrated to provi...
A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decompositi...
A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decompositi...
A number of algorithms are capable of iteratively calculating a polynomial matrix eigenvalue decompo...
In broadband array processing applications, an extension of the eigenvalue decomposition (EVD) to pa...
A variety of algorithms have been developed to compute an approximate polynomial matrix eigenvalue d...
The Multiple Shift Maximum Element Sequential Matrix Diagonalisation (MSME-SMD) algorithm is a power...
Polynomial parahermitian matrices can accurately and elegantly capture the space-time covariance in ...
This paper extends the analysis of the recently introduced row-shift corrected truncation method for...
For parahermitian polynomial matrices, which can be used, for example, to characterise space-time co...
In this paper, we show that the paraunitary (PU) matrices that arise from the polynomial eigenvalue ...
In this paper, we show that the paraunitary (PU) matrices that arise from the polynomial eigenvalue ...
A variety of algorithms have been developed to compute an approximate polynomial matrix eigenvalue d...