Add to ORKGThe popular QR algorithm for solving all eigenvalues of an unsymmetric matrix is reviewed. Among the basic components in the QR algorithm, it was concluded from this study, that the reduction of an unsymmetric matrix to a Hessenberg form (before applying the QR algorithm itself) can be done effectively by exploiting the vector speed and multiple processors offered by modern high-performance computers. Numerical examples of several test cases have indicated that the proposed parallel-vector algorithm for converting a given unsymmetric matrix to a Hessenberg form offers computational advantages over the existing algorithm. The time saving obtained by the proposed methods is increased as the problem size increased
This paper models data use in the Unsymmetric QR Eigenvalue Algorithm to improve performance on ma...
[EN] Practical implementation of the QR algorithm: how every linear algebra software library compute...
In many scientific applications, eigenvalues of a matrix have to be computed. By first reducing a ma...
We describe two techniques for speeding up eigenvalue and singular value computations on shared memo...
A novel variant of the parallel QR algorithm for solving dense nonsymmetric eigenvalue problems on h...
In the nonsymmetric eigenvalue problem, work has focused on the Hessenberg reduction and QR iteratio...
AbstractThis paper describes modifications to many of the standard algorithms used in computing eige...
The LR and QR algorithms, two of the best available iterative methods for finding the eigenvalues of...
In this paper, we present an algorithm for the reduction to block upper-Hessenberg form which can be...
One approach to solving the nonsymmetric eigenvalue problem in parallel is to parallelize the QR alg...
AbstractThe design and analysis of time-invariant linear control systems give rise to a variety of i...
In this paper a parallel implementation of the QR algorithm for the eigenvalues of a non-Hermitian m...
Communicated by Yasuaki Ito Solution of large-scale dense nonsymmetric eigenvalue problem is require...
AbstractIn the year 2000 the dominant method for solving matrix eigenvalue problems is still the QR ...
In this paper, we present an algorithm for the reduction to block upper-Hessenberg form which can be...
This paper models data use in the Unsymmetric QR Eigenvalue Algorithm to improve performance on ma...
[EN] Practical implementation of the QR algorithm: how every linear algebra software library compute...
In many scientific applications, eigenvalues of a matrix have to be computed. By first reducing a ma...
We describe two techniques for speeding up eigenvalue and singular value computations on shared memo...
A novel variant of the parallel QR algorithm for solving dense nonsymmetric eigenvalue problems on h...
In the nonsymmetric eigenvalue problem, work has focused on the Hessenberg reduction and QR iteratio...
AbstractThis paper describes modifications to many of the standard algorithms used in computing eige...
The LR and QR algorithms, two of the best available iterative methods for finding the eigenvalues of...
In this paper, we present an algorithm for the reduction to block upper-Hessenberg form which can be...
One approach to solving the nonsymmetric eigenvalue problem in parallel is to parallelize the QR alg...
AbstractThe design and analysis of time-invariant linear control systems give rise to a variety of i...
In this paper a parallel implementation of the QR algorithm for the eigenvalues of a non-Hermitian m...
Communicated by Yasuaki Ito Solution of large-scale dense nonsymmetric eigenvalue problem is require...
AbstractIn the year 2000 the dominant method for solving matrix eigenvalue problems is still the QR ...
In this paper, we present an algorithm for the reduction to block upper-Hessenberg form which can be...
This paper models data use in the Unsymmetric QR Eigenvalue Algorithm to improve performance on ma...
[EN] Practical implementation of the QR algorithm: how every linear algebra software library compute...
In many scientific applications, eigenvalues of a matrix have to be computed. By first reducing a ma...