The Eigenvalue class is used to find the eigenvalues of an 'n' by 'n' matrix
'a' using an iterative technique that applies similarity transformations to
convert 'a' into an upper triangular matrix, so that the eigenvalues appear
along the diagonal. To improve performance, the 'a' matrix is first reduced
to Hessenburg form. During the iterative steps, a shifted QR decomposition
is performed.
Caveats: (i) it will not handle eigenvalues that are complex numbers,
(ii) it uses a simple shifting strategy that may slow convergence.
The
Eigenvalue
class is used to find the eigenvalues of an 'n' by 'n' matrix 'a' using an iterative technique that applies similarity transformations to convert 'a' into an upper triangular matrix, so that the eigenvalues appear along the diagonal. To improve performance, the 'a' matrix is first reduced to Hessenburg form. During the iterative steps, a shifted QR decomposition is performed. Caveats: (i) it will not handle eigenvalues that are complex numbers, (ii) it uses a simple shifting strategy that may slow convergence.