cupynumeric.linalg.eigh#

cupynumeric.linalg.eigh( a: ndarray, UPLO: str = 'L', ) → tuple[ndarray, ...]#

Compute the eigenvalues and right eigenvectors of a square array.

Parameters:

a ((..., M, M) array_like) – Matrices for which the eigenvalues and right eigenvectors will be computed, at least dimension 2.
{'L' (UPLO) – Specifies whether the calculation is done with the lower triangular part of a (‘L’, default) or the upper triangular part (‘U’). Irrespective of this value only the real parts of the diagonal will be considered in the computation to preserve the notion of a Hermitian matrix. It therefore follows that the imaginary part of the diagonal will always be treated as zero.
'U'} – Specifies whether the calculation is done with the lower triangular part of a (‘L’, default) or the upper triangular part (‘U’). Irrespective of this value only the real parts of the diagonal will be considered in the computation to preserve the notion of a Hermitian matrix. It therefore follows that the imaginary part of the diagonal will always be treated as zero.
optional – Specifies whether the calculation is done with the lower triangular part of a (‘L’, default) or the upper triangular part (‘U’). Irrespective of this value only the real parts of the diagonal will be considered in the computation to preserve the notion of a Hermitian matrix. It therefore follows that the imaginary part of the diagonal will always be treated as zero.

Returns:

eigenvalues ((…, M) array_like) – The eigenvalues in ascending order, each repeated according to its multiplicity.
eigenvectors ((…, M, M) array) – The normalized (unit “length”) eigenvectors, such that the column eigenvectors[:,i] is the eigenvector corresponding to the eigenvalue eigenvalues[i].

Raises:

LinAlgError – If the eigenvalue computation does not converge.

Notes

Multi-GPU/CPU usage is limited to data parallel matrix-wise batching.