numpy.linalg.eigvalsh¶

numpy.linalg.eigvalsh(a, UPLO='L')[source]¶

Compute the eigenvalues of a Hermitian or real symmetric matrix.

Main difference from eigh: the eigenvectors are not computed.

Parameters:

Parameters:	a : (..., M, M) array_like A complex- or real-valued matrix whose eigenvalues are to be computed. UPLO : {‘L’, ‘U’}, optional Same as lower, with ‘L’ for lower and ‘U’ for upper triangular. Deprecated.
Returns:	w : (..., M,) ndarray The eigenvalues in ascending order, each repeated according to its multiplicity.
Raises:	LinAlgError If the eigenvalue computation does not converge.

a : (..., M, M) array_like

A complex- or real-valued matrix whose eigenvalues are to be computed.

UPLO : {‘L’, ‘U’}, optional

Same as lower, with ‘L’ for lower and ‘U’ for upper triangular. Deprecated.

Returns:

w : (..., M,) ndarray

The eigenvalues in ascending order, each repeated according to its multiplicity.

Raises:

LinAlgError

If the eigenvalue computation does not converge.

See also

eigh: eigenvalues and eigenvectors of symmetric/Hermitian arrays.
eigvals: eigenvalues of general real or complex arrays.
eig: eigenvalues and right eigenvectors of general real or complex arrays.

Notes

New in version 1.8.0.

Broadcasting rules apply, see the numpy.linalg documentation for details.

The eigenvalues are computed using LAPACK routines _syevd, _heevd

Examples

>>> from numpy import linalg as LA
>>> a = np.array([[1, -2j], [2j, 5]])
>>> LA.eigvalsh(a)
array([ 0.17157288,  5.82842712])