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, not necessarily ordered, 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, not necessarily ordered, 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

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

The eigenvalues are computed using LAPACK routines _ssyevd, _heevd

Examples

>>> from numpy import linalg as LA
>>> a = np.array([[1, -2j], [2j, 5]])
>>> LA.eigvalsh(a)
array([ 0.17157288+0.j,  5.82842712+0.j])