SciPy

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 :

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])

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