`numpy.linalg:eigvalsh`

Signature

def eigvalsh ( a , UPLO = L )

Compute the eigenvalues of a complex Hermitian or real symmetric matrix.

Main difference from eigh: the eigenvectors are not computed.

a : (..., M, M) array_like: A complex- or real-valued matrix whose eigenvalues are to be computed.
UPLO : {'L', 'U'}, 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.

w : (..., M,) ndarray: The eigenvalues in ascending order, each repeated according to its multiplicity.

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

The eigenvalues are computed using LAPACK routines _syevd, _heevd.

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

✓

LA.eigvalsh(a)

✗

a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]])
a
b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]])

✓

✗

wa = LA.eigvalsh(a)
wb = LA.eigvals(b)
wa
wb

✓