# jax.numpy.linalg.eigvalsh¶

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

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

LAX-backend implementation of eigvalsh(). Original docstring below.

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) – 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

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

Return type

(.., M,) ndarray

Raises

LinAlgError – If the eigenvalue computation does not converge.

eigh()

eigenvalues and eigenvectors of real symmetric or complex Hermitian (conjugate symmetric) 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]) # may vary

>>> # demonstrate the treatment of the imaginary part of the diagonal
>>> a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]])
>>> a
array([[5.+2.j, 9.-2.j],
[0.+2.j, 2.-1.j]])
>>> # with UPLO='L' this is numerically equivalent to using LA.eigvals()
>>> # with:
>>> b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]])
>>> b
array([[5.+0.j, 0.-2.j],
[0.+2.j, 2.+0.j]])
>>> wa = LA.eigvalsh(a)
>>> wb = LA.eigvals(b)
>>> wa; wb
array([1., 6.])
array([6.+0.j, 1.+0.j])