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# numpy.linalg.eigvalsh

`numpy.linalg.``eigvalsh`(a, UPLO='L')

  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 Specifies whether the calculation is done with the lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).
  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

  This is a simple interface to the LAPACK routines dsyevd and zheevd that sets those routines’ flags to return only the eigenvalues of real symmetric and complex Hermitian arrays, respectively.

  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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