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 |
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Returns: |
w : (..., M,) ndarray The eigenvalues in ascending order, each repeated according to its multiplicity. |
Raises: |
LinAlgError If the eigenvalue computation does not converge. |
See also
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
>>> from numpy import linalg as LA >>> a = np.array([[1, -2j], [2j, 5]]) >>> LA.eigvalsh(a) array([ 0.17157288, 5.82842712])
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https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.linalg.eigvalsh.html