numpy.linalg.eigvalsh()
numpy.linalg.eigvalsh
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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 in ascending order, each repeated according to its multiplicity.
Raises: LinAlgError
If the eigenvalue computation does not converge.
See also
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])
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Licensed under the NumPy License.
https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.linalg.eigvalsh.html