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numpy.linalg.eigvalsh(a, UPLO='L')
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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])
numpy.linalg.eigvalsh()
2017-01-10 18:14:44
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