numpy.linalg.eigvalsh()

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:

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.

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.

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])

Links:

doc_NumPy

2017-01-10 18:14:44

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