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Module « numpy.linalg »

Fonction eigh - module numpy.linalg

Signature de la fonction eigh

def eigh(a, UPLO='L') 

Description

help(numpy.linalg.eigh)

Return the eigenvalues and eigenvectors of a complex Hermitian
(conjugate symmetric) or a real symmetric matrix.

Returns two objects, a 1-D array containing the eigenvalues of `a`, and
a 2-D square array or matrix (depending on the input type) of the
corresponding eigenvectors (in columns).

Parameters
----------
a : (..., M, M) array
    Hermitian or real symmetric matrices whose eigenvalues and
    eigenvectors 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').
    Irrespective of this value only the real parts of the diagonal will
    be considered in the computation to preserve the notion of a Hermitian
    matrix. It therefore follows that the imaginary part of the diagonal
    will always be treated as zero.

Returns
-------
A namedtuple with the following attributes:

eigenvalues : (..., M) ndarray
    The eigenvalues in ascending order, each repeated according to
    its multiplicity.
eigenvectors : {(..., M, M) ndarray, (..., M, M) matrix}
    The column ``eigenvectors[:, i]`` is the normalized eigenvector
    corresponding to the eigenvalue ``eigenvalues[i]``.  Will return a
    matrix object if `a` is a matrix object.

Raises
------
LinAlgError
    If the eigenvalue computation does not converge.

See Also
--------
eigvalsh : eigenvalues of real symmetric or complex Hermitian
           (conjugate symmetric) arrays.
eig : eigenvalues and right eigenvectors for non-symmetric arrays.
eigvals : eigenvalues of non-symmetric arrays.
scipy.linalg.eigh : Similar function in SciPy (but also solves the
                    generalized eigenvalue problem).

Notes
-----
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.

The eigenvalues/eigenvectors are computed using LAPACK routines ``_syevd``,
``_heevd``.

The eigenvalues of real symmetric or complex Hermitian matrices are always
real. [1]_ The array `eigenvalues` of (column) eigenvectors is unitary and
`a`, `eigenvalues`, and `eigenvectors` satisfy the equations ``dot(a,
eigenvectors[:, i]) = eigenvalues[i] * eigenvectors[:, i]``.

References
----------
.. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
       FL, Academic Press, Inc., 1980, pg. 222.

Examples
--------
>>> import numpy as np
>>> from numpy import linalg as LA
>>> a = np.array([[1, -2j], [2j, 5]])
>>> a
array([[ 1.+0.j, -0.-2.j],
       [ 0.+2.j,  5.+0.j]])
>>> eigenvalues, eigenvectors = LA.eigh(a)
>>> eigenvalues
array([0.17157288, 5.82842712])
>>> eigenvectors
array([[-0.92387953+0.j        , -0.38268343+0.j        ], # may vary
       [ 0.        +0.38268343j,  0.        -0.92387953j]])

>>> (np.dot(a, eigenvectors[:, 0]) -
... eigenvalues[0] * eigenvectors[:, 0])  # verify 1st eigenval/vec pair
array([5.55111512e-17+0.0000000e+00j, 0.00000000e+00+1.2490009e-16j])
>>> (np.dot(a, eigenvectors[:, 1]) -
... eigenvalues[1] * eigenvectors[:, 1])  # verify 2nd eigenval/vec pair
array([0.+0.j, 0.+0.j])

>>> A = np.matrix(a) # what happens if input is a matrix object
>>> A
matrix([[ 1.+0.j, -0.-2.j],
        [ 0.+2.j,  5.+0.j]])
>>> eigenvalues, eigenvectors = LA.eigh(A)
>>> eigenvalues
array([0.17157288, 5.82842712])
>>> eigenvectors
matrix([[-0.92387953+0.j        , -0.38268343+0.j        ], # may vary
        [ 0.        +0.38268343j,  0.        -0.92387953j]])

>>> # demonstrate the treatment of the imaginary part of the diagonal
>>> a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]])
>>> a
array([[5.+2.j, 9.-2.j],
       [0.+2.j, 2.-1.j]])
>>> # with UPLO='L' this is numerically equivalent to using LA.eig() with:
>>> b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]])
>>> b
array([[5.+0.j, 0.-2.j],
       [0.+2.j, 2.+0.j]])
>>> wa, va = LA.eigh(a)
>>> wb, vb = LA.eig(b)
>>> wa
array([1., 6.])
>>> wb
array([6.+0.j, 1.+0.j])
>>> va
array([[-0.4472136 +0.j        , -0.89442719+0.j        ], # may vary
       [ 0.        +0.89442719j,  0.        -0.4472136j ]])
>>> vb
array([[ 0.89442719+0.j       , -0.        +0.4472136j],
       [-0.        +0.4472136j,  0.89442719+0.j       ]])

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