scipy.linalg.expm_frechet¶

scipy.linalg.
expm_frechet
(A, E, method=None, compute_expm=True, check_finite=True)[source]¶ Frechet derivative of the matrix exponential of A in the direction E.
Parameters: A : (N, N) array_like
Matrix of which to take the matrix exponential.
E : (N, N) array_like
Matrix direction in which to take the Frechet derivative.
method : str, optional
Choice of algorithm. Should be one of
 SPS (default)
 blockEnlarge
compute_expm : bool, optional
Whether to compute also expm_A in addition to expm_frechet_AE. Default is True.
check_finite : bool, optional
Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, nontermination) if the inputs do contain infinities or NaNs.
Returns: expm_A : ndarray
Matrix exponential of A.
expm_frechet_AE : ndarray
Frechet derivative of the matrix exponential of A in the direction E.
For
compute_expm = False
, only expm_frechet_AE is returned.See also
expm
 Compute the exponential of a matrix.
Notes
This section describes the available implementations that can be selected by the method parameter. The default method is SPS.
Method blockEnlarge is a naive algorithm.
Method SPS is ScalingPadeSquaring [R120]. It is a sophisticated implementation which should take only about 3/8 as much time as the naive implementation. The asymptotics are the same.
New in version 0.13.0.
References
[R120] (1, 2) Awad H. AlMohy and Nicholas J. Higham (2009) Computing the Frechet Derivative of the Matrix Exponential, with an application to Condition Number Estimation. SIAM Journal On Matrix Analysis and Applications., 30 (4). pp. 16391657. ISSN 10957162 Examples
>>> import scipy.linalg >>> A = np.random.randn(3, 3) >>> E = np.random.randn(3, 3) >>> expm_A, expm_frechet_AE = scipy.linalg.expm_frechet(A, E) >>> expm_A.shape, expm_frechet_AE.shape ((3, 3), (3, 3))
>>> import scipy.linalg >>> A = np.random.randn(3, 3) >>> E = np.random.randn(3, 3) >>> expm_A, expm_frechet_AE = scipy.linalg.expm_frechet(A, E) >>> M = np.zeros((6, 6)) >>> M[:3, :3] = A; M[:3, 3:] = E; M[3:, 3:] = A >>> expm_M = scipy.linalg.expm(M) >>> np.allclose(expm_A, expm_M[:3, :3]) True >>> np.allclose(expm_frechet_AE, expm_M[:3, 3:]) True