scipy.linalg.

expm_cond#

scipy.linalg.expm_cond(A, check_finite=True)[source]#

Relative condition number of the matrix exponential in the Frobenius norm.

Parameters:

A2-D array_like: Square input matrix with shape (N, N).
check_finitebool, optional: Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.

Returns:

kappafloat: The relative condition number of the matrix exponential in the Frobenius norm

See also

expm: Compute the exponential of a matrix.
expm_frechet: Compute the Frechet derivative of the matrix exponential.

Notes

A faster estimate for the condition number in the 1-norm has been published but is not yet implemented in SciPy.

Added in version 0.14.0.

Examples

>>> import numpy as np
>>> from scipy.linalg import expm_cond
>>> A = np.array([[-0.3, 0.2, 0.6], [0.6, 0.3, -0.1], [-0.7, 1.2, 0.9]])
>>> k = expm_cond(A)
>>> k
1.7787805864469866