logm(A, disp=True)
The matrix logarithm is the inverse of expm: expm(logm(A)) == A
Matrix whose logarithm to evaluate
Print warning if error in the result is estimated large instead of returning estimated error. (Default: True)
Matrix logarithm of A
(if disp == False)
1-norm of the estimated error, ||err||_1 / ||A||_1
Compute matrix logarithm.
>>> from scipy.linalg import logm, expm
... a = np.array([[1.0, 3.0], [1.0, 4.0]])
... b = logm(a)
... b array([[-1.02571087, 2.05142174], [ 0.68380725, 1.02571087]])
>>> expm(b) # Verify expm(logm(a)) returns a array([[ 1., 3.], [ 1., 4.]])See :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.linalg._matfuncs.logm
scipy.linalg._matfuncs_sqrtm._sqrtm_triu
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