scipy.linalg.sqrtm¶
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scipy.linalg.
sqrtm
(A, disp=True, blocksize=64)[source]¶ Matrix square root.
Parameters: - A : (N, N) array_like
Matrix whose square root to evaluate
- disp : bool, optional
Print warning if error in the result is estimated large instead of returning estimated error. (Default: True)
- blocksize : integer, optional
If the blocksize is not degenerate with respect to the size of the input array, then use a blocked algorithm. (Default: 64)
Returns: - sqrtm : (N, N) ndarray
Value of the sqrt function at A
- errest : float
(if disp == False)
Frobenius norm of the estimated error, ||err||_F / ||A||_F
References
[1] Edvin Deadman, Nicholas J. Higham, Rui Ralha (2013) “Blocked Schur Algorithms for Computing the Matrix Square Root, Lecture Notes in Computer Science, 7782. pp. 171-182. Examples
>>> from scipy.linalg import sqrtm >>> a = np.array([[1.0, 3.0], [1.0, 4.0]]) >>> r = sqrtm(a) >>> r array([[ 0.75592895, 1.13389342], [ 0.37796447, 1.88982237]]) >>> r.dot(r) array([[ 1., 3.], [ 1., 4.]])