scipy.linalg.sqrtm¶

scipy.linalg.sqrtm(A, disp=True, blocksize=64)[source]¶

Matrix square root.

Parameters :

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

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

[R66]

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.

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