sqrtm — SciPy v1.15.2 Manual (original) (raw)

scipy.linalg.

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

Matrix square root.

Parameters:

A(N, N) array_like

Matrix whose square root to evaluate

dispbool, optional

Print warning if error in the result is estimated large instead of returning estimated error. (Default: True)

blocksizeinteger, 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. The dtype is float or complex. The precision (data size) is determined based on the precision of input A.

errestfloat

(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

import numpy as np 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.]])