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# scipy.linalg.sqrtm

`scipy.linalg.``sqrtm`(A, disp=True, blocksize=64)

  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

  [R101] | 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.]])