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scipy.linalg.solve

# scipy.linalg.inv

`scipy.linalg.``inv`(a, overwrite_a=False, check_finite=True)

  Compute the inverse of a matrix.

  Parameters: | a : array_like Square matrix to be inverted. overwrite_a : bool, optional Discard data in a (may improve performance). Default is False. check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.
  Returns: | ainv : ndarray Inverse of the matrix a.
  Raises: | LinAlgError If a is singular. ValueError If a is not square, or not 2-dimensional.

  Examples

      >>> from scipy import linalg
      >>> a = np.array([[1., 2.], [3., 4.]])
      >>> linalg.inv(a)
      array([[-2. ,  1. ],
             [ 1.5, -0.5]])
      >>> np.dot(a, linalg.inv(a))
      array([[ 1.,  0.],
             [ 0.,  1.]])