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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 2D.

  Examples

      >>> import numpy as np
      >>> 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.]])