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# scipy.linalg.pinvh

`scipy.linalg.``pinvh`(a, cond=None, rcond=None, lower=True, return_rank=False, check_finite=True)

  Compute the (Moore-Penrose) pseudo-inverse of a Hermitian matrix.

  Calculate a generalized inverse of a Hermitian or real symmetric matrix using its eigenvalue decomposition and including all eigenvalues with ‘large’ absolute value.

  Parameters: | a : (N, N) array_like Real symmetric or complex hermetian matrix to be pseudo-inverted cond, rcond : float or None Cutoff for ‘small’ eigenvalues. Singular values smaller than rcond * largest_eigenvalue are considered zero. If None or -1, suitable machine precision is used. lower : bool, optional Whether the pertinent array data is taken from the lower or upper triangle of a. (Default: lower) return_rank : bool, optional if True, return the effective rank of the matrix 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: | B : (N, N) ndarray The pseudo-inverse of matrix a. rank : int The effective rank of the matrix. Returned if return_rank == True
  Raises: | LinAlgError If eigenvalue does not converge

  Examples

      >>> from scipy.linalg import pinvh
      >>> a = np.random.randn(9, 6)
      >>> a = np.dot(a, a.T)
      >>> B = pinvh(a)
      >>> np.allclose(a, np.dot(a, np.dot(B, a)))
      True
      >>> np.allclose(B, np.dot(B, np.dot(a, B)))
      True