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# scipy.linalg.rq

`scipy.linalg.``rq`(a, overwrite_a=False, lwork=None, mode='full', check_finite=True)

  Compute RQ decomposition of a matrix.

  Calculate the decomposition `A = R Q` where Q is unitary/orthogonal and R upper triangular.

  Parameters: | a : (M, N) array_like Matrix to be decomposed overwrite_a : bool, optional Whether data in a is overwritten (may improve performance) lwork : int, optional Work array size, lwork >= a.shape[1]. If None or -1, an optimal size is computed. mode : {‘full’, ‘r’, ‘economic’}, optional Determines what information is to be returned: either both Q and R (‘full’, default), only R (‘r’) or both Q and R but computed in economy-size (‘economic’, see Notes). 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: | R : float or complex ndarray Of shape (M, N) or (M, K) for `mode='economic'`. `K = min(M, N)`. Q : float or complex ndarray Of shape (N, N) or (K, N) for `mode='economic'`. Not returned if `mode='r'`.
  Raises: | LinAlgError If decomposition fails.

  Notes

  This is an interface to the LAPACK routines sgerqf, dgerqf, cgerqf, zgerqf, sorgrq, dorgrq, cungrq and zungrq.

  If `mode=economic`, the shapes of Q and R are (K, N) and (M, K) instead of (N,N) and (M,N), with `K=min(M,N)`.

  Examples

      >>> from scipy import linalg
      >>> from numpy import random, dot, allclose
      >>> a = random.randn(6, 9)
      >>> r, q = linalg.rq(a)
      >>> allclose(a, dot(r, q))
      True
      >>> r.shape, q.shape
      ((6, 9), (9, 9))
      >>> r2 = linalg.rq(a, mode='r')
      >>> allclose(r, r2)
      True
      >>> r3, q3 = linalg.rq(a, mode='economic')
      >>> r3.shape, q3.shape
      ((6, 6), (6, 9))