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scipy.linalg.

# qr_multiply

scipy.linalg.qr_multiply(a, c, mode='right', pivoting=False, conjugate=False, overwrite_a=False, overwrite_c=False)

  Calculate the QR decomposition and multiply Q with a matrix.

  Calculate the decomposition `A = Q R` where Q is unitary/orthogonal and R upper triangular. Multiply Q with a vector or a matrix c.

  Parameters:
    a(M, N), array_like

      Input array

    c array_like

      Input array to be multiplied by `q`.

    mode{‘left’, ‘right’}, optional

      `Q @ c` is returned if mode is ‘left’, `c @ Q` is returned if mode is ‘right’. The shape of c must be appropriate for the matrix multiplications, if mode is ‘left’, `min(a.shape) == c.shape[0]`, if mode is ‘right’, `a.shape[0] == c.shape[1]`.

    pivoting bool, optional

      Whether or not factorization should include pivoting for rank-revealing qr decomposition, see the documentation of qr.

    conjugate bool, optional

      Whether Q should be complex-conjugated. This might be faster than explicit conjugation.

    overwrite_a bool, optional

      Whether data in a is overwritten (may improve performance)

    overwrite_c bool, optional

      Whether data in c is overwritten (may improve performance). If this is used, c must be big enough to keep the result, i.e. `c.shape[0]` = `a.shape[0]` if mode is ‘left’.

  Returns:
    CQ ndarray

      The product of `Q` and `c`.

    R(K, N), ndarray

      R array of the resulting QR factorization where `K = min(M, N)`.

    P(N,) ndarray

      Integer pivot array. Only returned when `pivoting=True`.

  Raises:
    LinAlgError

      Raised if QR decomposition fails.

  Notes

  This is an interface to the LAPACK routines `?GEQRF`, `?ORMQR`, `?UNMQR`, and `?GEQP3`.

  Added in version 0.11.0.

  Examples

      >>> import numpy as np
      >>> from scipy.linalg import qr_multiply, qr
      >>> A = np.array([[1, 3, 3], [2, 3, 2], [2, 3, 3], [1, 3, 2]])
      >>> qc, r1, piv1 = qr_multiply(A, 2*np.eye(4), pivoting=1)
      >>> qc
      array([[-1.,  1., -1.],
             [-1., -1.,  1.],
             [-1., -1., -1.],
             [-1.,  1.,  1.]])
      >>> r1
      array([[-6., -3., -5.            ],
             [ 0., -1., -1.11022302e-16],
             [ 0.,  0., -1.            ]])
      >>> piv1
      array([1, 0, 2], dtype=int32)
      >>> q2, r2, piv2 = qr(A, mode='economic', pivoting=1)
      >>> np.allclose(2*q2 - qc, np.zeros((4, 3)))
      True