scipy.linalg.qr_update#
- scipy.linalg.qr_update(Q, R, u, v, overwrite_qruv=False, check_finite=True)#
Rank-k QR update
If
A = Q R
is the QR factorization ofA
, return the QR factorization ofA + u v**T
for realA
orA + u v**H
for complexA
.- Parameters
- Q(M, M) or (M, N) array_like
Unitary/orthogonal matrix from the qr decomposition of A.
- R(M, N) or (N, N) array_like
Upper triangular matrix from the qr decomposition of A.
- u(M,) or (M, k) array_like
Left update vector
- v(N,) or (N, k) array_like
Right update vector
- overwrite_qruvbool, optional
If True, consume Q, R, u, and v, if possible, while performing the update, otherwise make copies as necessary. Defaults to False.
- check_finitebool, 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. Default is True.
- Returns
- Q1ndarray
Updated unitary/orthogonal factor
- R1ndarray
Updated upper triangular factor
See also
Notes
This routine does not guarantee that the diagonal entries of R1 are real or positive.
New in version 0.16.0.
References
- 1
Golub, G. H. & Van Loan, C. F. Matrix Computations, 3rd Ed. (Johns Hopkins University Press, 1996).
- 2
Daniel, J. W., Gragg, W. B., Kaufman, L. & Stewart, G. W. Reorthogonalization and stable algorithms for updating the Gram-Schmidt QR factorization. Math. Comput. 30, 772-795 (1976).
- 3
Reichel, L. & Gragg, W. B. Algorithm 686: FORTRAN Subroutines for Updating the QR Decomposition. ACM Trans. Math. Softw. 16, 369-377 (1990).
Examples
>>> from scipy import linalg >>> a = np.array([[ 3., -2., -2.], ... [ 6., -9., -3.], ... [ -3., 10., 1.], ... [ 6., -7., 4.], ... [ 7., 8., -6.]]) >>> q, r = linalg.qr(a)
Given this q, r decomposition, perform a rank 1 update.
>>> u = np.array([7., -2., 4., 3., 5.]) >>> v = np.array([1., 3., -5.]) >>> q_up, r_up = linalg.qr_update(q, r, u, v, False) >>> q_up array([[ 0.54073807, 0.18645997, 0.81707661, -0.02136616, 0.06902409], # may vary (signs) [ 0.21629523, -0.63257324, 0.06567893, 0.34125904, -0.65749222], [ 0.05407381, 0.64757787, -0.12781284, -0.20031219, -0.72198188], [ 0.48666426, -0.30466718, -0.27487277, -0.77079214, 0.0256951 ], [ 0.64888568, 0.23001 , -0.4859845 , 0.49883891, 0.20253783]]) >>> r_up array([[ 18.49324201, 24.11691794, -44.98940746], # may vary (signs) [ 0. , 31.95894662, -27.40998201], [ 0. , 0. , -9.25451794], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ]])
The update is equivalent, but faster than the following.
>>> a_up = a + np.outer(u, v) >>> q_direct, r_direct = linalg.qr(a_up)
Check that we have equivalent results:
>>> np.allclose(np.dot(q_up, r_up), a_up) True
And the updated Q is still unitary:
>>> np.allclose(np.dot(q_up.T, q_up), np.eye(5)) True
Updating economic (reduced, thin) decompositions is also possible:
>>> qe, re = linalg.qr(a, mode='economic') >>> qe_up, re_up = linalg.qr_update(qe, re, u, v, False) >>> qe_up array([[ 0.54073807, 0.18645997, 0.81707661], # may vary (signs) [ 0.21629523, -0.63257324, 0.06567893], [ 0.05407381, 0.64757787, -0.12781284], [ 0.48666426, -0.30466718, -0.27487277], [ 0.64888568, 0.23001 , -0.4859845 ]]) >>> re_up array([[ 18.49324201, 24.11691794, -44.98940746], # may vary (signs) [ 0. , 31.95894662, -27.40998201], [ 0. , 0. , -9.25451794]]) >>> np.allclose(np.dot(qe_up, re_up), a_up) True >>> np.allclose(np.dot(qe_up.T, qe_up), np.eye(3)) True
Similarly to the above, perform a rank 2 update.
>>> u2 = np.array([[ 7., -1,], ... [-2., 4.], ... [ 4., 2.], ... [ 3., -6.], ... [ 5., 3.]]) >>> v2 = np.array([[ 1., 2.], ... [ 3., 4.], ... [-5., 2]]) >>> q_up2, r_up2 = linalg.qr_update(q, r, u2, v2, False) >>> q_up2 array([[-0.33626508, -0.03477253, 0.61956287, -0.64352987, -0.29618884], # may vary (signs) [-0.50439762, 0.58319694, -0.43010077, -0.33395279, 0.33008064], [-0.21016568, -0.63123106, 0.0582249 , -0.13675572, 0.73163206], [ 0.12609941, 0.49694436, 0.64590024, 0.31191919, 0.47187344], [-0.75659643, -0.11517748, 0.10284903, 0.5986227 , -0.21299983]]) >>> r_up2 array([[-23.79075451, -41.1084062 , 24.71548348], # may vary (signs) [ 0. , -33.83931057, 11.02226551], [ 0. , 0. , 48.91476811], [ 0. , 0. , 0. ], [ 0. , 0. , 0. ]])
This update is also a valid qr decomposition of
A + U V**T
.>>> a_up2 = a + np.dot(u2, v2.T) >>> np.allclose(a_up2, np.dot(q_up2, r_up2)) True >>> np.allclose(np.dot(q_up2.T, q_up2), np.eye(5)) True