qr_update(Q, R, u, v, overwrite_qruv=False, check_finite=True)
If A = Q R
is the QR factorization of A
, return the QR factorization of A + u v**T
for real A
or A + u v**H
for complex A
.
This routine does not guarantee that the diagonal entries of :None:None:`R1`
are real or positive.
Unitary/orthogonal matrix from the qr decomposition of A.
Upper triangular matrix from the qr decomposition of A.
Left update vector
Right update vector
If True, consume Q, R, u, and v, if possible, while performing the update, otherwise make copies as necessary. Defaults to False.
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.
Rank-k QR update
>>> 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)) TrueSee :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.linalg._decomp_update.qr_insert
scipy.linalg._decomp_update.qr_delete
scipy.linalg._decomp_update.qr_update
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