scipy.linalg.rq#

scipy.linalg.rq(a, overwrite_a=False, lwork=None, mode='full', check_finite=True)[source]#

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_abool, optional: Whether data in a is overwritten (may improve performance)
lworkint, 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_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.

Returns:

Rfloat or complex ndarray: Of shape (M, N) or (M, K) for mode='economic'. K = min(M, N).
Qfloat 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

>>> import numpy as np
>>> from scipy import linalg
>>> rng = np.random.default_rng()
>>> a = rng.standard_normal((6, 9))
>>> r, q = linalg.rq(a)
>>> np.allclose(a, r @ q)
True
>>> r.shape, q.shape
((6, 9), (9, 9))
>>> r2 = linalg.rq(a, mode='r')
>>> np.allclose(r, r2)
True
>>> r3, q3 = linalg.rq(a, mode='economic')
>>> r3.shape, q3.shape
((6, 6), (6, 9))