Hi all, I've modified the QR routine in scipy.linalg.qr in 3 ways, to make it faster and more compatible with Matlab. This is my first patch so any feedback is appreciated... you can find the files here: http://www.cs.kuleuven.be/~bartvde/scipy/ 1) The original method used only one LAPACK-routine (GEQRF) to compute the R factor, but NOT the (xORGQR and xUNGQR) to compute the Q factor. This is a huge efficiency loss, see the figure at my site for a comparison of the old and new qr method. I included the 2 LAPACK-wrappers in patch_flapack.diff and the new qr method in patch_qr.diff. (I don't use clapack, so I can't test this.) The old qr method is still available as qr_old in case you want to compare the results, use test_qr.py ;-) 2) You can compute economy-sized (as Matlab calls it) or skinny QR factorizations for tall matrices (more rows than cols). The performance gain should be obvious now. 3) If you only want the R factor and not the Q, the method stops early and is 2 times faster. Testing for test_decompy.py is included in patch_test.diff. There are 2 new parameters for scipy.decomp.qr: econ=False: economy size or not -> Matlab's qr(A,0) mode='qr': 'qr' or 'r' (or 'qre', qr with Givens rotations, maybe I'll do this later) -> Matlab's Q,R=qr(A) or R=qr(A) or Q,R,E=qr(A) Bart