[Numpy-discussion] Checking matrix condition number
What is the best way to make sure that a matrix inversion makes any sense before preforming it? I am currently struggling to understand some results from matrix inversions in my work, and I would like to see if I am dealing with an ill-conditioned problem. It is probably user error, but I don't like having the possibility hanging over my head. I naively put a call to np.linalg.cond into my code; all of my cores went to 100% and a few minutes later I got a number. To be fair A is 6400 elements square, but this takes ~20x more time than the inversion. This is not really practical for what I am doing, is there a better way? This is partly in response to Ilhan Polat's post about introducing the A\b operator to numpy. I also couldn't check the Numpy mailing list archives to see if this has been asked before, the numpy-discussion gmane link isn't working for me at all. Thanks for your time, Ned ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] performance solving system of equations in numpy and MATLAB
Thanks everyone for helping me glimpse the secret world of FORTRAN compilers. I am running a Linux machine, so I will look into MKL and openBLAS. It was easy for me to get a Intel parallel studio XE license as a student, so I have options. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] performance solving system of equations in numpy and MATLAB
I recently did a conceptual experiment to estimate the computational time required to solve an exact expression in contrast to an approximate solution (Helmholtz vs. Helmholtz-Kirchhoff integrals). The exact solution requires a matrix inversion, and in my case the matrix would contain ~15000 rows. On my machine MATLAB seems to perform this matrix inversion with random matrices about 9x faster (20 sec vs 3 mins). I thought the performance would be roughly the same because I presume both rely on the same LAPACK solvers. I will not actually need to solve this problem (even at 20 sec it is prohibitive for broadband simulation), but if I needed to I would reluctantly choose MATLAB . I am simply wondering why there is this performance gap, and if there is a better way to solve this problem in numpy? Thank you, Ned #Python version import numpy as np testA = np.random.randn(15000, 15000) testb = np.random.randn(15000) %time testx = np.linalg.solve(testA, testb) %MATLAB version testA = randn(15000); testb = randn(15000, 1); tic(); testx = testA \ testb; toc(); ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
