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Wed, 25 Jan 2017 12:15:12 -0800

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
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