On Jan 23, 6:59 pm, Robert Kern <[EMAIL PROTECTED]> wrote: > Paul McGuire wrote: > > I've posted a simple Matrix class on my website as a small-footprint > > package for doing basic calculations on matrices up to about 10x10 in > > size (no theoretical limit, but performance on inverse is exponential). > > Why is that? A simple and robust LU decomposition should be no more than > O(n**3). > > -- > Robert Kern
Well "3" is an exponent isn't it? :) In truth, in my laziness, I didn't *actually* test the performance. But after measuring inversion times for nxn matrices for n=2 to 12, I get these results (run on Python 2.4.2, on a 2GHz CPU): n seconds ln(seconds) 2 0.000411225449045 -7.79636895604 3 0.00102247632031 -6.88552782893 4 0.00437541642862 -5.43175358002 5 0.0146999129778 -4.21991370509 6 0.0507813143849 -2.98022681913 7 0.143077961026 -1.94436561528 8 0.39962257773 -0.917234732978 9 1.14412558021 0.134640659841 10 3.01953516439 1.10510290046 11 8.76039971561 2.17024153354 12 21.8032182861 3.0820575867 Plotting n vs. ln(seconds) gives a fairly straight line of slope about 1.09, and exp(1.09) = 2.97, so your big-O estimate seems to line up nicely with the experimental data - I couldn't have fudged it any better. -- Paul -- http://mail.python.org/mailman/listinfo/python-list
