On Jun 3, 2009, at 4:42 AM, Dag Sverre Seljebotn wrote: > Robert Bradshaw wrote: >> From before, relative errors for the naive vs. other algorithm, >> 100000 runs, uniformly chosen in unit square (though nearly all >> distributions look basically the same): >> >> naive >> better 26187 >> avg 1.4940916064705992895601085724e-16 >> worst 5.7659574333851360909896621025e-16 >> other >> better 63116 >> avg 9.5414951065097745299683547276e-17 >> worst 3.9584519821557590591785765975e-16 >> >> - Robert >> > > This is not my speciality, but since the problem here is with things > like fp cancellation on subtraction etc., wouldn't it be better to > increase the odds of wildly different values? > > Something like exp(uniform square)?
I did exp(X) + exp(X)*I where X was a uniform distribution on [-10,10] and various other distributions, all yielded very similar results (which was actually surprising to me). Any thoughts on the 4x speed difference? - Robert _______________________________________________ Cython-dev mailing list [email protected] http://codespeak.net/mailman/listinfo/cython-dev
