[Numpy-discussion] Precision difference between dot and sum
Joon
groups.and.lists@gmail....
Mon Nov 1 18:30:33 CDT 2010
Hi,
I just found that using dot instead of sum in numpy gives me better
results in terms of precision loss. For example, I optimized a function
with scipy.optimize.fmin_bfgs. For the return value for the function, I
tried the following two things:
sum(Xb) - sum(denominator)
and
dot(ones(Xb.shape), Xb) - dot(ones(denominator.shape), denominator)
Both of them are supposed to yield the same thing. But the first one gave
me -589112.30492110562 and the second one gave me -589112.30492110678.
In addition, with the routine using sum, the optimizer gave me "Warning:
Desired error not necessarily achieved due to precision loss." With the
routine with dot, the optimizer gave me "Optimization terminated
successfully."
I checked the gradient value as well (I provided analytical gradient) and
gradient was smaller in the dot case as well. (Of course, the the
magnitude was e-5 to e-6, but still)
I was wondering if this is well-known fact and I'm supposed to use dot
instead of sum whenever possible.
It would be great if someone could let me know why this happens.
Thank you,
Joon
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