[SciPy-Dev] Doc error in scipy.optimize.fmin and missing info in ref guide
Sat Mar 20 11:30:27 CDT 2010
> Agreed: the most robust behavior it to allow specification of both
> types of tolerance.
Yes, definitely. Most numerical algorithms for optimization / root
finding can benefit from a range of stopping conditions for different
usage situations. But beyond just the abs and rel tolerances in x, it
can be useful to also provide an absolute tolerance for the size of f
to declare it small enough (ftol) regardless of how close the x values
are. These conditions are very easy to add to the code.
The whole point of absolute tolerances is to take care of the
possibilities of x, f going to zero and ruining the meaning of
relative error. But for large values of x or f their relative error
tolerances make a lot of sense.
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