[SciPy-User] tip (maybe): scaling and optimizers

Paweł Kwaśniewski pawel.kw@gmail....
Fri Oct 26 02:20:48 CDT 2012

Hi Josef,

I also noticed  that fmin_slsqp is highly scale-sensitive, I also had
that impression using leastsq. Can you tell me where I can find some
more information on how to deal with this?



2012/10/26  <josef.pktd@gmail.com>:
> mainly an observation:
> After figuring out that fmin_slsqp is scale sensitive, I switched to
> normalizing, rescaling loglikelihood functions in statsmodels.
> Loglikelihood functions are our main functions for nonlinear optimization.
> Today I was working by accident on an older branch of statsmodels, and
> the results I got with fmin_bfgs were awful.
> After switching to statsmodels master, the results I get with
> fmin_bfgs are much better (very good: robust and accurate).
> The impression I got from this and from a discussion with Ian Langmore
> (on an L1 penalized optimization pull request) is that many scipy
> optimizers might be scale sensitive in the default settings.
> Watch the scale of your objective function !?
> (qualifier: I don't remember if other changes are in statsmodels
> master and not in my old branch that make optimization more robust.)
> Josef
> ------
> "anecdotal evidence ain't proof"
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> ( http://www.unilang.org/viewtopic.php?f=11&t=38585&start=0&st=0&sk=t&sd=a )
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