[SciPy-User] OT: global optimization, hybrid global local search
Sun Apr 22 12:04:14 CDT 2012
On 22/04/2012 17:00, email@example.com wrote:
> I'm looking at nonlinear regression, function estimation again.
> I'm interested in combining global search with local optimizers, which
> I think should be much faster in many of our problems.
> Anyone with ideas, experience, code?
agree that hybrid methods have potential
but like clearer goals before rushing in to code -- de gustabus.
"Optimization" covers a HUGE range --
interactive, e.g. run 10 NM then look +- .1 then 10 NM more ...
dimension: 2d / 3d visualizable, 4d .. say 10d, higher
smooth / Gaussian noise / noisy but no noise model
user gradients / finite-difference gradient est (low fruit) / no-deriv
convex / not
many application areas
with a correspondingly huge range of optimizers, frameworks, plot / visualizers
scipy.optimize, scikit-learn stuff, nlopt, cvxopt, stuff in R ...
and a huge range of users from curve_fit
to people who want X (but don't use it if it's there already)
not to mention *lots* of papers on 1-user methods.
There are more optimizers than test functions --
show how noisy some no-deriv optimizers are on Powell's difficult sin-cos function.
Do you use leastsq, any comments on that ?
cf. Martin Teichmann rewrite https://github.com/scipy/scipy/pull/90
In short, we have to concentrate; suggestions ?
> (Why does scipy not have any good global optimizers?)
Examples please ?
deja vu: late March rant on "why doesn't leastsq do bounds" ?
Well it can, easily
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