[RBFOpt] problem with binary vector input and linear constraints

[Zhengxing Chen]

Hi Giacomo,

Thanks for the reply.

Does there exist any other python package that can deal with black-box
optimization with binary inputs and linear constraints? I couldn't find one
as far as I search.

Thanks,
Zhengxing







On Sat, Jan 20, 2018 at 1:51 PM, Giacomo Nannicini <giacomo.n at gmail.com>
wrote:

> Hi Zhengxing,
> your intuition is correct. Binary variables are natively supported
> (just say they are integer and in [0, 1]), but linear constraints are
> not. You can add violation of constraints as a penalty to the
> objective function. However, I should remark that in practice this
> only works in case the feasible region is "lightly constrained". If
> finding a feasible point becomes difficult, then the method will fail
> because the numerics of the penalty function are typically very bad.
>
> Giacomo
>
> On Wed, Jan 17, 2018 at 3:49 PM, Zhengxing Chen <czxttkl at gmail.com> wrote:
> > Hi,
> >
> > I have a black-box minimization problem f(x), where x is a binary vector
> and
> > x must satisfy some linear equality constraints.
> >
> > Is RBF-OPT applicable to my problem? I can imagine a way to do it: set
> > variables to be integers and bound them in [0, 1], and also let f(x)
> return
> > a very large number if x doesn't satisfy the constraints. But I'm not
> sure
> > if this violates any assumption of the model.
> >
> > Thanks,
> > Zhengxing
> >
> >
> >
> > _______________________________________________
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> > RBFOpt at list.coin-or.org
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>
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