[RBFOpt] problem with binary vector input and linear constraints

[Giacomo Nannicini]

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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