[SciPy-user] Constrained Optimization using Simulated Annealing

lorenzo bolla lbolla@gmail....
Tue Apr 22 15:26:46 CDT 2008


I had the same problem some time ago and I concluded that the easiest way is
to introduce a map on the objective function's parameters to force the
constrains.

For example, if I have a function f(x) and I want to force x to be in
[-1,1], I can introduce a map like:
x --> t / (1 + |t|)
which maps x in [-1,1] to t in [-inf,inf].
Then use the unconstrained optimizer over t.

hth,
L.

On Tue, Apr 22, 2008 at 10:19 PM, lechtlr <lechtlr@yahoo.com> wrote:

>
> Is there way to introduce constraints for the objective function in the
> Simulated Annealing Optimization method in scipy ?
>
> Thanks,
> -Lex
>
>
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-- 
Lorenzo Bolla
lbolla@gmail.com
http://lorenzobolla.emurse.com/
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