[SciPy-user] Minimize a bounded convex function
Thu Oct 18 21:04:22 CDT 2007
On 18/10/2007, Geoffrey Zhu <email@example.com> wrote:
> I am trying to minimize a convex function f(x) that is not defined
> when x<=0 or when x>=1.5. I am trying to use scipy.optimize.golden(),
> but when the minimal point is very close to zero, golden() will call
> my target function on the undefined region. Is there any way to ensure
> that the optimizer will only look for answers within the domain?
There's one open interval that many optimizers can handle, namely the
real line. You can try transforming your x coordinate so that (for
example) x = (tanh(w)+1)*0.75 and then minimizing as a function of w.
This probably doesn't preserve convexity (though it should not
introduce any local minima), but it means that you can use unbounded
minimization and avoid the endpoints.
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