[SciPy-dev] Derivative-based nonlinear optimization with linear ineqality constraints

[Anne Archibald]

On 22/05/07, Bill Baxter <wbaxter@gmail.com> wrote:

> All very interesting, but I'm not sure what point you're trying to make.
> I'd be happy to try different solvers on my problem, and in fact I
> have tried a couple.  The BFGS seem to work pretty well if I start
> from a feasible point, but it makes me a little nervious using them
> since the values of my function are bogus outside the feasible region.
>  I'm just setting f(x) to HUGE_NUM outside the feasible region.
> fmin_tnc did *not* work. It seems to take a step out of bounds and get
> lost.

What happened when you tried using a derivative-less solver (which is
guess is just fmin_cobyla)? Too slow? I've never actually gone to the
trouble of implementing analytic derivatives even when I could have,
but then I've never had particularly demanding minimizations to do.

Anne