[SciPy-user] nonlinear optimisation with constraints
Tue Jun 23 03:10:53 CDT 2009
2009/6/22 Ernest Adrogué <email@example.com>:
> 22/06/09 @ 13:54 (+0200), thus spake Sebastian Walter:
>> 2009/6/22 Ernest Adrogué <firstname.lastname@example.org>:
>> > Hi Sebastian,
>> > 22/06/09 @ 09:57 (+0200), thus spake Sebastian Walter:
>> >> are you sure you can't reformulate the problem?
>> > Another approach would be to try to solve the system of
>> > equations resulting from equating the gradient to zero.
>> > Such equations are defined for all x. I have already tried
>> > that with fsolve(), but it only seems to find the obvious,
>> > useless solution of x=0. I was going to try with a
>> > Newton-Raphson alorithm, but since this would require the
>> > hessian matrix to be calculated, I'm leaving this option
>> > as a last resort :)
>> Ermmm, I don't quite get it. You have an NLP with linear equality
>> constraints and box constraints.
>> Of course you could write down the Lagrangian for that and define an
>> algorithm that satisifies the first and second order optimality
>> But that is not going to be easy, even if you have the exact hessian:
>> you'll need some globalization strategy (linesearch, trust-region,...)
>> to guarantee global convergence
>> and implement something like projected gradients so you stay within
>> the box-constraints.
>> I guess it will be easier to use an existing algorithm...
> Mmmm, yes, but the box constraints are merely to prevent the
> algorithm from evaluating f(x) with values of x for which f(x)
> is not defined. It's not a "real" constraint, because I know
> beforehand that all elements of x are > 0 at the maximum.
>> And I just had a look at fmin_l_bfgs_b: how did you set the equality
>> constraints for this algorithm. It seems to me that this is an
>> unconstrained optimization algorithm which is worthless if you have a
>> constrained NLP.
> You're right. I included the equality constraint within the
> function itself, so that the function I omptimised with fmin_l_bfgs_b
> had one parameter less and computed the "missing" parameter
> internally as a function of the others.
> The problem is that this dependent parameter, was unaffected by
> the box constraint and eventually would take values < 0.
> Fortunately, Siddhardh Chandra has told me the solution, which
> is to maximise f(|x|) instead of f(x), with the linear
> constraint incorporated into the function, using a simple
> unconstrained optimisation algorithm. His message hasn't made it
> to the list though.
I'm curious: Could you elaborate how you incoroporated the linear
constraints into the objective function?
> I have just done this and it seems to work! After 10.410
> function evaluations and 8.904 iterations fmin has found the
> solution and it looks sound at first sight
> Thanks for your help.
>> To compute the Hessian you can always use an AD tool. There are
>> several available in Python.
>> My biased favourite one being pyadolc (
>> http://github.com/b45ch1/pyadolc ) which is slowly approaching version
> I will have a look.
> Thanks again :)
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