[SciPy-user] Questions about scipy.optimize.fmin_cobyla

fdu.xiaojf@gmai... fdu.xiaojf@gmai...
Tue Jul 17 01:13:57 CDT 2007

Dominique Orban wrote:
 >> This problem happens because while fmin_cobyla is able to handle
 >> constraints, it nevertheless evaluates the objective function outside
 >> those constraints. I think this is a very serious bug, which limits
 >> the usefulness of fmin_cobyla. Unfortunately, I do not have a general
 >> solution.
 > This behavior of Cobyla is not a bug.
 > There are two kinds of methods for constrained problems in optimization:
 > feasible and infeasible methods. The iterates generated in a feasible method
 > will all satisfy the constraints. In an infeasible method, they may or may not
 > satisfy the constraints but, if convergence happens and if all goes well, the
 > iterates converge to a point which is (approximately) feasible. Cobyla is an
 > infeasible method.
 > The fact that your objective function is not defined everywhere (because of 
 > log terms) suggests that you should not be using an infeasible method. 
 > you could look into feasible interior-point methods (beware, there also are
 > infeasible interior-point methods... in fact, most of them are).
 > NLPy features one such method, albeit a simple one, under the name TRIP (for
 > 'trust-region interior-point method'). For those who care about this sort of
 > thing, it is a purely primal method at the moment. I mostly implemented it 
as a
 > proof of concept in the NLPy framework, and plan to upgrade it in the 
future. It
 > will not treat equality constraints, but the inequalities can be as 
nonlinear as
 > you please, as long as they are twice differentiable. You can give it a try 
 > let me know what happens.

I couldn't find proper documents for TRIP method from NLPY. There are
only 3 small examples in the website of NLPY, and when I tried to get
the source code of NLPY:

$ svn co https://nlpy.svn.sourceforge.net/svnroot/nlpy/trunk/nlpy ./
svn: Can't convert string from native encoding to 'UTF-8':
svn: ?\208?\194?\189?\168 ?\206?\196?\177?\190?\206?\196?\181?\181.txt

I have tried to fix this problem, but with no success.

So could you point me some links that give a detailed explanation of how
to use TRIP?

 > In addition to the constraints you already have, add new constraints stating
 > that the arguments of the logs must be >= 0. Interior-point methods ensure 
 > at each iteration, those quantities remain > 0 (strictly).
 > If the constraints you end up with are *only bound constraints*, you can also
 > look into L-BFGS-B (which I believe is interfaced in SciPy), a projected
 > quasi-Newton method, or TRON (www.mcs.anl.gov/~more/tron which I don't 
think is
 > interfaced in SciPy), a projected Newton method. NLPy also contains a
 > gradient-projection method which I find surprisingly efficient, but also only
 > applies to bound constraints.
 > Re-parametrizing your problem is, of course, another option.
 > Good luck,
 > Dominique

Thanks a lot.

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