[SciPy-User] Sometimes fmin_l_bfgs_b tests NaN parameters andthen fails to converge

[Dmitrey]

 hi, 

     Well, openopt does provide the same methods as SciPy, otherwise there
     are few custom algorithms for bounded optimization (gsubg and ralg) and
     connectors to few more Fortan libraries, but the documentation really
     leaves much to be desired. Also, the interface didn't seem very much
     convenient to me / installation is a bit complicated etc.
     
     In particular, the descriptions of the optimizers all claim to perform
     the same thing better than any other, but there is no comprehensive
     comparison and highlights of specific features, such as evaluation
     outside of the bounds as you mentioned :-(
        

   ralg (as well as gsubg) is adjusted very well to handle problems with
   restricted domains.

     So not knowing the specifics of the algorithms, their limits, advantages
     and disadvantages, you are pretty much left in the dark just trying out
     stuff in the hope that something will finally work out...
        

   For your problem (nonlinear local minimization) for a solver
   efficiency matter numbers of: variables, box constraints, linear
   eq/ineq constraints, nonlinear eq/ineq constraints. Another one very
   essential issue is having some gradients of active constraints forming
   linear system close to singular (then lots of NLP solvers will fail to
   solve it). Are you capable of taking into account all these
   parameters? I guess it's easy to change solver from the "stuff"
   instead and try what is better for the nlp involved. Moreover, would I
   provide some comparison info in the way you would like, after new
   release of any of the openopt-connected solver I would have to perform
   the comparison over and over again. Thus I don't see any reason to
   perform it.

   D.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.scipy.org/pipermail/scipy-user/attachments/20110104/ef93e3ef/attachment.html>