Yesterday I have got a letter from Nils Wagner that having OO NLP solvers benchmark would be useful.
So I have committed my file that can be use for your own purposes, modifying the one for your problems and solvers. Let me note that some problems with matplotlib still exist.
Below you can observe a graphic output from the example (also available from openopt/examples/, after svn update). Let me note that usually ALGENCAN works much better that cobyla or lincher. Also, turning stop criteria is very important (for ALGENCAN you should try to set gradtol to 1e-1...1e-6).
Let me also note that current OO-ALGENCAN connection suffers the following drawback: constraints always are calculating, while ALGENCAN uses only active ones. Hence, if user supply constraints c as a sequence (c1, c2, ..., cm) calculations can be speed up. However, it's not implemented in OO-ALGENCAN connection yet.
Please don't forget that cobyla can't handle user-supplied 1st derivatives, so time, cputime, counter will differ significantly (vs lincher and ALGENCAN) if you'll provide the ones.
Also, I intend to provide constraints for ralg soon; maybe, then I'll publish the example with ralg as well.