[SciPy-User] scipy.optimize.minimize constraint optimization

[Kevin Kunzmann]

Hi,

I have a question concerning constraint optimization with scipy. After 
setting up my problem using scipy.optimize.minimize and the SLSQP method 
the procedure failed to converge and the final iteration was a bit 
awkward. So I inspected the iteration steps closely and the problem 
seems to be, that the bounds / constraints are not respected in every 
step. This blows up some function evaluations, as they are only well 
defined for the feasible set. Is there a way of doing general purpose, 
nonlinear, constraint optimization, that respects the feasible set at 
ALL times in scipy (or at least in python?). The Matlab implementations 
of fmincon using sqp and trust-region-reflective do implement that 
feature 
http://www.mathworks.de/de/help/optim/ug/writing-constraints.html#br9p_ry but 
i would rather stick with python if possible...
If that feature is available already in scipy an comment on that in the 
help would be superb :)

thank you,

Kevin