[SciPy-User] optimize.fmin_slsqp bounds problem

[Jean-Francois.Moulin at hzg.de]

>http://sscce.org/
>
>Please provide a self-contained reproducible example. What is the exact objfn?
>
>I don't get any errors with
>
>------------------------------
>import numpy as np
>from scipy.optimize import fmin_slsqp>
>
>np.random.seed(1234)
>y = np.random.randn(9) * 100
>def objfn(x):
>        return ((x - y)**2).sum()
>
>bounds = [(-1e-09, 1e-08), (1e-07, 1e-05), (1e-07, 1e-05), (1e-10,
>1e-08), (1e-10, 1e-08), (1e-10, 1e-08), (80.0, 200.0), (1.0, 5.0), (1.0,
>5.0)]
>x0 = np.array([  0.00000000e+00,   1.01000000e-06,   3.03000000e-06,
>                 1.00000000e-09,   1.00000000e-09,   1.00000000e-09,
>                 1.50000000e+02,   2.00000000e+00,   2.00000000e+00])
>res = fmin_slsqp(objfn,
>np.array(x0,dtype=float),bounds=bounds,full_output=True)
>------------------------------

>The algorithm and code used (with permission from ACM) is this:
>http://dl.acm.org/citation.cfm?id=192124
>
>The only way to understand what goes wrong is probably to look in the
>Fortran code and see what it does.
>
>-- 
>Pauli Virtanen

Well my objective function is a quite complex recursive function of the 9 input parameters which in the end returns the sum of the squared deviation as expected by fmin_slsqp (it involves several of my own modules and a large dataset, so too long to be posted). I have checked that the return value is valid (not NAN eg) and as you see there is a single iteration done. 
I also have checked the minimizer with examples similar to yours, and it worked.
I'll have to get my hand Fortran dirty it seems, I just hoped someone had alreay had this kind of symptom and found a "trivial" error.
Thanks anyway!

JF
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