On 08/08/13 00:30, sudipta sinha wrote: 
Hi All,

I am facing a problem for constrained linear least square fitting. In my case the matrix equation looks like [Y]nX1=[X]nXm[P]mX1, where Y and P are vectors and X is a matrix and n, m are dimension of the matrix. Further, there is a equality constraint on P which is Sum(P(i))=0.0. How do I proceed to solve that? Which function of python is suitable for this? I saw few of discussion on scipy.optimize.fmin_slsqp() function but the implementation of this function is not very straightforward. Therefore, I need your help. I am new in SCIPY. Please help me out in this regard.
_______________________________________________
SciPy-User mailing list
SciPy-User@scipy.org
http://mail.scipy.org/mailman/listinfo/scipy-user
Hi

Did you have a look to lmfit package (http://newville.github.io/lmfit-py/). I am trying some minimization with boundaries and constrains, and it seems that this is the way to go.  Do not know if somebody here has ever use it.

Best

Jose

-- 
Jose Guzman
http://www.ist.ac.at/~jguzman/