[Tutor] constrained least square fitting using python

[sudipta]

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.
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