[SciPy-User] constrained least square fitting using scipy.optimize.fmin_slsqp() function

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.