Constrained Least-squares Estimation In Python

I'm trying to perform a constrained least-squares estimation using Scipy such that all of the coefficients are in the range (0,1) and sum to 1 (this functionality is implemented in

Solution 1:

scipy-optimize-leastsq-with-bound-constraints on SO givesleastsq_bounds, which is leastsq with bound constraints such as 0 <= x_i <= 1. The constraint that they sum to 1 can be added in the same way.
(I've found leastsq_bounds / MINPACK to be good on synthetic test functions in 5d, 10d, 20d; how many variables do you have ?)

Solution 2:

Have a look at this tutorial, it seems pretty clear.

Solution 3:

Since MATLAB's lsqlin is a bounded linear least squares solver, you would want to check out scipy.optimize.lsq_linear.