Constrained least-squares estimation in Python

Question

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 (th

Answer 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 ?)

Answer 2

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

Answer 3

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