# Description of algorithm for small scale linear least squares with box constraints

I have small scale dense least squares problem with box constraints

$$\mbox{argmin}||Ax - b||^2 \quad$$ $$\mbox{subject to} \quad l_i \leq x_i \leq u_i,$$

Number of variables is about 10-50, several hundreds in worst case. Number of constraints is equal to number of variables. So i can factorize $A$ and/or $A^TA$. Also in my particular problem very often solution will "touch" only several "sides of box".

I understand that many modern numerical packages have functions to deal with such QP problem. What i seek is detailed description or paper of efficient algorithm (first of all in terms of speed and then accuracy) because i want to understand how it works and implement it by myself.

Currently i only found this paper based on active set. But it looks like (if i am not mistaken) it adds only one variable into "free" variables set per iteration which looks like not very effective for me.