Lasso screening with a small regularization parameter

Screening for lasso problems is a means of quickly reducing the size of the dictionary needed to solve a given instance without impacting the optimality of the solution obtained. We investigate a sequential screening scheme using a selected sequence of regularization parameter values decreasing to the given target value. Using analytical and empirical means we give insight on how the values of this sequence should be chosen and show that well designed sequential screening yields significant improvement in dictionary reduction and computational efficiency for lightly regularized lasso problems.