A greedy screening test strategy to accelerate solving LASSO problems with small regularization parameters - Liang Yong

In the era of big data remarked by high dimensionality and large sample size, the least absolute shrinkage and selection operator (LASSO) problems demand efficient algorithms. Both static and dynamic strategies based on screening test principle have been proposed recently, in order to safely filter out irrelevant atoms from the dictionary. However, such strategies only work well for LASSO problems with large regularization parameters, and lose their efficiency for those with small regularization parameters. This paper presents a novel greedy screening test strategy to accelerate solving LASSO problems with small regularization parameters, as well as its effectiveness through adoption of a relatively larger regularization parameter which filters out irrelevant atoms in every iteration. Further more, the convergence proof of the greedy strategy is given, and the computational complexity of LASSO solvers …