  The lasso is a popular method to induce shrinkage and sparsity in the
solution vector (coefficients) of regression problems, particularly when there
are many predictors relative to the number of observations. Solving the lasso
in this high-dimensional setting can, however, be computationally demanding.
Fortunately, this demand can be alleviated via the use of screening rules that
discard predictors prior to fitting the model, leading to a reduced problem to
be solved. In this paper, we present a new screening strategy: look-ahead
screening. Our method uses safe screening rules to find a range of penalty
values for which a given predictor cannot enter the model, thereby screening
predictors along the remainder of the path. In experiments we show that these
look-ahead screening rules outperform the active warm-start version of the Gap
Safe rules.
