  Convex sparsity-promoting regularizations are ubiquitous in modern
statistical learning. By construction, they yield solutions with few non-zero
coefficients, which correspond to saturated constraints in the dual
optimization formulation. Working set (WS) strategies are generic optimization
techniques that consist in solving simpler problems that only consider a subset
of constraints, whose indices form the WS. Working set methods therefore
involve two nested iterations: the outer loop corresponds to the definition of
the WS and the inner loop calls a solver for the subproblems. For the Lasso
estimator a WS is a set of features, while for a Group Lasso it refers to a set
of groups. In practice, WS are generally small in this context so the
associated feature Gram matrix can fit in memory. Here we show that the
Gauss-Southwell rule (a greedy strategy for block coordinate descent
techniques) leads to fast solvers in this case. Combined with a working set
strategy based on an aggressive use of so-called Gap Safe screening rules, we
propose a solver achieving state-of-the-art performance on sparse learning
problems. Results are presented on Lasso and multi-task Lasso estimators.
