  In high dimensional settings, sparse structures are crucial for efficiency,
either in term of memory, computation or performance. In some contexts, it is
natural to handle more refined structures than pure sparsity, such as for
instance group sparsity. Sparse-Group Lasso has recently been introduced in the
context of linear regression to enforce sparsity both at the feature level and
at the group level. We adapt to the case of Sparse-Group Lasso recent safe
screening rules that discard early in the solver irrelevant features/groups.
Such rules have led to important speed-ups for a wide range of iterative
methods. Thanks to dual gap computations, we provide new safe screening rules
for Sparse-Group Lasso and show significant gains in term of computing time for
a coordinate descent implementation.
