  A recently introduced technique for a sparse optimization problem called
"safe screening" allows us to identify irrelevant variables in the early stage
of optimization. In this paper, we first propose a flexible framework for safe
screening based on the Fenchel-Rockafellar duality and then derive a strong
safe screening rule for norm-regularized least squares by the framework. We
call the proposed screening rule for norm-regularized least squares "dynamic
Sasvi" because it can be interpreted as a generalization of Sasvi. Unlike the
original Sasvi, it does not require the exact solution of a more strongly
regularized problem; hence, it works safely in practice. We show that our
screening rule can eliminate more features and increase the speed of the solver
in comparison with other screening rules both theoretically and experimentally.
