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

Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares