existing screening rules as special cases.

3.  We are able to express all screening rules using general optimization complexity notions such as smoothness or strong convexity, getting rid of problem-specific geometric properties.

4.  Our proposed rules are dynamic (allowing any existing algorithm to be additionally equipped with screening) and safe (guaranteed to only eliminate truly unimportant variables).

#### Related Work.

The concept of screening in the sense of eliminating non-influential data points to reduce the problem size has originated relatively independently in at least two communities. Coming from computational geometry, [@Ahipasaoglu:2008il] has proposed a screening technique for the minimum enclosing ball problem for a given set of data points. Here screening can be interpreted as simply removing points which are guaranteed to lie in the strict interior of the final ball. Later [@Kallberg:2014kd] improve the threshold for this rule in the minimum enclosing ball setting.

Independently, the breakthrough work of [@Ghaoui:2010tu] gave the first screening rules for the important case of sparse regression, as given in the Lasso. Since then, there have been many extensions and alterations of the general concept. While [@Ghaoui:2010tu] exploits geometric quantities to bound the the Lasso dual solution within a compact region, we recommend the 