Poster

Safe screening rules for L0-regression from Perspective Relaxations

Alper Atamturk · Andres Gomez

Keywords: [ Combinatorial Optimization ] [ Dimensionality Reduction ] [ Sparsity and Compressed Sensing ] [ Optimization - General ]

Abstract: We give safe screening rules to eliminate variables from regression with $\ell_0$ regularization or cardinality constraint. These rules are based on guarantees that a feature may or may not be selected in an optimal solution. The screening rules can be computed from a convex relaxation solution in linear time, without solving the L0-optimization problem. Thus, they can be used in a preprocessing step to safely remove variables from consideration apriori. Numerical experiments on real and synthetic data indicate that a significant number of the variables can be removed quickly, hence reducing the computational burden for optimization substantially. Therefore, the proposed fast and effective screening rules extend the scope of algorithms for L0-regression to larger data sets.

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