Screening Rules for Overlapping Group Lasso
This work addresses a specific bottleneck in large-scale optimization for overlapping group lasso, offering incremental improvements in computational efficiency.
The paper tackles the challenge of screening for overlapping group lasso, which was previously infeasible due to group overlaps, by developing rules that allow independent group testing through a dual polytope projection approach, demonstrating efficiency on various datasets.
Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero coefficients using simple rules independently of the others. However, screening for overlapping group lasso remains an open challenge because the overlaps between groups make it infeasible to test each group independently. In this paper, we develop screening rules for overlapping group lasso. To address the challenge arising from groups with overlaps, we take into account overlapping groups only if they are inclusive of the group being tested, and then we derive screening rules, adopting the dual polytope projection approach. This strategy allows us to screen each group independently of each other. In our experiments, we demonstrate the efficiency of our screening rules on various datasets.