# A Screening Strategy for Structured Optimization Involving Nonconvex ℓq,p Regularization

arXiv: https://arxiv.org/abs/2208.02161 · HTML: https://mlsanity.com/paper/2208.02161/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [Gap Safe screening rules for sparsity enforcing penalties](https://mlsanity.com/paper/1611.05780/) (2016)
- [Mind the duality gap: safer rules for the Lasso](https://mlsanity.com/paper/1505.03410/) (2015)
- [Screening Rules for Lasso with Non-Convex Sparse Regularizers](https://mlsanity.com/paper/1902.06125/) (2019)
- [Nonconvex and Nonsmooth Sparse Optimization via Adaptively Iterative Reweighted Methods](https://mlsanity.com/paper/1810.10167/) (2018)
- [Screening Rules and its Complexity for Active Set Identification](https://mlsanity.com/paper/2009.02709/) (2020)

## Abstract (arXiv)

In this paper, we develop a simple yet effective screening rule strategy to improve the computational efficiency in solving structured optimization involving nonconvex ℓq,p regularization. Based on an iteratively reweighted ℓ1 (IRL1) framework, the proposed screening rule works like a preprocessing module that potentially removes the inactive groups before starting the subproblem solver, thereby reducing the computational time in total. This is mainly achieved by heuristically exploiting the dual subproblem information during each iteration.Moreover, we prove that our screening rule can remove all inactive variables in a finite number of iterations of the IRL1 method. Numerical experiments illustrate the efficiency of our screening rule strategy compared with several state-of-the-art algorithms.