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Screening Rules for Lasso with Non-Convex Sparse Regularizers

Abstract : Leveraging on the convexity of the Lasso problem , screening rules help in accelerating solvers by discarding irrelevant variables, during the optimization process. However, because they provide better theoretical guarantees in identifying relevant variables, several non-convex regulariz-ers for the Lasso have been proposed in the literature. This work is the first that introduces a screening rule strategy into a non-convex Lasso solver. The approach we propose is based on a iterative majorization-minimization (MM) strategy that includes a screening rule in the inner solver and a condition for propagating screened variables between iterations of MM. In addition to improve efficiency of solvers, we also provide guarantees that the inner solver is able to identify the zeros components of its critical point in finite time. Our experimental analysis illustrates the significant computational gain brought by the new screening rule compared to classical coordinate-descent or proximal gradient descent methods.
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Contributor : Alain Rakotomamonjy <>
Submitted on : Wednesday, February 13, 2019 - 2:56:44 PM
Last modification on : Tuesday, December 8, 2020 - 10:02:39 AM
Long-term archiving on: : Tuesday, May 14, 2019 - 4:50:55 PM


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  • HAL Id : hal-02017967, version 1
  • ARXIV : 1902.06125


Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon. Screening Rules for Lasso with Non-Convex Sparse Regularizers. International Conference on Machine Learning, 2019, Long Beach, United States. ⟨hal-02017967⟩



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