Skip to yearly menu bar Skip to main content


Screening rules for Lasso with non-convex Sparse Regularizers

alain rakotomamonjy · Gilles Gasso · Joseph Salmon

Pacific Ballroom #190

Keywords: [ Sparsity and Compressed Sensing ] [ Dimensionality Reduction ]


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 regularizers 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.

Live content is unavailable. Log in and register to view live content