Smooth Bilevel Programming for Sparse Regularization

TL;DR

This paper introduces a simple, smooth bilevel optimization method for sparse regularization that outperforms existing approaches across various models and regularization types, using only linear system solutions and BFGS optimization.

Contribution

It proposes a novel reparametrization and bilevel scheme for IRLS, enabling a unified, efficient, and robust approach to sparse regularization problems.

Findings

Achieves top performance on diverse sparsity models.

Removes need for multiple specialized solvers.

Demonstrates fast convergence and robustness.

Abstract

Iteratively reweighted least square (IRLS) is a popular approach to solve sparsity-enforcing regression problems in machine learning. State of the art approaches are more efficient but typically rely on specific coordinate pruning schemes. In this work, we show how a surprisingly simple reparametrization of IRLS, coupled with a bilevel resolution (instead of an alternating scheme) is able to achieve top performances on a wide range of sparsity (such as Lasso, group Lasso and trace norm regularizations), regularization strength (including hard constraints), and design matrices (ranging from correlated designs to differential operators). Similarly to IRLS, our method only involves linear systems resolutions, but in sharp contrast, corresponds to the minimization of a smooth function. Despite being non-convex, we show that there is no spurious minima and that saddle points are "ridable", so that there always exists a descent direction. We thus advocate for the use of a BFGS quasi-Newton solver, which makes our approach simple, robust and efficient. We perform a numerical benchmark of the convergence speed of our algorithm against state of the art solvers for Lasso, group Lasso, trace norm and linearly constrained problems. These results highlight the versatility of our approach, removing the need to use different solvers depending on the specificity of the ML problem under study.