4 papers
El0ps: An Exact L0-regularized Problems Solver
Théo Guyard, Cédric Herzet, Clément Elvira
This paper presents El0ps, a Python toolbox providing several utilities to handle L0-regularized problems related to applications in machine learning, statistics, and signal proces…
A Generic Branch-and-Bound Algorithm for -Penalized Problems with Supplementary Material
Clément Elvira, Théo Guyard, Cédric Herzet
We present a generic Branch-and-Bound procedure designed to solve L0-penalized optimization problems. Existing approaches primarily focus on quadratic losses and construct relaxati…
A New Branch-and-Bound Pruning Framework for -Regularized Problems
Theo Guyard, Cédric Herzet, Clément Elvira +1
We consider the resolution of learning problems involving -regularization via Branch-and-Bound (BnB) algorithms. These methods explore regions of the feasible space of the…
Safe Peeling for L0-Regularized Least-Squares with supplementary material
Théo Guyard, Gilles Monnoyer, Clément Elvira +1
We introduce a new methodology dubbed ``safe peeling'' to accelerate the resolution of L0-regularized least-squares problems via a Branch-and-Bound (BnB) algorithm. Our procedure e…