A fast algorithm for solving the lasso problem exactly without homotopy using differential inclusions

Fuente: arXiv
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Autores principales: Langlois, Gabriel P., Darbon, Jérôme
Formato: Preprint
Publicado: 2025
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author Langlois, Gabriel P.
Darbon, Jérôme
author_facet Langlois, Gabriel P.
Darbon, Jérôme
contents We prove in this work that the well-known lasso problem can be solved exactly without homotopy using novel differential inclusions techniques. Specifically, we show that a selection principle from the theory of differential inclusions transforms the dual lasso problem into the problem of calculating the trajectory of a projected dynamical system that we prove is integrable. Our analysis yields an exact algorithm for the lasso problem, numerically up to machine precision, that is amenable to computing regularization paths and is very fast. Moreover, we show the continuation of solutions to the integrable projected dynamical system in terms of the hyperparameter naturally yields a rigorous homotopy algorithm. Numerical experiments confirm that our algorithm outperforms the state-of-the-art algorithms in both efficiency and accuracy. Beyond this work, we expect our results and analysis can be adapted to compute exact or approximate solutions to a broader class of polyhedral-constrained optimization problems.
format Preprint
id arxiv_https___arxiv_org_abs_2507_05562
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A fast algorithm for solving the lasso problem exactly without homotopy using differential inclusions
Langlois, Gabriel P.
Darbon, Jérôme
Optimization and Control
Machine Learning
Functional Analysis
90C25, 65K05, 37N40, 46N10, 34A60, 62J07
G.1.6; I.5.4
We prove in this work that the well-known lasso problem can be solved exactly without homotopy using novel differential inclusions techniques. Specifically, we show that a selection principle from the theory of differential inclusions transforms the dual lasso problem into the problem of calculating the trajectory of a projected dynamical system that we prove is integrable. Our analysis yields an exact algorithm for the lasso problem, numerically up to machine precision, that is amenable to computing regularization paths and is very fast. Moreover, we show the continuation of solutions to the integrable projected dynamical system in terms of the hyperparameter naturally yields a rigorous homotopy algorithm. Numerical experiments confirm that our algorithm outperforms the state-of-the-art algorithms in both efficiency and accuracy. Beyond this work, we expect our results and analysis can be adapted to compute exact or approximate solutions to a broader class of polyhedral-constrained optimization problems.
title A fast algorithm for solving the lasso problem exactly without homotopy using differential inclusions
topic Optimization and Control
Machine Learning
Functional Analysis
90C25, 65K05, 37N40, 46N10, 34A60, 62J07
G.1.6; I.5.4
url https://arxiv.org/abs/2507.05562