L0Learn: A Scalable Package for Sparse Learning using L0 Regularization

• URL: http://arxiv.org/abs/2202.04820v2
• Date: Fri, 9 Jun 2023 16:20:37 GMT
• Title: L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
• Authors: Hussein Hazimeh, Rahul Mazumder, Tim Nonet
• Abstract summary: L0Learn is an open-source package for sparse linear regression classification. It implements scalable, approximate algorithms, based on coordinate descent and local optimization.
• Score: 6.037383467521294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present L0Learn: an open-source package for sparse linear regression and classification using $\ell_0$ regularization. L0Learn implements scalable, approximate algorithms, based on coordinate descent and local combinatorial optimization. The package is built using C++ and has user-friendly R and Python interfaces. L0Learn can address problems with millions of features, achieving competitive run times and statistical performance with state-of-the-art sparse learning packages. L0Learn is available on both CRAN and GitHub (https://cran.r-project.org/package=L0Learn and https://github.com/hazimehh/L0Learn).

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