L0Learn: A Scalable Package for Sparse Learning using L0 Regularization

Hussein Hazimeh, Rahul Mazumder, Tim Nonet.

Year: 2023, Volume: 24, Issue: 205, Pages: 1−8


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.

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