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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