L0Learn
Fast Algorithms for Best Subset Selection
Highly optimized toolkit for approximately solving L0-regularized learning problems (a.k.a. best subset selection). The algorithms are based on coordinate descent and local combinatorial search. For more details, check the paper by Hazimeh and Mazumder (2020) <doi:10.1287/opre.2019.1919>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.1.0 |
rolling linux/jammy R-4.5 | L0Learn_2.1.0.tar.gz |
1.2 MiB |
2.1.0 |
rolling linux/noble R-4.5 | L0Learn_2.1.0.tar.gz |
1.3 MiB |
2.1.0 |
rolling source/ R- | L0Learn_2.1.0.tar.gz |
1022.8 KiB |
2.1.0 |
latest linux/jammy R-4.5 | L0Learn_2.1.0.tar.gz |
1.2 MiB |
2.1.0 |
latest linux/noble R-4.5 | L0Learn_2.1.0.tar.gz |
1.3 MiB |
2.1.0 |
latest source/ R- | L0Learn_2.1.0.tar.gz |
1022.8 KiB |
2.1.0 |
2026-04-26 source/ R- | L0Learn_2.1.0.tar.gz |
1022.8 KiB |
2.1.0 |
2026-04-23 source/ R- | L0Learn_2.1.0.tar.gz |
1022.8 KiB |
2.1.0 |
2026-04-09 windows/windows R-4.5 | L0Learn_2.1.0.zip |
1.5 MiB |
2.1.0 |
2025-04-20 source/ R- | L0Learn_2.1.0.tar.gz |
1022.8 KiB |