# abess: A Fast Best Subset Selection Library in Python and R

arXiv: https://arxiv.org/abs/2110.09697 · HTML: https://mlsanity.com/paper/2110.09697/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [OpenML-Python: an extensible Python API for OpenML](https://mlsanity.com/paper/1911.02490/) (2019)
- [Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms](https://mlsanity.com/paper/1803.01454/) (2018)
- [Sparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitions](https://mlsanity.com/paper/1709.10029/) (2017)
- [Celer: a Fast Solver for the Lasso with Dual Extrapolation](https://mlsanity.com/paper/1802.07481/) (2018)
- [Accelerated Alternating Projections for Robust Principal Component Analysis](https://mlsanity.com/paper/1711.05519/) (2017)
- [Community detection in sparse networks via Grothendieck's inequality](https://mlsanity.com/paper/1411.4686/) (2014)
- [A Splicing Approach to Best Subset of Groups Selection](https://mlsanity.com/paper/2104.12576/) (2021)

## Built on by (most cited first, 16 of 16)

- [Noise-aware Physics-informed Machine Learning for Robust PDE Discovery](https://mlsanity.com/paper/2206.12901/) (2022)
- [A sparse PAC-Bayesian approach for high-dimensional quantile prediction](https://mlsanity.com/paper/2409.01687/) (2024)
- [High-dimensional Bayesian Tobit regression for censored response with Horseshoe prior](https://mlsanity.com/paper/2505.08288/) (2025)
- [Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique](https://mlsanity.com/paper/2308.00251/) (2023)
- [Holistic Generalized Linear Models](https://mlsanity.com/paper/2205.15447/) (2022)
- [Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offs](https://mlsanity.com/paper/2605.26631/) (2026)
- [On the Computational Complexity of Private High-dimensional Model Selection](https://mlsanity.com/paper/2310.07852/) (2023)
- [skscope: Fast Sparsity-Constrained Optimization in Python](https://mlsanity.com/paper/2403.18540/) (2024)
- [FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models](https://mlsanity.com/paper/2410.19081/) (2024)
- [Understanding Best Subset Selection: A Tale of Two C(omplex)ities](https://mlsanity.com/paper/2301.06259/) (2023)
- [A Consistent and Scalable Algorithm for Best Subset Selection in Single Index Models](https://mlsanity.com/paper/2309.06230/) (2023)
- [IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight](https://mlsanity.com/paper/2403.11363/) (2024)
- [Automated Model Selection for Generalized Linear Models](https://mlsanity.com/paper/2404.16560/) (2024)
- [Sparse Optimization for Transfer Learning: A L0-Regularized Framework for Multi-Source Domain Adaptation](https://mlsanity.com/paper/2504.04812/) (2025)
- [Exponential Lasso: robust sparse penalization under heavy-tailed noise and outliers with exponential-type loss](https://mlsanity.com/paper/2511.15332/) (2025)
- [spca: An R package to Compute Least Squares Sparse Principal Components](https://mlsanity.com/paper/2606.29104/) (2026)

## Abstract (arXiv)

We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis. Particularly, the abess certifiably gets the optimal solution within polynomial times with high probability under the linear model. Our efficient implementation allows abess to attain the solution of best-subset selection problems as fast as or even 20x faster than existing competing variable (model) selection toolboxes. Furthermore, it supports common variants like best group subset selection and ℓ2 regularized best-subset selection. The core of the library is programmed in C++. For ease of use, a Python library is designed for conveniently integrating with scikit-learn, and it can be installed from the Python library Index. In addition, a user-friendly R library is available at the Comprehensive R Archive Network. The source code is available at: https://github.com/abess-team/abess.