okridge 0.1.1

A packge for solving k-sparse ridge regression certifiably!

OKRidge

This repository contains source code to our NeurIPS 2023 paper:

OKRidge: Scalable Optimal k-Sparse Ridge Regression

• Documentation: https://okridge.readthedocs.io
• GitHub: https://github.com/jiachangliu/OKRidge
• PyPI: https://pypi.org/project/okridge/
• Free and open source software: BSD license

Table of Content

• Introduction
• Installation
• Usage
• Contributing
• License

Introduction

We consider the following optimization problem:

$$\min_{\mathbf{\beta}} \sum_{i=1}^n (y_i - \mathbf{x}_i^T \mathbf{\beta})^2 + \lambda_2 \lVert \mathbf{\beta} \rVert_2^2 \quad \text{s.t.} \quad \lVert \mathbf{\beta} \rVert_0 \leq k$$

Optimal k-sparse ridge regression is a crucial ML problem that has many applications in statistics, machine learning, and data mining. However, the problem is NP-hard, and existing algorithms are either slow (using commercial MIP solvers) or suboptimal (using convex or nonconvex regularizers to approximate $\ell_0$). We propose a novel algorithm, OKRidge, that can solve the problem to provable optimality in a scalable manner.

OKRidge is based on the branch-and-bound framework. The insight leading to the computational efficiency comes from a novel lower bound calculation involving, first, a saddle point formulation, and from there, either solving (i) a linear system or (ii) using an ADMM-based approach, where the proximal operators can be efficiently evaluated by solving another linear system and an isotonic regression problem. We also propose a method to warm-start our solver, which leverages a beam search.

Experimentally, our methods attain provable optimality with run times that are orders of magnitude faster than those of the existing MIP formulations solved by the commercial solver Gurobi.

Installation

$ pip install okridge

Usage

Please see the example.ipynb jupyter notebook on GitHub for a detailed tutorial on how to use OKRidge in a python environment.

k = 10 # cardinality constraint
lambda2 = 0.1 # l2 regularization parameter
gap_tol = 1e-4 # optimality gap tolerance
verbose = True # print out the progress
time_limit = 180 # time limit in seconds

BnB_optimizer = BNBTree(X=X, y=y, lambda2=lambda2)

upper_bound, betas, optimality_gap, max_lower_bound, running_time = BnB_optimizer.solve(k = k, gap_tol = gap_tol, verbose = verbose, time_limit = time_limit)

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

okridge was created by Jiachang Liu. It is licensed under the terms of the BSD 3-Clause license.

Credits

okridge was created with cookiecutter and the py-pkgs-cookiecutter template.

Citing Our Work

If you find our work useful in your research, please consider citing the following paper:

@inproceedings{liu2023okridge,
  title={OKRidge: Scalable Optimal k-Sparse Ridge Regression},
  author={Liu, Jiachang and Rosen, Sam and Zhong, Chudi and Rudin, Cynthia},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}