Grouped Variable Selection with Discrete Optimization

Hussein Hazimeh, Rahul Mazumder, and Peter Radchenko

This is the accompanying code for our paper Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives.

This repo contains code for (i) approximate algorithms based on coordinate descent and combinatorial local search, and (ii) exact algorithms based on a custom branch-and-bound algorithm.

To get started please refer to Demo.ipynb

Prerequisites and Usage

The package is written in Python 3. It requires the following prerequisites:

• numpy
• scipy
• numba
• gurobi (only needed for the BnB algorithm)

See the Jupyter notebook Demo.ipynb for a demonstration on how to use the different algorithms.