# fpo-dys

**Repository Path**: jackfgao/fpo-dys

## Basic Information

- **Project Name**: fpo-dys
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-09-11
- **Last Updated**: 2025-09-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![Docs](https://github.com/mines-opt-ml/spo-tos/actions/workflows/ci.yml/badge.svg)

# Faster Predict-and-Optimize (FPO) with Davis-Yin Splitting (DYS)

In many practical settings, a combinatorial problem must be repeatedly solved with similar, but distinct parameters. Yet, the parameters $w$ are not directly observed; only contextual data $d$ that correlates with $w$ is available. It is tempting to use a neural network to predict $w$ given $d$, but training such a model requires reconciling the discrete nature of combinatorial optimization with the gradient-based frameworks used to train neural networks. One approach to overcoming this issue is to consider a continuous relaxation of the combinatorial problem. While existing such approaches have shown to be highly effective on small problems (10--100 variables) they do not scale well to large problems. In this work, we show how recent results in operator splitting can be used to design such a system which is easy to train  and scales effortlessly to problems with thousands of variables.

## Installation

See ```src/requirements.txt``` for standard required packages. In addition, the code in this repository also requires [PyEPO](https://github.com/khalil-research/PyEPO) package. 

## Quick Start
After installation, type 

``` python -m src.knapsack.train --model_type DYS --num_item 20 --num-knapsack 2  ```

to train our ```DYS``` model on a sample knapsack task. To duplicate, for example, our shortest path experiment, type

``` bash shortest_path_experiment.sh```


## Publication

_Learning to Optimize Integer Linear Programs with Davis-Yin Splitting_ (**[arXiv Link](https://arxiv.org/abs/2301.13395)**)
    
    @article{mckenzie2024differentiating,
  title={Differentiating through integer linear programs with quadratic regularization and davis-yin splitting},
  author={McKenzie, Daniel and Heaton, Howard and Fung, Samy Wu},
  journal={Transactions on Machine Learning Research},
  year={2024}
  }

See [documentation site](https://fpo-dys.research.typal.academy) for more details.