OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models

Live demo: https://optimus-solver.vercel.app/

This repository contains the official implementations for OptiMUS: Scalable Optimization Modeling with (MI) LP Solvers and Large Language Models. Check out this branch for an implementation of the older version.

NLP4LP Dataset

You can download the dataset from https://nlp4lp.vercel.app/. Please note that NLP4LP is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes (The updated version will be added soon).

Running the code

First install the requirement:

pip install -r requirements.txt

Install gurobi and your license (for gurobi installation, please refer to the official website):

grbgetkey YOUR_LICENSE

Add your API keys to config.py:

{
    "openai_api_key": "OPENAI_API_KEY",
    "openai_org_id": "OPENAI_ORG_ID",
    "together_api_key": "TOGETHER_API_KEY",
    "mistral_api_key": "MISTRAL_API_KEY",
}

Download and add the data to the repo in the following structure:

OptiMUS
│   README.md
│   run.py
│   requirements.txt
│   config.json
│   LICENSE
│   agents/
│   data/
│       nlp4lp/
│       complexor/
│       nl4opt/

NLP4LP is available here. ComplexOR and NLP4LP datasets are available here (in the supplementary material): https://openreview.net/forum?id=HobyL1B9CZ

Run the script:

python run.py

You can modify the arguments to run the script on different datasets, models, and problems. For example:

 python run.py --dataset nlp4lp --problem 1

or

 python run.py --dataset complexor --problem Knapsack

Reference

Have questions? Want to implement this idea? Feel free to reach out via email (teshnizi /at/ stanford /dot/ edu)

Reference

@article{ahmaditeshnizi2024optimus,
  title={OptiMUS: Scalable Optimization Modeling with (MI) LP Solvers and Large Language Models},
  author={AhmadiTeshnizi, Ali and Gao, Wenzhi and Udell, Madeleine},
  journal={arXiv preprint arXiv:2402.10172},
  year={2024}
}