EVANQP

EPFL Verifier for Approximate Neural Networks and QPs

This repository provides the code accompanying the paper Stability Verification of Neural Network Controllers using Mixed-Integer Programming.

Getting Started

To get started with the code, clone this repo, and install the evanqp python package with

python setup.py install

Jupiter Notebooks with examples can be found in examples/.

Running Benchmarks

There are benchmarks available for two different examples available: the dc-dc converter example (examples/dc_dc_converter/) and the lipschitz example (examples/lipschitz/). To run the benchmarks change the parameters in run_benchmarks.sh to match your hardware configuration and execute the benchmark with

bash run_benchmarks.sh

The results can then be analysed in the Jupiter Notebook benchmark_analysis.ipynb.

Citing our Work

To cite our work in other academic papers, please use the following BibTex entry:

@ARTICLE{schwan2023,
author={Schwan, Roland and Jones, Colin N. and Kuhn, Daniel},
journal={IEEE Transactions on Automatic Control}, 
title={Stability Verification of Neural Network Controllers Using Mixed-Integer Programming}, 
year={2023},
volume={68},
number={12},
pages={7514-7529},
doi={10.1109/TAC.2023.3283213}
}