Reachability Is NP-Complete Even for the Simplest Neural Networks
Computational Complexity
2022-03-16 v2 Machine Learning
Abstract
We investigate the complexity of the reachability problem for (deep) neural networks: does it compute valid output given some valid input? It was recently claimed that the problem is NP-complete for general neural networks and conjunctive input/output specifications. We repair some flaws in the original upper and lower bound proofs. We then show that NP-hardness already holds for restricted classes of simple specifications and neural networks with just one layer, as well as neural networks with minimal requirements on the occurring parameters.
Keywords
Cite
@article{arxiv.2108.13179,
title = {Reachability Is NP-Complete Even for the Simplest Neural Networks},
author = {Marco Sälzer and Martin Lange},
journal= {arXiv preprint arXiv:2108.13179},
year = {2022}
}