Reachability Is NP-Complete Even for the Simplest Neural Networks

Marco Sälzer; Martin Lange

doi:10.1007/978-3-030-89716-1_10

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arXiv · 2108.13179

Reachability Is NP-Complete Even for the Simplest Neural Networks

Marco Sälzer·Martin Lange

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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.

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BibTeX RIS

Marco Sälzer, Martin Lange. 2021-08-30. Reachability Is NP-Complete Even for the Simplest Neural Networks. https://doi.org/10.1007/978-3-030-89716-1_10

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