SAT instances generation using Graph Variational Autoencoders
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Published Version
Date
2024-10-11
Authors
Crowley, Daniel
Dalla, Marco
O'Sullivan, Barry
Visentin, Andrea
Journal Title
Journal ISSN
Volume Title
Publisher
ESANN
Published Version
Abstract
This paper presents a SAT instance generator using a Graph Variational Autoencoder (GVAE2SAT) architecture that outperforms existing generative deep learning models in speed and requires minimal post-processing. Our computational analyses benchmark this model against current deep learning techniques, introducing advanced metrics for more accurate evaluation. This new model is unique in its ability to maintain partial satisfiability of SAT instances while significantly reducing computational time. Although no method perfectly addresses all challenges in generating SAT instances, our approach marks a significant step forward in the efficiency and effectiveness of SAT instance generation.
Description
Keywords
SAT , Graph Variational Autoencoder , GVAE2SAT
Citation
Crowley, D., Dalla, M., O’Sullivan, B. and Visentin, A. (2024) 'SAT instances generation using graph variational autoencoders', ESANN 2024: Bruges,Belgium, 9-11 October 2024, pp. 369–374. https://doi.org/10.14428/esann/2024.ES2024-223