Learning to Rank the Initial Branching Order of SAT Solvers
Finding good branching orders is key to solving SAT problems efficiently, but finding such branching orders is a difficult problem. Using a learning based approach to predict a good branching order before solving, therefore, has potential. In this paper, we investigate predicting branching orders using graph neural networks as a preprocessing step to conflict-driven clause learning (CDCL) SAT solvers. We show that there are significant gains to be made in existing CDCL SAT solvers by providing a good initial branching. Further, we provide three labeling methods to find such initial branching orders in a tractable way. Finally, we train a graph neural network to predict these branching ord...
| Authors | Arvid Eriksson, Gabriel Poesia, Roman Bresson et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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