Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets
This addresses a bottleneck in infeasibility analysis for applications like constraint satisfaction, though it is incremental as it builds on existing MUS enumerators.
The paper tackles the problem of enumerating Minimal Unsatisfiable Subsets (MUSes) in over-constrained systems, which is time-consuming due to exponential search spaces, by proposing a graph-based learning model to prune formulas, resulting in significant average acceleration in benchmarks including real-world problems.
Finding Minimal Unsatisfiable Subsets (MUSes) of binary constraints is a common problem in infeasibility analysis of over-constrained systems. However, because of the exponential search space of the problem, enumerating MUSes is extremely time-consuming in real applications. In this work, we propose to prune formulas using a learned model to speed up MUS enumeration. We represent formulas as graphs and then develop a graph-based learning model to predict which part of the formula should be pruned. Importantly, our algorithm does not require data labeling by only checking the satisfiability of pruned formulas. It does not even require training data from the target application because it extrapolates to data with different distributions. In our experiments we combine our algorithm with existing MUS enumerators and validate its effectiveness in multiple benchmarks including a set of real-world problems outside our training distribution. The experiment results show that our method significantly accelerates MUS enumeration on average on these benchmark problems.