  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.
