UpMax: User partitioning for MaxSAT
This work addresses performance optimization for MaxSAT solvers, which is incremental as it builds on existing partitioning methods by enabling user-defined schemes.
The paper tackles the problem of solving Maximum Satisfiability (MaxSAT) instances by introducing UpMax, a framework that decouples partitioning from solving algorithms, allowing independent design of partitioning schemes and showing that partitioning significantly impacts algorithm performance.
It has been shown that Maximum Satisfiability (MaxSAT) problem instances can be effectively solved by partitioning the set of soft clauses into several disjoint sets. The partitioning methods can be based on clause weights (e.g., stratification) or based on graph representations of the formula. Afterwards, a merge procedure is applied to guarantee that an optimal solution is found. This paper proposes a new framework called UpMax that decouples the partitioning procedure from the MaxSAT solving algorithms. As a result, new partitioning procedures can be defined independently of the MaxSAT algorithm to be used. Moreover, this decoupling also allows users that build new MaxSAT formulas to propose partition schemes based on knowledge of the problem to be solved. We illustrate this approach using several problems and show that partitioning has a large impact on the performance of unsatisfiability-based MaxSAT algorithms.