Maximum Satisfiability (MaxSAT) is an optimization variant of the Boolean Satisfiability (SAT) problem. In general, MaxSAT algorithms perform a succession of SAT solver calls to reach an optimum solution making extensive use of cardinality constraints. Many of these algorithms are non-incremental in nature, i.e. at each iteration the formula is rebuilt and no knowledge is reused from one iteration to another. In this paper, we exploit the knowledge acquired across iterations using novel schemes to use cardinality constraints in an incremental fashion. We integrate these schemes with several MaxSAT algorithms. Our experimental results show a significant performance boost for these algo- rithms as compared to their non-incremental counterparts. These results suggest that incremental cardinality constraints could be beneficial for other constraint solving domains.
Incomplete MaxSAT solving aims to quickly find a solution that attempts to minimize the sum of the weights of the unsatisfied soft clauses without providing any optimality guarantees. In this paper, we propose two approximation strategies for improving incomplete MaxSAT solving. In one of the strategies, we cluster the weights and approximate them with a representative weight. In another strategy, we break up the problem of minimizing the sum of weights of unsatisfiable clauses into multiple minimization subproblems. Experimental results show that approximation strategies can be used to find better solutions than the best incomplete solvers in the MaxSAT Evaluation 2017.
Core-guided algorithms proved to be effective on industrial instances of MaxSAT, the optimization variant of the satisfiability problem for propositional formulas. These algorithms work by iteratively checking satisfiability of a formula that is relaxed at each step by using the information provided by unsatisfiable cores. The paper introduces a new core-guided algorithm that adds cardinality constraints for each detected core, but also limits the number of literals in each constraint in order to control the number of refutations in subsequent satisfiability checks. The performance gain of the new algorithm is assessed on the industrial instances of the 2014 MaxSAT Evaluation.
Solvers for the Maximum satisfiability (MaxSAT) problem find an increasing number of applications today. We focus on improving MaxHS — one of the most successful recent MaxSAT algorithms — via SAT-based preprocessing. We show that employing SAT-based preprocessing via the so-called labelled CNF (LCNF) framework before calling MaxHS can in some cases greatly degrade the performance of the solver. As a remedy, we propose a lifting of MaxHS that works directly on LCNFs, allowing for a tighter integration of SAT-based preprocessing and MaxHS. Our empirical results on standard crafted and industrial weighted partial MaxSAT Evaluation benchmarks show overall improvements over the original MaxHS algorithm both with and without SAT-based preprocessing.
Several MaxSAT algorithms based on iterative SAT solving have been proposed in recent years. These algorithms are in general the most efﬁcient for real-world applications. Existing data indicates that, among MaxSAT algorithms based on iterative SAT solving, the most efﬁcient ones are core-guided, i.e. algorithms which guide the search by iteratively computing unsatisﬁable subformulas (or cores). For weighted MaxSAT, core-guided algorithms exhibit a number of important drawbacks, including a possibly exponential number of iterations and the use of a large number of auxiliary variables. This paper develops two new algorithms for (weighted) MaxSAT that address these two drawbacks. The ﬁrst MaxSAT algorithm implements core-guided iterative SAT solving with binary search. The second algorithm extends the ﬁrst one by exploiting disjoint cores. The empirical evaluation shows that core-guided binary search is competitive with current MaxSAT solvers.