资源论文Improving the Effectiveness of SAT-Based Preprocessing for MaxSAT

Improving the Effectiveness of SAT-Based Preprocessing for MaxSAT

2019-11-18 | |  48 |   32 |   0

Abstract 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.

上一篇:A Multicore Tool for Constraint Solving

下一篇:Maximum Satisfiability Using Cores and Correction Sets

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...