Maximum Satisfiability Using Cores and Correction Sets
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Abstract Core-guided M AX S AT algorithms dominate other methods in solving industrial M AX S AT problems. In this work, we propose a new efficient algorithm that is guided by correction sets and cores. At every iteration, the algorithm obtains a correction set or a core, which is then used to rewrite the formula using incremental and succinct transformations. We theoretically show that correction sets and cores have complementary strengths and empirically demonstrate that their combination leads to an efficient M AX S AT solver that outperforms state-of-the-art WPMS solvers on the 2014 Evaluation on industrial instances.
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