Approximation Strategies for Incomplete MaxSAT

TL;DR

This paper introduces two novel approximation strategies for incomplete MaxSAT solving, aiming to improve solution quality by weight clustering and problem decomposition, with experimental validation showing superior results over existing solvers.

Contribution

The paper presents two new approximation methods for incomplete MaxSAT, enhancing solution quality and efficiency compared to prior approaches.

Findings

Approximation strategies outperform previous incomplete solvers in MaxSAT evaluations.

Weight clustering and problem decomposition improve solution quality.

Experimental results validate the effectiveness of proposed methods.

Abstract

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.