最大Satisfiabiliy

F. Bacchus, M. Järvisalo, R. Martins
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引用次数: 10

摘要

最大可满足性(MaxSAT)是SAT的优化版本,它通过寻找最优真值分配而不仅仅是一个令人满意的真值分配来求解。在MaxSAT中,要优化的目标函数由一组加权软子句来指定:一个真值赋值的目标值是它所满足的软子句的权值之和。此外,MaxSAT问题可能存在真值赋值必须满足的硬子句。许多优化问题可以自然地编码到MaxSAT中,再加上MaxSAT求解器的显著性能改进,使得MaxSAT在许多不同的应用领域得到了应用。本章详细概述了近年来在解决现实世界优化问题中最成功的MaxSAT解决方法。本文还概述了MaxSAT研究的最新进展,包括编码、应用、预处理、不完全求解、算法组合、基于分区的求解和并行求解。
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Maximum Satisfiabiliy
Maximum satisfiability (MaxSAT) is an optimization version of SAT that is solved by finding an optimal truth assignment instead of just a satisfying one. In MaxSAT the objective function to be optimized is specified by a set of weighted soft clauses: the objective value of a truth assignment is the sum of the weights of the soft clauses it satisfies. In addition, the MaxSAT problem can have hard clauses that the truth assignment must satisfy. Many optimization problems can be naturally encoded into MaxSAT and this, along with significant performance improvements in MaxSAT solvers, has led to MaxSAT being used in a number of different application areas. This chapter provides a detailed overview of the approaches to MaxSAT solving that have in recent years been most successful in solving real-world optimization problems. Further recent developments in MaxSAT research are also overviewed, including encodings, applications, preprocessing, incomplete solving, algorithm portfolios, partitioning-based solving, and parallel solving.
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Fixed-Parameter Tractability Complete Algorithms A History of Satisfiability Automated Configuration and Selection of SAT Solvers Quantified Boolean Formulas
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