Tailoring local search for partial MaxSAT

65Citations
Citations of this article
21Readers
Mendeley users who have this article in their library.

Abstract

Partial MaxSAT (PMS) is a generalization to SAT and MaxSAT. Many real world problems can be encoded into PMS in a more natural and compact way than SAT and MaxSAT. In this paper, we propose new ideas for local search for PMS, which mainly rely on the distinction between hard and soft clauses. We use these ideas to develop a local search PMS algorithm called Dist. Experimental results on PMS benchmarks from MaxSAT Evaluation 2013 show that Dist significantly outperforms state-of-the-art PMS algorithms, including both local search algorithms and complete ones, on random and crafted benchmarks. For the industrial benchmark, Dist dramatically outperforms previous local search algorithms and is comparable with complete algorithms.

Cite

CITATION STYLE

APA

Cai, S., Luo, C., Thornton, J., & Su, K. (2014). Tailoring local search for partial MaxSAT. In Proceedings of the National Conference on Artificial Intelligence (Vol. 4, pp. 2623–2629). AI Access Foundation. https://doi.org/10.1609/aaai.v28i1.9109

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free