Reducing SAT to Max2SAT

Authors: Carlos Ansótegui, Jordi Levy

IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental In this paper, we provide an efficient and constructive method for Reducing SAT to Max2SAT and show empirical results of how Max SAT solvers are more efficient than SAT solvers solving the translation of hard formulas for Resolution.
Researcher Affiliation Academia Carlos Ans otegui1 and Jordi Levy2 1Logic & Optimization Group (LOG), University of Lleida 2IIIA-CSIC carlos.ansotegui@udl.cat, levy@iiia.csic.es
Pseudocode Yes Algorithm 1: SAT through Max SAT
Open Source Code No The paper does not provide a link or explicit statement about releasing the source code for their methodology.
Open Datasets No The paper mentions generating PHPn n 1 SAT instances and the Pigeon Hole principle but does not provide access information (link, DOI, citation with authors/year) for a publicly available or open dataset.
Dataset Splits No The paper mentions generating PHPn n 1 SAT instances but does not specify any training, validation, or test splits for these instances.
Hardware Specification Yes The machine has 2.1GHz and 5GB RAM. We used a machine of 2.1GHz and 5GB RAM memory.
Software Dependencies Yes In particular, we experimented with SAT solver Ca Di Ca L (version 1.0.3) and the top performing three Max SAT solvers from the weighted category of the Max SAT Evaluation 2020 [Bacchus et al., 2019]: RC2 [Ignatiev et al., 2019], Max HS [Davies and Bacchus, 2011] and Uwrmaxsat [Piotr ow, 2020].
Experiment Setup Yes The Max SAT solvers were run on a Max SAT formula built from the generated PHPn n 1 SAT instance ϕ as follows: the 2SAT clauses in ϕ are assigned weight and the rest (n 1)ary clauses are translated into Max SAT using a gadget.