Schur Number Five

Authors: Marijn Heule

AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We obtained the solution, n = 160, by encoding the problem into propositional logic and applying massively parallel satisfiability solving techniques on the resulting formula. We constructed and validated a proof of the solution to increase trust in the correctness of the multi-CPU-year computations.
Researcher Affiliation Academia Marijn J. H. Heule Computer Science Department The University of Texas at Austin 2317 Speedway, M/S D9500 Austin, Texas 78712-0233
Pseudocode No The paper describes methods and algorithms in text but does not include structured pseudocode or algorithm blocks.
Open Source Code Yes The tools and proof parts presented in this paper are available at https://www.cs.utexas.edu/~marijn/Schur/.
Open Datasets No The paper does not use a traditional dataset in the machine learning sense, but rather a mathematical problem encoding. No explicit public access information (link, DOI, formal citation) for a dataset is provided.
Dataset Splits No The paper solves a mathematical SAT problem and does not describe training, validation, or test dataset splits in the context of machine learning experiments.
Hardware Specification Yes We solved the subproblems on the Lonestar 5 cluster of the Texas Advanced Computing Center (TACC). Each compute node consists of a Xeon E5-2690 v3 (Haswell) chip with 24 cores running on 2.6 GHz.
Software Dependencies Yes For each cube α P 5, we solved2 the problem R5 160 α using our cube-and-conquer solver consisting of a modified version of MARCH CU (Heule et al. 2012) as look-ahead (cube) solver and GLUCOSE 3.0 (Audemard and Simon 2009) as CDCL (conquer) solver.
Experiment Setup Yes Examples of effective values are e = 1.0 and f = 0.6, e = 0.5 and f = 0.1, and e = 0.3 and f = 0.02. For the final experiments we used e = 0.3 and f = 0.02.