Should Algorithms for Random SAT and Max-SAT Be Different?

AAAI 2017 pp. 3915-3921

doi:10.1609/AAAI.V31I1.11135 /aaai/2017/liu2017aaai-algorithms/

Abstract

We analyze to what extent the random SAT and Max-SAT problems differ in their properties. Our findings suggest that for random k-CNF with ratio in a certain range, Max-SAT can be solved by any SAT algorithm with subexponential slowdown, while for formulae with ratios greater than some constant, algorithms under the random walk framework require substantially different heuristics. In light of these results, we propose a novel probabilistic approach for random Max-SAT called ProMS. Experimental results illustrate that ProMS outperforms many state-of-the-art local search solvers on random Max-SAT benchmarks.

PDF AAAI Semantic Scholar

Cite

Text

Liu and de Melo. "Should Algorithms for Random SAT and Max-SAT Be Different?." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11135

Markdown

[Liu and de Melo. "Should Algorithms for Random SAT and Max-SAT Be Different?." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/liu2017aaai-algorithms/) doi:10.1609/AAAI.V31I1.11135

BibTeX

@inproceedings{liu2017aaai-algorithms,
  title     = {{Should Algorithms for Random SAT and Max-SAT Be Different?}},
  author    = {Liu, Sixue and de Melo, Gerard},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {3915-3921},
  doi       = {10.1609/AAAI.V31I1.11135},
  url       = {https://mlanthology.org/aaai/2017/liu2017aaai-algorithms/}
}