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Solving and Sampling with Many Solutions: Satisfiability and Other Hard Problems

Authors Jean Cardinal, Jerri Nummenpalo, Emo Welzl

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  • Satisfiability
  • Sampling
  • Parameterized complexity

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Abstract

We investigate parameterizing hard combinatorial problems by the size of the solution set compared to all solution candidates. Our main result is a uniform sampling algorithm for satisfying assignments of 2-CNF formulas that runs in expected time O^*(eps^{-0.617}) where eps is the fraction of assignments that are satisfying. This improves significantly over the trivial sampling bound of expected Theta^*(eps^{-1}), and on all previous algorithms whenever eps = Omega(0.708^n). We also consider algorithms for 3-SAT with an eps fraction of satisfying assignments, and prove that it can be solved in O^*(eps^{-2.27}) deterministic time, and in O^*(eps^{-0.936}) randomized time. Finally, to further demonstrate the applicability of this framework, we also explore how similar techniques can be used for vertex cover problems.

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Jean Cardinal, Jerri Nummenpalo, and Emo Welzl. Solving and Sampling with Many Solutions: Satisfiability and Other Hard Problems. In 12th International Symposium on Parameterized and Exact Computation (IPEC 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 89, pp. 11:1-11:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.IPEC.2017.11

Author Details

Jean Cardinal
Jerri Nummenpalo
Emo Welzl

References

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