License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.IPEC.2021.5
URN: urn:nbn:de:0030-drops-153886
URL: https://drops.dagstuhl.de/opus/volltexte/2021/15388/
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Arvind, Vikraman ; Guruswami, Venkatesan

CNF Satisfiability in a Subspace and Related Problems

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Abstract

We introduce the problem of finding a satisfying assignment to a CNF formula that must further belong to a prescribed input subspace. Equivalent formulations of the problem include finding a point outside a union of subspaces (the Union-of-Subspace Avoidance (USA) problem), and finding a common zero of a system of polynomials over 𝔽₂ each of which is a product of affine forms.

We focus on the case of k-CNF formulas (the k-Sub-Sat problem). Clearly, k-Sub-Sat is no easier than k-SAT, and might be harder. Indeed, via simple reductions we show that 2-Sub-Sat is NP-hard, and W[1]-hard when parameterized by the co-dimension of the subspace. We also prove that the optimization version Max-2-Sub-Sat is NP-hard to approximate better than the trivial 3/4 ratio even on satisfiable instances.

On the algorithmic front, we investigate fast exponential algorithms which give non-trivial savings over brute-force algorithms. We give a simple branching algorithm with running time (1.5)^r for 2-Sub-Sat, where r is the subspace dimension, as well as an O^*(1.4312)ⁿ time algorithm where n is the number of variables.

Turning to k-Sub-Sat for k ⩾ 3, while known algorithms for solving a system of degree k polynomial equations already imply a solution with running time ≈ 2^{r(1-1/2k)}, we explore a more combinatorial approach. Based on an analysis of critical variables (a key notion underlying the randomized k-SAT algorithm of Paturi, Pudlak, and Zane), we give an algorithm with running time ≈ {n choose {⩽t}} 2^{n-n/k} where n is the number of variables and t is the co-dimension of the subspace. This improves upon the running time of the polynomial equations approach for small co-dimension. Our combinatorial approach also achieves polynomial space in contrast to the algebraic approach that uses exponential space. We also give a PPZ-style algorithm for k-Sub-Sat with running time ≈ 2^{n-n/2k}. This algorithm is in fact oblivious to the structure of the subspace, and extends when the subspace-membership constraint is replaced by any constraint for which partial satisfying assignments can be efficiently completed to a full satisfying assignment. Finally, for systems of O(n) polynomial equations in n variables over 𝔽₂, we give a fast exponential algorithm when each polynomial has bounded degree irreducible factors (but can otherwise have large degree) using a degree reduction trick.

BibTeX - Entry

@InProceedings{arvind_et_al:LIPIcs.IPEC.2021.5,
  author =	{Arvind, Vikraman and Guruswami, Venkatesan},
  title =	{{CNF Satisfiability in a Subspace and Related Problems}},
  booktitle =	{16th International Symposium on Parameterized and Exact Computation (IPEC 2021)},
  pages =	{5:1--5:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-216-7},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{214},
  editor =	{Golovach, Petr A. and Zehavi, Meirav},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/15388},
  URN =		{urn:nbn:de:0030-drops-153886},
  doi =		{10.4230/LIPIcs.IPEC.2021.5},
  annote =	{Keywords: CNF Satisfiability, Exact exponential algorithms, Hardness results}
}

Keywords: CNF Satisfiability, Exact exponential algorithms, Hardness results
Collection: 16th International Symposium on Parameterized and Exact Computation (IPEC 2021)
Issue Date: 2021
Date of publication: 22.11.2021


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