ICALP 2012: Automata, Languages, and Programming pp 339-350 | Cite as
Universal Factor Graphs
Abstract
The factor graph of an instance of a symmetric constraint satisfaction problem on n Boolean variables and m constraints (CSPs such as k-SAT, k-AND, k-LIN) is a bipartite graph describing which variables appear in which constraints. The factor graph describes the instance up to the polarity of the variables, and hence there are up to 2 km instances of the CSP that share the same factor graph. It is well known that factor graphs with certain structural properties make the underlying CSP easier to either solve exactly (e.g., for tree structures) or approximately (e.g., for planar structures). We are interested in the following question: is there a factor graph for which if one can solve every instance of the CSP with this particular factor graph, then one can solve every instance of the CSP regardless of the factor graph (and similarly, for approximation)? We call such a factor graph universal. As one needs different factor graphs for different values of n and m, this gives rise to the notion of a family of universal factor graphs.
We initiate a systematic study of universal factor graphs, and present some results for max-kSAT. Our work has connections with the notion of preprocessing as previously studied for closest codeword and closest lattice-vector problems, with proofs for the PCP theorem, and with tests for the long code. Many questions remain open.
Keywords
Approximation Ratio Constraint Satisfaction Problem Factor Graph 3CNF Formula Sorting NetworkPreview
Unable to display preview. Download preview PDF.
References
- 1.Ajtai, M., Komlós, J., Szemerédi, E.: An O(n log n) sorting network. In: STOC 1983, pp. 1–9. ACM (1983)Google Scholar
- 2.Alekhnovich, M., Khot, S.A., Kindler, G., Vishnoi, N.K.: Hardness of approximating the closest vector problem with pre-processing. In: FOCS 2005, pp. 216–225 (2005)Google Scholar
- 3.Applebaum, B., Barak, B., Wigderson, A.: Public-key cryptography from different assumptions. In: STOC 2010, pp. 171–180 (2010)Google Scholar
- 4.Arora, S., Khot, S.A., Kolla, A., Steurer, D., Tulsiani, M., Vishnoi, N.K.: Unique games on expanding constraint graphs are easy. In: STOC 2008, pp. 21–28 (2008)Google Scholar
- 5.Bellare, M., Goldreich, O., Sudan, M.: Free bits, pcps, and nonapproximability—towards tight results. SIAM Journal on Computing 27(3), 804–915 (1998)MathSciNetMATHCrossRefGoogle Scholar
- 6.Bruck, J., Naor, M.: The hardness of decoding linear codes with preprocessing. IEEE Transactions on Information Theory 36(2), 381–385 (1990)MathSciNetMATHCrossRefGoogle Scholar
- 7.Creignou, N., Khanna, S., Sudan, M.: Complexity classifications of boolean constraint satisfaction problems. Society for Industrial and Applied Mathematics (2001)Google Scholar
- 8.Dinur, I.: The PCP theorem by gap amplification. J. ACM 54(3), 12 (2007)MathSciNetCrossRefGoogle Scholar
- 9.Feige, U., Jozeph, S.: Universal Factor Graphs (2012), http://arxiv.org/abs/1204.6484
- 10.Feige, U., Micciancio, D.: The inapproximability of lattice and coding problems with preprocessing. In: CCC 2002, pp. 32–40 (2002)Google Scholar
- 11.Feige, U., Kim, J.H., Ofek, E.: Witnesses for non-satisfiability of dense random 3CNF formulas. In: FOCS 2006, pp. 497–508 (2006)Google Scholar
- 12.Håstad, J.: Some optimal inapproximability results. J. ACM 48, 798–859 (2001)MathSciNetMATHCrossRefGoogle Scholar
- 13.Impagliazzo, R., Paturi, R., Zane, F.: Which problems have strongly exponential complexity? J. Comput. Syst. Sci. 63, 512–530 (2001)MathSciNetMATHCrossRefGoogle Scholar
- 14.Khot, S.: On the power of unique 2-prover 1-round games. In: STOC 2002, pp. 767–775 (2002)Google Scholar
- 15.Khot, S., Popat, P., Vishnoi, N.: \(2^{\log^{1 - \epsilon} n}\) Hardness for Closest Vector Problem with Preprocessing. ECCC Report, 119 (2011), (To appear in STOC 2012)Google Scholar
- 16.Kolla, A.: Spectral algorithms for unique games. In: CCC 2010, pp. 122–130 (2010)Google Scholar
- 17.Lichtenstein, D.: Planar formulae and their uses. SIAM Journal on Computing 11(2), 329–343 (1982)MathSciNetMATHCrossRefGoogle Scholar
- 18.Lingas, A., Pinter, R., Rivest, R., Shamir, A.: Minimum edge length decompositions of rectilinear figure. In: Proceedings of 12th Annual Allerton Conference on Communication, Control, and Computing (1982)Google Scholar
- 19.Radhakrishnan, J., Sudan, M.: On Dinur’s proof of the PCP theorem. B. AMS 44(1), 19–61 (2007)MathSciNetMATHCrossRefGoogle Scholar
- 20.Raghavendra, P.: Optimal algorithms and inapproximability results for every CSP? In: STOC 2008, pp. 245–254 (2008)Google Scholar
- 21.Schaefer, T.J.: The complexity of satisfiability problems. In: STOC 1978, pp. 216–226 (1978)Google Scholar
- 22.Trevisan, L.: Approximation algorithms for unique games. In: FOCS 2005, pp. 197–205 (2005)Google Scholar