Abstract
A theorem of Håstad shows that for every constraint satisfaction problem (CSP) over a fixed size domain, instances where each variable appears in at most O(1) constraints admit a non-trivial approximation algorithm, in the sense that one can beat (by an additive constant) the approximation ratio achieved by the naive algorithm that simply picks a random assignment. We consider the analogous question for ordering CSPs, where the goal is to find a linear ordering of the variables to maximize the number of satisfied constraints, each of which stipulates some restriction on the local order of the involved variables. It was shown recently that without the bounded occurrence restriction, for every ordering CSP it is Unique Games-hard to beat the naive random ordering algorithm.
In this work, we prove that the CSP with monotone ordering constraints \(x_{i_1} < x_{i_2} < \cdots < x_{i_k}\) of arbitrary arity k can be approximated beyond the random ordering threshold 1/k! on bounded occurrence instances. We prove a similar result for all ordering CSPs, with arbitrary payoff functions, whose constraints have arity at most 3. Our method is based on working with a carefully defined Boolean CSP that serves as a proxy for the ordering CSP. One of the main technical ingredients is to establish that certain Fourier coefficients of this proxy constraint have substantial mass. These are then used to guarantee a good ordering via an algorithm that finds a good Boolean assignment to the variables of a low-degree bounded occurrence multilinear polynomial. Our algorithm for the latter task is similar to Håstad’s earlier method but is based on a greedy choice that achieves a better performance guarantee.
This research was supported in part by NSF CCF 1115525 and US-Israel BSF grant 2008293. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. A full version of this paper can be found hat http://eccc.hpi-web.de/report/2012/074/
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Austrin, P., Mossel, E.: Approximation resistant predicates from pairwise independence. Computational Complexity 18(2), 249–271 (2009); Preliminary version in CCC 2008
Bodirsky, M., Kára, J.: The complexity of temporal constraint satisfaction problems. J. ACM 57, 9:1–9:41 (2010)
Berger, B., Shor, P.W.: Tight bounds for the Maximum Acyclic Subgraph problem. J. Algorithms 25(1), 1–18 (1997)
Charikar, M., Guruswami, V., Manokaran, R.: Every permutation CSP of arity 3 is approximation resistant. In: Proceedings of the 24th IEEE Conference on Computational Complexity, pp. 62–73 (July 2009)
Chor, B., Sudan, M.: A geometric approach to betweenness. SIAM J. Discrete Math. 11(4), 511–523 (1998)
Engebretsen, L., Guruswami, V.: Is constraint satisfaction over two variables always easy? Random Structures and Algorithms 25(2), 150–178 (2004)
Guruswami, V., Håstad, J., Manokaran, R., Raghavendra, P., Charikar, M.: Beating the random ordering is hard: Every ordering CSP is approximation resistant. SIAM J. Comput. 40(3), 878–914 (2011)
Guttmann, W., Maucher, M.: Variations on an Ordering Theme with Constraints. In: Navarro, G., Bertossi, L.E., Kohayakawa, Y. (eds.) IFIP TCS. IFIP, vol. 209, pp. 77–90. Springer, Boston (2006)
Guruswami, V., Manokaran, R., Raghavendra, P.: Beating the random ordering hard: Inapproximability of maximum acyclic subgraph. In: Proceedings of the 49th IEEE Symposium on Foundations of Computer Science, pp. 573–582 (2008)
Goemans, M.X., Williamson, D.P.: Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM 42(6), 1115–1145 (1995)
Håstad, J.: On bounded occurrence constraint satisfaction. Inf. Process. Lett. 74(1-2), 1–6 (2000)
Håstad, J.: Some optimal inapproximability results. Journal of the ACM 48(4), 798–859 (2001)
Håstad, J.: Every 2-CSP allows nontrivial approximation. Computational Complexity 17(4), 549–566 (2008)
Makarychev, Y.: Simple linear time approximation algorithm for betweenness. Microsoft Research Technical Report MSR-TR-2009-74 (2009)
Raghavendra, P.: Optimal algorithms and inapproximability results for every CSP? In: Proceedings of the 40th ACM Symposium on Theory of Computing, pp. 245–254 (2008)
Trevisan, L.: Non-approximability results for optimization problems on bounded degree instances. In: Proceedings of the 33rd Annual ACM Symposium on Theory of Computing, pp. 453–461 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guruswami, V., Zhou, Y. (2012). Approximating Bounded Occurrence Ordering CSPs. In: Gupta, A., Jansen, K., Rolim, J., Servedio, R. (eds) Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX RANDOM 2012 2012. Lecture Notes in Computer Science, vol 7408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32512-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-32512-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32511-3
Online ISBN: 978-3-642-32512-0
eBook Packages: Computer ScienceComputer Science (R0)