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Tight hardness results for minimizing discrepancy

Published: 23 January 2011 Publication History

Abstract

In the Discrepancy problem, we are given M sets {S1,..., SM} on N elements. Our goal is to find an assignment χ of {−1, + 1} values to elements, so as to minimize the maximum discrepancy maxj | ΣiεSj χ(i)|. Recently, Bansal gave an efficient algorithm for achieving O(√N) discrepancy for any set system where M = O(N) [Ban10], giving a constructive version of Spencer's proof that the discrepancy of any set system is at most O(√N) for this range of M [Spe85].
We show that from the perspective of computational efficiency, these results are tight for general set systems where M = O(N). Specifically, we show that it is NP-hard to distinguish between such set systems with discrepancy zero and those with discrepancy Ω(√N). This means that even if the optimal solution has discrepancy zero, we cannot hope to efficiently find a coloring with discrepancy o(√N). We also consider the hardness of the Discrepancy problem on sets with bounded shatter function, and show that the upper bounds due to Matoušek [Mat95] are tight for these sets systems as well.
The hardness results in both settings are obtained from a common framework: we compose a family of high discrepancy set systems with set systems for which it is NP-hard to distinguish instances with discrepancy zero from instances in which a large number of the sets (i.e. constant fraction of the sets) have non-zero discrepancy. Our composition amplifies this zero versus non-zero gap.

References

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{Ale90} R. Alexander. Geometric methods in the study of irregularities of distribution. Combinatorica, 10(2):115--136, 1990.
[2]
{Ban10} Nikhil Bansal. Constructive algorithms for discrepancy minimization. CoRR, abs/1002.2259, 2010.
[3]
{BS96} J. Beck and V. T. Sós. Discrepancy theory. In Handbook of combinatorics (vol. 2), page 1446. MIT Press, 1996.
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{CGW05} Moses Charikar, Venkatesan Guruswami, and Anthony Wirth. Clustering with qualitative information. J. Comput. Syst. Sci., 71(3):360--383, 2005.
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{Cha91} Bernard Chazelle. The Discrepancy Method. Cambridge University Press, 1991.
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{CMS95} B. Chazelle, J. Matoušek, and M. Sharir. An elementary approach to lower bounds in geometric discrepancy. Discrete and Computational Geometry, 13(1):363--381, 1995.
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{Gur03} Venkatesan Guruswami. Inapproximability results for set splitting and satisfiability problems with no mixed clauses. Algorithmica, 38(3):451--469, 2003.
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{Mat95} J. Matoušek. Tight upper bounds for the discrepancy of half-spaces. Discrete and Computational Geometry, 13(1):593--601, 1995.
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{Mat10} J. Matousek. Geometric Discrepancy: An Illustrated Guide. Springer Verlag, 2010.
[10]
{Spe85} Joel Spencer. Six standard deviations suffice. Trans. Amer. Math. Soc., 289:679--706, 1985.

Cited By

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  • (2018)The Gram-Schmidt walk: a cure for the Banaszczyk bluesProceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing10.1145/3188745.3188850(587-597)Online publication date: 20-Jun-2018
  • (2016)Approximation-Friendly Discrepancy RoundingProceedings of the 18th International Conference on Integer Programming and Combinatorial Optimization - Volume 968210.1007/978-3-319-33461-5_31(375-386)Online publication date: 1-Jun-2016
  • (2015)Strong inapproximability results on balanced rainbow-colorable hypergraphsProceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete algorithms10.5555/2722129.2722185(822-836)Online publication date: 4-Jan-2015
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Published In

cover image ACM Conferences
SODA '11: Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete algorithms
January 2011
1785 pages

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Society for Industrial and Applied Mathematics

United States

Publication History

Published: 23 January 2011

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SODA '11
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SODA '11: 22nd ACM-SIAM Symposium on Discrete Algorithms
January 23 - 25, 2011
California, San Francisco

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Overall Acceptance Rate 411 of 1,322 submissions, 31%

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Cited By

View all
  • (2018)The Gram-Schmidt walk: a cure for the Banaszczyk bluesProceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing10.1145/3188745.3188850(587-597)Online publication date: 20-Jun-2018
  • (2016)Approximation-Friendly Discrepancy RoundingProceedings of the 18th International Conference on Integer Programming and Combinatorial Optimization - Volume 968210.1007/978-3-319-33461-5_31(375-386)Online publication date: 1-Jun-2016
  • (2015)Strong inapproximability results on balanced rainbow-colorable hypergraphsProceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete algorithms10.5555/2722129.2722185(822-836)Online publication date: 4-Jan-2015
  • (2015)Approximating hereditary discrepancy via small width ellipsoidsProceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete algorithms10.5555/2722129.2722153(324-336)Online publication date: 4-Jan-2015
  • (2014)Better algorithms and hardness for broadcast scheduling via a discrepancy approachProceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms10.5555/2634074.2634079(55-71)Online publication date: 5-Jan-2014
  • (2014)Integer feasibility of random polytopesProceedings of the 5th conference on Innovations in theoretical computer science10.1145/2554797.2554838(449-458)Online publication date: 12-Jan-2014
  • (2014)Toric algebra of hypergraphsJournal of Algebraic Combinatorics: An International Journal10.1007/s10801-013-0444-y39:1(187-208)Online publication date: 1-Feb-2014
  • (2013)Bin Packing via Discrepancy of PermutationsACM Transactions on Algorithms10.1145/2483699.24837049:3(1-15)Online publication date: 1-Jun-2013
  • (2012)Mergeable summariesProceedings of the 31st ACM SIGMOD-SIGACT-SIGAI symposium on Principles of Database Systems10.1145/2213556.2213562(23-34)Online publication date: 21-May-2012
  • (2011)Comparing distributions and shapes using the kernel distanceProceedings of the twenty-seventh annual symposium on Computational geometry10.1145/1998196.1998204(47-56)Online publication date: 13-Jun-2011

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