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Disjointness through the lens of Vapnik-Chervonenkis dimension: sparsity and beyond. (English) Zbl 1494.94001

Summary: The disjointness problem – where Alice and Bob are given two subsets of \(\{1, \dots, n\}\) and they have to check if their sets intersect – is a central problem in the world of communication complexity. While both deterministic and randomized communication complexities for this problem are known to be \(\Theta(n)\), it is also known that if the sets are assumed to be drawn from some restricted set systems then the communication complexity can be much lower. In this work, we explore how communication complexity measures change with respect to the complexity of the underlying set system. The complexity measure for the set system that we use in this work is the Vapnik-Chervonenkis (VC) dimension. More precisely, on any set system with VC dimension bounded by \(d\), we analyze how large can the deterministic and randomized communication complexities be, as a function of \(d\) and \(n\). The \(d\)-sparse set disjointness problem, where the sets have size at most \(d\), is one such set system with VC dimension \(d\). The deterministic and the randomized communication complexities of the \(d\)-sparse set disjointness problem have been well studied and are known to be \(\Theta \left( d \log \left({n}/{d}\right)\right)\) and \(\Theta(d)\), respectively, in the multi-round communication setting. In this paper, we address the question of whether the randomized communication complexity of the disjointness problem is always upper bounded by a function of the VC dimension of the set system, and does there always exist a gap between the deterministic and randomized communication complexities of the disjointness problem for set systems with small VC dimension. We construct two natural set systems of VC dimension \(d\), motivated from geometry. Using these set systems, we show that the deterministic and randomized communication complexity can be \(\widetilde{\Theta}\left(d\log \left( n/d \right)\right)\) for set systems of VC dimension \(d\) and this matches the deterministic upper bound for all set systems of VC dimension \(d\). We also study the deterministic and randomized communication complexities of the set intersection problem when sets belong to a set system of bounded VC dimension. We show that there exist set systems of VC dimension \(d\) such that both deterministic and randomized (one-way and multi-round) complexities for the set intersection problem can be as high as \(\Theta\left( d\log \left( n/d \right) \right)\).

MSC:

94A05 Communication theory
52C35 Arrangements of points, flats, hyperplanes (aspects of discrete geometry)

References:

[1] Tom M. Apostol (1976). Introduction to Analytic Number Theory. Springer, New York, NY, 1st edition. · Zbl 0335.10001
[2] Ziv Bar-Yossef, TS; Jayram, Ravi Kumar; Sivakumar, D., An Information Statistics Approach to Data Stream and Communication Complexity, Journal of Computer and System Sciences, 68, 4, 702-732 (2004) · Zbl 1074.68022 · doi:10.1016/j.jcss.2003.11.006
[3] Mark Braverman, Ankit Garg, Denis Pankratov & Omri Weinstein (2013). From information to exact communication. In Symposium on Theory of Computing Conference, STOC, 151-160. · Zbl 1293.68160
[4] Brody, Joshua; Chakrabarti, Amit; Kondapally, Ranganath; Woodruff, David P.; Yaroslavtsev, Grigory, Beyond Set Disjointness: The Communication Complexity of Finding the Intersection, 106-113 (2014), PODC: In ACM Symposium on Principles of Distributed Computing, PODC · Zbl 1321.68298
[5] Komaravolu Chandrasekharan (1968). Introduction to Analytic Number Theory. Springer, Berlin, Heidelberg, 1st edition. · Zbl 0169.37502
[6] Bernard Chazelle (2001). The Discrepancy Method: Randomness and Complexity. Cambridge University Press. · Zbl 0960.65149
[7] Dasgupta, Anirban; Kumar, Ravi; Sivakumar, D., Sparse and Lopsided Set Disjointness via Information Theory, 517-528 (2012), RANDOM: In International Workshop on Randomization and Computation, RANDOM · Zbl 1372.68106
[8] Johan Håstad & Avi Wigderson, The Randomized Communication Complexity of Set Disjointness, Theory of Computing, 3, 1, 211-219 (2007) · Zbl 1213.68332 · doi:10.4086/toc.2007.v003a011
[9] Jayram, TS; Kumar, Ravi; Sivakumar, D., Two Applications of Information Complexity, 673-682 (2003), STOC: In ACM Symposium on Theory of Computing, STOC · Zbl 1192.68297
[10] Bala Kalyanasundaram & Georg Schnitger, The Probabilistic Communication Complexity of Set Intersection, SIAM Journal on Discrete Mathematics, 5, 4, 545-557 (1992) · Zbl 0760.68040 · doi:10.1137/0405044
[11] Eyal Kushilevitz & Noam Nisan, Communication Complexity (1996), Cambridge University Press · Zbl 0869.68048
[12] Jiri Matousek (2009). Geometric Discrepancy: An Illustrated Guide, volume 18. Springer Science & Business Media. · Zbl 0930.11060
[13] Jiri Matousek (2013). Lectures on Discrete Geometry, volume 212. Springer Science & Business Media. · Zbl 0999.52006
[14] Miltersen, Peter Bro; Nisan, Noam; Safra, Shmuel; Wigderson, Avi, On Data Structures and Asymmetric Communication Complexity, Journal of Computer and System Sciences, 57, 1, 37-49 (1998) · Zbl 0920.68042 · doi:10.1006/jcss.1998.1577
[15] Ilan Newman (1991). Private vs. Common Random Bits in Communication Complexity. Information Processing Letters39(2), 67-71. · Zbl 0735.68034
[16] Pach, János; Agarwal, Pankaj K., Combinatorial Geometry, (2011), John Wiley & Sons · Zbl 0881.52001
[17] Anup Rao & Amir Yehudayoff, Communication Complexity: and Applications (2020), Cambridge University Press · Zbl 1436.68005
[18] Razborov, Alexander A., On the Distributional Complexity of Disjointness, Theoretical Computer Science, 106, 2, 385-390 (1992) · Zbl 0787.68055 · doi:10.1016/0304-3975(92)90260-M
[19] Guy Robin (1983). Estimation de la fonction de Tchebychef \(\theta\) sur le \(k\)-ième nombre premier et grandes valeurs de la fonction \(\omega (n)\) nombre de diviseurs premiers de n. Acta Arithmetica42(4), 367-389. · Zbl 0475.10034
[20] Roughgarden, Tim, Communication Complexity (for Algorithm Designers), Foundations and Trends in Theoretical Computer Science, 11, 3-4, 217-404 (2016) · Zbl 1470.68007 · doi:10.1561/0400000076
[21] Mert Saglam & Gábor Tardos, On the Communication Complexity of Sparse Set Disjointness and Exists-Equal Problems, 678-687 (2013), FOCS: In IEEE Symposium on Foundations of Computer Science, FOCS
[22] Sauer, N., On the Density of Families of Sets, Journal of Combinatorial Theory, Series A, 13, 1, 145-147 (1972) · Zbl 0248.05005 · doi:10.1016/0097-3165(72)90019-2
[23] Shelah, S., A Combinatorial Problem, Stability and Order for Models and Theories in Infinitary Languages, Pacific Journal of Mathematics, 41, 247-261 (1972) · Zbl 0239.02024 · doi:10.2140/pjm.1972.41.247
[24] Vapnik, VN; Chervonenkis, AY, On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities, Theory of Probability and its Applications, 16, 2, 264-280 (1971) · Zbl 0247.60005 · doi:10.1137/1116025
[25] Andrew Chi-Chih Yao, Some Complexity Questions Related to Distributive Computing (Preliminary Report), 209-213 (1979), STOC: In ACM Symposium on Theory of Computing, STOC
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