Abstract
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 \(\Theta\left( d\log \left( n/d \right) \right)\).
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References
Tom M. Apostol (1976). Introduction to Analytic Number Theory. Springer, New York, NY, 1st edition.
Ziv Bar-Yossef, T. S. Jayram, Ravi Kumar & D. Sivakumar (2004). An Information Statistics Approach to Data Stream and Communication Complexity. Journal of Computer and System Sciences 68(4), 702–732.
Mark Braverman, Ankit Garg, Denis Pankratov & Omri Weinstein (2013). From information to exact communication. In Symposium on Theory of Computing Conference, STOC, 151–160.
Joshua Brody, Amit Chakrabarti, Ranganath Kondapally, David P. Woodruff & Grigory Yaroslavtsev (2014). Beyond Set Disjointness: The Communication Complexity of Finding the Intersection. In ACM Symposium on Principles of Distributed Computing, PODC, 106–113.
Komaravolu Chandrasekharan (1968). Introduction to Analytic Number Theory. Springer, Berlin, Heidelberg, 1st edition.
Bernard Chazelle (2001). The Discrepancy Method: Randomness and Complexity. Cambridge University Press.
Anirban Dasgupta, Ravi Kumar & D. Sivakumar (2012). Sparse and Lopsided Set Disjointness via Information Theory. In International Workshop on Randomization and Computation, RANDOM, 517–528.
Johan Håstad & Avi Wigderson (2007). The Randomized Communication Complexity of Set Disjointness. Theory of Computing 3(1), 211–219.
T. S. Jayram, Ravi Kumar & D. Sivakumar (2003). Two Applications of Information Complexity. In ACM Symposium on Theory of Computing, STOC, 673–682.
Bala Kalyanasundaram & Georg Schnitger (1992). The Probabilistic Communication Complexity of Set Intersection. SIAM Journal on Discrete Mathematics 5(4), 545–557.
Eyal Kushilevitz & Noam Nisan (1996). Communication Complexity. Cambridge University Press.
Jiri Matousek (2009). Geometric Discrepancy: An Illustrated Guide, volume 18. Springer Science & Business Media.
Jiri Matousek (2013). Lectures on Discrete Geometry, volume 212. Springer Science & Business Media.
Peter Bro Miltersen, Noam Nisan, Shmuel Safra & Avi Wigderson (1998). On Data Structures and Asymmetric Communication Complexity. Journal of Computer and System Sciences 57(1), 37–49.
Ilan Newman (1991). Private vs. Common Random Bits in Communication Complexity. Information Processing Letters 39(2), 67–71.
János Pach & Pankaj K. Agarwal (2011). Combinatorial Geometry, volume 37. John Wiley & Sons.
Anup Rao & Amir Yehudayoff (2020). Communication Complexity: and Applications. Cambridge University Press.
Alexander A. Razborov (1992). On the Distributional Complexity of Disjointness. Theoretical Computer Science 106(2), 385–390.
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 Arithmetica 42(4), 367–389.
Tim Roughgarden (2016). Communication Complexity (for Algorithm Designers). Foundations and Trends in Theoretical Computer Science 11(3-4), 217–404.
Mert Saglam & Gábor Tardos (2013). On the Communication Complexity of Sparse Set Disjointness and Exists-Equal Problems. In IEEE Symposium on Foundations of Computer Science, FOCS, 678–687.
N. Sauer (1972). On the Density of Families of Sets. Journal of Combinatorial Theory, Series A 13(1), 145–147.
S. Shelah (1972). A Combinatorial Problem, Stability and Order for Models and Theories in Infinitary Languages. Pacific Journal of Mathematics 41, 247–261.
V. N. Vapnik & A. Y. Chervonenkis (1971). On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities. Theory of Probability and its Applications 16(2), 264–280.
Andrew Chi-Chih Yao (1979). Some Complexity Questions Related to Distributive Computing (Preliminary Report). In ACM Symposium on Theory of Computing, STOC, 209–213.
Acknowledgements
Part of this work was done when Anup Bhattacharya was supported by SERB-National Post Doctoral Fellowship, India, and Arijit Ghosh was supported in part by Ramanujan Fellowship (No. SB/S2/RJN-064/2015), India. Gopinath Mishra is supported in part by the Centre for Discrete Mathematics and its Applications (DIMAP) and by EPSRC award EP/V01305X/1. The authors would like to thank Sudeshna Kolay and Arijit Bishnu for the many discussions in the early stages of this work.
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Bhattacharya, A., Chakraborty, S., Ghosh, A. et al. Disjointness through the Lens of Vapnik–Chervonenkis Dimension: Sparsity and Beyond. comput. complex. 31, 9 (2022). https://doi.org/10.1007/s00037-022-00225-6
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DOI: https://doi.org/10.1007/s00037-022-00225-6