Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Lower Bounds for Semialgebraic Range Searching and Stabbing Problems

Published: 18 April 2023 Publication History
  • Get Citation Alerts
  • Abstract

    In the semialgebraic range searching problem, we are given a set of n points in ℝd, and we want to preprocess the points such that for any query range belonging to a family of constant complexity semialgebraic sets (Tarski cells), all the points intersecting the range can be reported or counted efficiently. When the ranges are composed of simplices, the problem is well-understood: It can be solved using S(n) space and with Q(n) query time with \(S(n)Q(n)^d = \tilde{O}(n^d),\) where the \(\tilde{O}(\cdot)\) notation hides polylogarithmic factors and this trade-off is tight (up to no(1) factors). In particular, there exist “low space” structures that use O(n) space with O(n1-1/d}) query time [8, 25] and “fast query” structures that use O(nd) space with O(log n) query time [9]. However, for general semialgebraic ranges, only “low space” solutions are known, but the best solutions [7] match the same trade-off curve as simplex queries, with O(n) space and \(\tilde{O}(n^{1-1/d})\) query time. It has been conjectured that the same could be done for the “fast query” case, but this open problem has stayed unresolved.
    Here, we disprove this conjecture. We give the first nontrivial lower bounds for semialgebraic range searching and other related problems. More precisely, we show that any data structure for reporting the points between two concentric circles, a problem that we call 2D annulus reporting, with Q(n) query time must use \(S(n)=\overset{\scriptscriptstyle o}{\Omega }(n^3/Q(n)^5)\) space, where the \(\overset{\scriptscriptstyle o}{\Omega }(\cdot)\) notation hides \(n^{o(1)}\) factors, meaning, for \(Q(n)=\log ^{O(1)}n\) , \(\overset{\scriptscriptstyle o}{\Omega }(n^3)\) space must be used. In addition, we study the problem of reporting the subset of input points in a polynomial slab defined by \(\lbrace (x,y)\in \mathbb {R}^2:P(x)\le y\le P(x)+w\rbrace\) , where \(P(x)=\sum _{i=0}^\Delta a_i x^i\) is a univariate polynomial of degree Δ and \(a_0, \ldots , a_\Delta , w\) are given at the query time, a problem that we call polynomial slab reporting. For this, we show a space lower bound of \(\overset{\scriptscriptstyle o}{\Omega }(n^{\Delta +1}/Q(n)^{(\Delta +3)\Delta /2})\) , which implies that for \(Q(n)=\log ^{O(1)}n\) , we must use \(\overset{\scriptscriptstyle o}{\Omega }(n^{\Delta +1})\) space. We also consider the dual semialgebraic stabbing problems of semialgebraic range searching and present lower bounds for them. In particular, we show that in linear space, any data structure that solves 2D annulus stabbing problems must use \(\Omega (n^{2/3})\) query time. Note that this almost matches the upper bound obtained by lifting 2D annuli to 3D. Like semialgebraic range searching, we also present lower bounds for general polynomial slab stabbing problems. Again, our lower bounds are almost tight for linear size data structures in this case.

    Acknowledgment

    The authors thank Esther Ezra for sparking the initial ideas behind the proof.

    References

    [1]
    Peyman Afshani. 2012. Improved pointer machine and I/O lower bounds for simplex range reporting and related problems. In Proceedings of the 28th Annual Symposium on Computational Geometry (SoCG’12). ACM, New York, NY, 339–346.
    [2]
    Peyman Afshani and Pingan Cheng. 2022. An optimal lower bound for simplex range reporting. Retrieved from https://arXiv:2210.14736.
    [3]
    Peyman Afshani and Anne Driemel. 2018. On the complexity of range searching among curves. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM, Philadelphia, PA, 898–917.
    [4]
    Pankaj K. Agarwal. 2017. Simplex Range Searching and Its Variants: A Review. Springer International Publishing, Cham, 1–30.
    [5]
    Pankaj K. Agarwal, Boris Aronov, Esther Ezra, and Joshua Zahl. 2019. An efficient algorithm for generalized polynomial partitioning and its applications. In Proceedings of the 35th International Symposium on Computational Geometry. LIPIcs. Leibniz Int. Proc. Inform., Vol. 129. Schloss Dagstuhl. Leibniz-Zent. Inform., Wadern, Art. No. 5, 14.
    [6]
    Pankaj K. Agarwal and Jiří Matoušek. 1994. On range searching with semialgebraic sets. Discrete Comput. Geom. 11, 4 (1994), 393–418.
    [7]
    Pankaj K. Agarwal, Jiří Matoušek, and Micha Sharir. 2013. On range searching with semialgebraic sets. II. SIAM J. Comput. 42, 6 (2013), 2039–2062.
    [8]
    Timothy M. Chan. 2012. Optimal partition trees. Discrete Comput. Geom. 47, 4 (2012), 661–690.
    [9]
    Timothy M. Chan and Da Wei Zheng. 2022. Simplex range searching revisited: How to shave logs in multi-level data structures. Retrieved from https://arXiv:2210.10172.
    [10]
    Bernard Chazelle. 1989. Lower bounds on the complexity of polytope range searching. J. Amer. Math. Soc. 2, 4 (1989), 637–666.
    [11]
    Bernard Chazelle. 1990. Lower bounds for orthogonal range searching. I. The reporting case. J. Assoc. Comput. Mach. 37, 2 (1990), 200–212.
    [12]
    Bernard Chazelle. 1993. Cutting hyperplanes for divide-and-conquer. Discrete Comput. Geom. 9, 2 (Dec.1993), 145–158.
    [13]
    Bernard Chazelle. 2018. Cuttings. In Handbook of Data Structures and Applications, Dinesh P. Mehta and Sartaj Sahni (Eds.). Chapman and Hall/CRC. Second edition.
    [14]
    B. Chazelle and J. Friedman. 1990. A deterministic view of random sampling and its use in geometry. Combinatorica 10, 3 (1990), 229–249.
    [15]
    Bernard Chazelle and Burton Rosenberg. 1996. Simplex range reporting on a pointer machine. Comput. Geom. 5, 5 (1996), 237–247.
    [16]
    Bernard Chazelle and Emo Welzl. 1989. Quasi-optimal range searching in spaces of finite VC-dimension. Discrete Comput. Geom. 4, 5 (1989), 467–489.
    [17]
    Kenneth L. Clarkson. 1987. New applications of random sampling in computational geometry. Discrete Comput. Geom. 2, 2 (1987), 195–222.
    [18]
    Mark de Berg, Otfried Cheong, Marc J. van Kreveld, and Mark H. Overmars. 2008. Computational Geometry: Algorithms and Applications, 3rd ed. Springer. Retrieved from https://www.worldcat.org/oclc/227584184.
    [19]
    Zeev Dvir. 2009. On the size of Kakeya sets in finite fields. J. Amer. Math. Soc. 22, 4 (2009), 1093–1097.
    [20]
    Jacob E. Goodman, Joseph O’Rourke, and Csaba D. Tóth (Eds.). 2018. Handbook of Discrete and Computational Geometry. CRC Press, Boca Raton, FL. xxi+1927 pages. Third edition.
    [21]
    Larry Guth. 2015. Polynomial partitioning for a set of varieties. Math. Proc. Cambridge Philos. Soc. 159, 3 (2015), 459–469.
    [22]
    Larry Guth and Nets Hawk Katz. 2015. On the Erdős distinct distances problem in the plane. Ann. Math. (2) 181, 1 (2015), 155–190. DOI:
    [23]
    D. Haussler and E. Welzl. 1986. Epsilon-nets and simplex range queries. In Proceedings of the 2nd Annual Symposium on Computational Geometry (SCG’86). ACM, New York, NY, 61–71. DOI:
    [24]
    Jiří Matoušek. 1991. Cutting hyperplane arrangements. Discrete Comput. Geom. 6, 5 (1991), 385–406. DOI:
    [25]
    Jiří Matoušek. 1993. Range searching with efficient hierarchical cuttings. Discrete Comput. Geom. 10, 2 (1993), 157–182. DOI:
    [26]
    Jiří Matoušek and Zuzana Patáková. 2015. Multilevel polynomial partitions and simplified range searching. Discrete Comput. Geom. 54, 1 (2015), 22–41. DOI:
    [27]
    Emo Welzl. 1988. Partition trees for triangle counting and other range searching problems. In Proceedings of the 4th Annual Symposium on Computational Geometry. ACM, New York, 23–33. DOI:
    [28]
    Dan E. Willard. 1982. Polygon retrieval. SIAM J. Comput. 11, 1 (1982), 149–165. DOI:
    [29]
    A. C. Yao and F. F. Yao. 1985. A general approach to D-Dimensional geometric queries. In Proceedings of the 17th Annual ACM Symposium on Theory of Computing (STOC’85). ACM, New York, NY, 163–168. DOI:

    Cited By

    View all
    • (2024)Intersection Searching amid Tetrahedra in Four DimensionsDiscrete & Computational Geometry10.1007/s00454-024-00656-8Online publication date: 3-Jun-2024

    Index Terms

    1. Lower Bounds for Semialgebraic Range Searching and Stabbing Problems

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image Journal of the ACM
      Journal of the ACM  Volume 70, Issue 2
      April 2023
      329 pages
      ISSN:0004-5411
      EISSN:1557-735X
      DOI:10.1145/3587260
      Issue’s Table of Contents

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 18 April 2023
      Online AM: 03 January 2023
      Accepted: 19 November 2022
      Revised: 03 November 2022
      Received: 20 April 2021
      Published in JACM Volume 70, Issue 2

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Semialgebraic range searching
      2. lower bound
      3. computational geometry

      Qualifiers

      • Research-article

      Funding Sources

      • DFF (Det Frie Forskningsråd) of Danish Council for Independent Research

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)86
      • Downloads (Last 6 weeks)7

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Intersection Searching amid Tetrahedra in Four DimensionsDiscrete & Computational Geometry10.1007/s00454-024-00656-8Online publication date: 3-Jun-2024

      View Options

      Get Access

      Login options

      Full Access

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Full Text

      View this article in Full Text.

      Full Text

      HTML Format

      View this article in HTML Format.

      HTML Format

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media