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DOI: 10.4230/LIPIcs.SoCG.2025.25

Approximating Klee’s Measure Problem and a Lower Bound for Union Volume Estimation

Authors: Karl Bringmann, Kasper Green Larsen, André Nusser, Eva Rotenberg, and Yanheng Wang

Published in: LIPIcs, Volume 332, 41st International Symposium on Computational Geometry (SoCG 2025)

Abstract

Union volume estimation is a classical algorithmic problem. Given a family of objects O₁,…,O_n ⊂ ℝ^d, we want to approximate the volume of their union. In the special case where all objects are boxes (also called hyperrectangles) this is known as Klee’s measure problem. The state-of-the-art (1+ε)-approximation algorithm [Karp, Luby, Madras '89] for union volume estimation as well as Klee’s measure problem in constant dimension d uses a total of O(n/ε²) queries of three types: (i) determine the volume of O_i; (ii) sample a point uniformly at random from O_i; and (iii) ask whether a given point is contained in O_i. First, we show that if an algorithm learns about the objects only through these types of queries, then Ω(n/ε²) queries are necessary. In this sense, the complexity of [Karp, Luby, Madras '89] is optimal. Our lower bound holds even if the objects are equiponderous axis-aligned polygons in ℝ², if the containment query allows arbitrary (not necessarily sampled) points, and if the algorithm can spend arbitrary time and space examining the query responses. Second, we provide a more efficient approximation algorithm for Klee’s measure problem, which improves the running time from O(n/ε²) to O((n+1/ε²) ⋅ log^{O(d)} (n)). We circumvent our lower bound by exploiting the geometry of boxes in various ways: (1) We sort the boxes into classes of similar shapes after inspecting their corner coordinates. (2) With orthogonal range searching, we show how to sample points from the union of boxes in each class, and how to merge samples from different classes. (3) We bound the amount of wasted work by arguing that most pairs of classes have a small intersection.

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Karl Bringmann, Kasper Green Larsen, André Nusser, Eva Rotenberg, and Yanheng Wang. Approximating Klee’s Measure Problem and a Lower Bound for Union Volume Estimation. In 41st International Symposium on Computational Geometry (SoCG 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 332, pp. 25:1-25:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bringmann_et_al:LIPIcs.SoCG.2025.25,
  author =	{Bringmann, Karl and Larsen, Kasper Green and Nusser, Andr\'{e} and Rotenberg, Eva and Wang, Yanheng},
  title =	{{Approximating Klee’s Measure Problem and a Lower Bound for Union Volume Estimation}},
  booktitle =	{41st International Symposium on Computational Geometry (SoCG 2025)},
  pages =	{25:1--25:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-370-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{332},
  editor =	{Aichholzer, Oswin and Wang, Haitao},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2025.25},
  URN =		{urn:nbn:de:0030-drops-231778},
  doi =		{10.4230/LIPIcs.SoCG.2025.25},
  annote =	{Keywords: approximation, volume of union, union of objects, query complexity}
}

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Track A: Algorithms, Complexity and Games

DOI: 10.4230/LIPIcs.ICALP.2022.103

A Perfect Sampler for Hypergraph Independent Sets

Authors: Guoliang Qiu, Yanheng Wang, and Chihao Zhang

Published in: LIPIcs, Volume 229, 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022)

Abstract

The problem of uniformly sampling hypergraph independent sets is revisited. We design an efficient perfect sampler for the problem under a similar condition of the asymmetric Lovász local lemma. When specialized to d-regular k-uniform hypergraphs on n vertices, our sampler terminates in expected O(n log n) time provided d ≤ c⋅ 2^{k/2} where c > 0 is a constant, matching the rapid mixing condition for Glauber dynamics in Hermon, Sly and Zhang [Hermon et al., 2019]. The analysis of our algorithm is simple and clean.

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Guoliang Qiu, Yanheng Wang, and Chihao Zhang. A Perfect Sampler for Hypergraph Independent Sets. In 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 229, pp. 103:1-103:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{qiu_et_al:LIPIcs.ICALP.2022.103,
  author =	{Qiu, Guoliang and Wang, Yanheng and Zhang, Chihao},
  title =	{{A Perfect Sampler for Hypergraph Independent Sets}},
  booktitle =	{49th International Colloquium on Automata, Languages, and Programming (ICALP 2022)},
  pages =	{103:1--103:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-235-8},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{229},
  editor =	{Boja\'{n}czyk, Miko{\l}aj and Merelli, Emanuela and Woodruff, David P.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2022.103},
  URN =		{urn:nbn:de:0030-drops-164442},
  doi =		{10.4230/LIPIcs.ICALP.2022.103},
  annote =	{Keywords: Coupling from the past, Markov chains, Hypergraph independent sets}
}

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Approximating Klee’s Measure Problem and a Lower Bound for Union Volume Estimation

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A Perfect Sampler for Hypergraph Independent Sets

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