ACM Home Page
Please provide us with feedback. Feedback
A (slightly) faster algorithm for klee's measure problem
Full text PdfPdf (201 KB)
Source
Annual Symposium on Computational Geometry archive
Proceedings of the twenty-fourth annual symposium on Computational geometry table of contents
College Park, MD, USA
SESSION: 3 table of contents
Pages 94-100  
Year of Publication: 2008
ISBN:978-1-60558-071-5
Author
Timothy M. Chan  University of Waterloo, Waterloo, ON, Canada
Sponsors
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 11,   Downloads (12 Months): 92,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1377676.1377693
What is a DOI?

ABSTRACT

Given n axis-parallel boxes in a fixed dimension d ≥ 3, how efficiently can we compute the volume of the union? This standard problem in computational geometry, commonly referred to as Klee's measure problem, can be solved in time O(nd/2 log n) by an algorithm of Overmars and Yap (FOCS 1988). We give the first (albeit small) improvement: our new algorithm runs in time nd/22O(log*n), where log* denotes the iterated logarithm.

For the related problem of computing the depth in an arrangement of n boxes, we further improve the time bound to near O(nd/2 logd/2-1 n), ignoring log\log n factors. Other applications and lower-bound possibilities are discussed. The ideas behind the improved algorithms are simple.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
 
2
I. Baran, E.D. Demaine, and M. Patraşcu. Subquadratic algorithms for 3SUM. In Proc. 9th Workshop Algorithms Data Struct. Lect. Notes Comput. Sci., vol. 3608, Springer-Verlag, pages 409--421, 2005.
3
 
4
J.-D. Boissonnat, M. Sharir, B. Tagansky, and M. Yvinec. Voronoi diagrams in higher dimensions under certain polyhedral distance functions. Discrete Comput. Geom. 19:473--484, 1998.
 
5
 
6
T.M. Chan. Geometric applications of a randomized optimization technique. Discrete Comput. Geom. 22:547--567, 1999.
 
7
8
 
9
L.P. Chew, D. Dor, A. Efrat, and K. Kedem. Geometric pattern matching in d-dimensional space. Discrete Comput. Geom. 21:257--274, 1999.
 
10
 
11
12
 
13
 
14
D. Eppstein and J. Erickson. Iterated nearest neighbors and finding minimal polytopes. Discrete Comput. Geom.11:321--350, 1994.
 
15
 
16
J. Erickson. Klee's measure problem. http://compgeom.cs.uiuc.edu/~jeffe/open/klee.html, 1998.
 
17
18
19
 
20
 
21
V. Klee. Can the measure of ∪n/1{ai, bi} be computed in less than O(n log n) steps? Amer. Math. Monthly 84:284--285, 1977.
 
22
J. van Leeuwen and D. Wood. The measure problem for rectangular ranges in d-space. J. Algorithms
 
23
 
24
 
25
J. Matoušek. Range searching with efficient hierarchical cuttings. Discrete Comput. Geom. 10:157--182, 1993.
 
26
J. Nesetril and S. Poljak. On the complexity of the subgraph problem.Commen. Math. Univ. Carol. 26: 415--419, 1985.
 
27
 
28