From Wikipedia, the free encyclopedia
In
computational geometry, Klee's measure problem is the problem of determining how efficiently the
measure of a
union of (
multidimensional) rectangular ranges can be computed. Here, a d-dimensional rectangular range is defined to be a
cartesian product of d
intervals of
real numbers, which is a
subset of Rd.
The problem is named after
Victor Klee, who gave an algorithm for computing the length of a union of
intervals (the case d = 1) which was later shown to be optimally efficient in the sense of
computational complexity theory. The computational complexity of computing the area of a union of 2-dimensional rectangular ranges is now also known, but the case d ≥ 3 remains an
open problem.
Contents
[
hide]
1 History and algorithms2 Current status3 References and further reading3.1 Important papers3.2 Secondary literature[
edit]History and algorithms
In 1977,
Victor Klee considered the following problem: given a collection of n
intervals in the
real line, compute the length of their union. He then presented an
algorithm to solve this problem with
computational complexity (or "running time") O(nlogn) — see
Big O notation for the meaning of this statement. This algorithm, based on
sorting the intervals, was later shown by
Michael Fredman and Bruce Weide (1978) to be optimal.
Later in 1977,
Jon Bentley considered a 2-dimensional analogue of this problem: given a collection of n
rectangles, find the
area of their union. He also obtained a complexity O(nlogn)algorithm, now known as Bentley's algorithm, based on reducing the problem to n 1-dimensional problems: this is done by sweeping a vertical line across the area. Using this method, the area of the union can be computed without explicitly constructing the union itself. Bentley's algorithm is now also known to be optimal (in the 2-dimensional case), and is used in
computer graphics, among other areas.
These two problems are the 1- and 2-dimensional cases of a more general question: given a collection of n d-dimensional rectangular ranges, compute the measure of their union. This general problem is Klee's measure problem.
When generalized to the d-dimensional case, Bentley's algorithm has a running time of O(nd − 1logn). This turns out not to be optimal, because it only decomposes the d-dimensional problem into n (d-1)-dimensional problems, and does not further decompose those subproblems. In 1981, Jan van Leeuwen and Derek Wood improved the running time of this algorithm toO(nd − 1) for d ≥ 3 by using dynamic
quadtrees.
In 1988,
Mark Overmars and Chee Yap proposed an O(nd / 2logn) algorithm for d ≥ 3 which is the fastest known algorithm to date. Their algorithm uses a particular data structure similar to a
kd-tree to decompose the problem into 2-dimensional components and aggregate those components efficiently; the 2-dimensional problems themselves are solved efficiently using a
trellis structure. Although asymptotically faster than Bentley's algorithm, its data structures use significantly more space, so it is only used in problems where either n or d is large. In 1998, Bogdan Chlebus proposed a simpler algorithm with the same asymptotic running time for the common special cases where d is 3 or 4.
[
edit]Current status
The only known
lower bound for any d is Ω(nlogn). The Overmars–Yap algorithm provides an upper bound of O(nd / 2logn), so for d ≥ 3, it remains an open question whether faster algorithms are possible, or alternatively whether tighter lower bounds can be proven. In particular, it remains open whether the algorithm's running time must depend on d. In addition, the question of whether there are faster algorithms that can deal with special cases (for example, when the input coordinates are integers within a bounded range) remains open.
[
edit]References and further reading
[
edit]Important papers
Victor Klee (1977). Can the measure of
be computed in less than O(nlogn) steps?
American Mathematical Monthly 84: 284-285.
Jon L. Bentley (1977). Algorithms for Klee's rectangle problems. Unpublished notes, Computer Science Department, Carnegie Mellon University.
Michael L. Fredman and Bruce Weide (1978). The complexity of computing the measure of
.
Communications of the ACM 21: 540-544.
Jan van Leeuwen and Derick Wood (1981). The measure problem for rectangular ranges in d-space.
Journal of Algorithms 2: 282-300.
Mark H. Overmars and Chee-Keng Yap (1988). New upper bounds in Klee's measure problem. Extended abstract. Rijksuniversiteit Utrecht Technical Report RUU-CS-88-22. Full version published in
SIAM Journal on Computing 20(6): 1034-1045 (1991). (
PDF of the tech report version.)
Bogdan S. Chlebus (1998). On the Klee's measure problem in small dimensions. In Proceedings of the 25th Conference on Current Trends in Theory and Practice of Informatics (SOFSEM-98) (Jasná, Slovakia, November 21-27, 1998),
Lecture Notes in Computer Science 1521 (Springer-Verlag, Berlin, 1998).
[
edit]Secondary literature
Franco P. Preparata and
Michael I. Shamos (1985). Computational Geometry (Springer-Verlag, Berlin).
Klee's Measure Problem, from Professor Jeff Erickson's
list of open problems in computational geometry. (Accessed November 8, 2005, when the last update was July 31, 1998.)
Categories:
Geometric algorithms |
Measure theory |
Mathematical problems