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Persistent triangulations

Published online by Cambridge University Press:  29 August 2001

GUY BLELLOCH
Affiliation:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
HAL BURCH
Affiliation:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
KARL CRARY
Affiliation:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
ROBERT HARPER
Affiliation:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
GARY MILLER
Affiliation:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
NOEL WALKINGTON
Affiliation:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Abstract

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Triangulations of a surface are of fundamental importance in computational geometry, computer graphics, and engineering and scientific simulations. Triangulations are ordinarily represented as mutable graph structures for which both adding and traversing edges take constant time per operation. These representations of triangulations make it difficult to support persistence, including ‘multiple futures’, the ability to use a data structure in several unrelated ways in a given computation; ‘time travel’, the ability to move freely among versions of a data structure; or parallel computation, the ability to operate concurrently on a data structure without interference. We present a purely functional interface and representation of triangulated surfaces, and more generally of simplicial complexes in higher dimensions. In addition to being persistent in the strongest sense, the interface more closely matches the mathematical definition of triangulations (simplicial complexes) than do interfaces based on mutable representations. The representation, however, comes at the cost of requiring O(lg n) time for traversing or adding triangles (simplices), where n is the number of triangles in the surface. We show both analytically and experimentally that for certain important cases, this extra cost does not seriously affect end-to-end running time. Analytically, we present a new randomized algorithm for 3-dimensional Convex Hull based on our representations for which the running time matches the Ω(n lg n) lower-bound for the problem. This is achieved by using only O(n) traversals of the surface. Experimentally, we present results for both an implementation of the 3-dimensional Convex Hull and for a terrain modeling algorithm, which demonstrate that, although there is some cost to persistence, it seems to be a small constant factor.

Type
Research Article
Copyright
© 2001 Cambridge University Press

Footnotes

This research was supported in part by NSF Grant CCR-9706572.
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