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Kansas State University Department of Computing and Information Sciences Friday, July 13, 2001 Mantena V. Raju Department of Computing and Information Sciences, KSU [email protected] Parallel algorithm for Delaunay Triangulati Computational Geometry Computational Geometry

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Page 1: Kansas State University Department of Computing and Information Sciences Friday, July 13, 2001 Mantena V. Raju Department of Computing and Information

Kansas State University

Department of Computing and Information Sciences

Friday, July 13, 2001

Mantena V. Raju

Department of Computing and Information Sciences, KSU

[email protected]

A Parallel algorithm for Delaunay Triangulation

Computational GeometryComputational Geometry

Page 2: Kansas State University Department of Computing and Information Sciences Friday, July 13, 2001 Mantena V. Raju Department of Computing and Information

Kansas State University

Department of Computing and Information Sciences

Basic DefinitionsBasic Definitions

Convex Hull

Given k distinct points P1,P2,…….. Pk in Ed the set of points

P = a1P1 + a2P2 + ………+ akPk where a1,a2,…….. ak are all 0 and

a1 + a2+ ……..+ ak = 1 is the convex set generated by P1,P2,… Pk

and P is a convex combination of P1,P2,…….. Pk.

Example

If P1, P2 are two points in Ed, the convex combination of P1, P2

is the line segment joining P1, P2

Given an arbitrary subset L of points in Ed the convex hull

conv(L) of L is the smallest convex set containing L.

Page 3: Kansas State University Department of Computing and Information Sciences Friday, July 13, 2001 Mantena V. Raju Department of Computing and Information

Kansas State University

Department of Computing and Information Sciences

Convex HullConvex Hull

Convex Hull of the points shown in blue dots

Some algorithms for calculating Convex Hull in 2 dimensions

1. Graham’s Scan O(n log n) worst case running time.

2. Jarvis March O(nh) where h is the number of vertices of the convex hull. The worst case running time is O(n2)

Page 4: Kansas State University Department of Computing and Information Sciences Friday, July 13, 2001 Mantena V. Raju Department of Computing and Information

Kansas State University

Department of Computing and Information Sciences

Voronoi DiagramVoronoi Diagram

Distance between 2 points p, q denoted by dist(p,q) = sqrt((px- qx)2 + (py- qy)2

Voronoi Diagram

Let P = {P1,P2,…….. Pn} be a set of n distinct points in the plane. Voronoi diagram of P is defined as the subdivsion of the plane into n cells, one for

each site in P, with the property that a point q lies in the cell corresponding to a site Pi if and only if dist(Pi ,q) < dist(Pj ,q) for each Pj P

Page 5: Kansas State University Department of Computing and Information Sciences Friday, July 13, 2001 Mantena V. Raju Department of Computing and Information

Kansas State University

Department of Computing and Information Sciences

Voronoi DiagramVoronoi Diagram

Some algorithms for calculating Voronoi in 2 dimensions

1. Fortunes’s Sweep line algorithm O(n log n) worst case running time using O(n) storage.

2. Guiba’s and Stolfi’s divide and conquer algorithm O(n log n) worst case running time.

Animation of Fortune’s Sweep line Algorithm

http://www.diku.dk/students/duff/Fortune/

Page 6: Kansas State University Department of Computing and Information Sciences Friday, July 13, 2001 Mantena V. Raju Department of Computing and Information

Kansas State University

Department of Computing and Information Sciences

Delaunay TriangulationDelaunay Triangulation

Delaunay Triangulation is the straight line dual of the Voronoi Diagram.

Algorithm for calculating Delaunay in 2 dimensionsR. A. Dwyer’s Divide and Conquer O(n log log n) average case running time

O(n log n) worst case running time.

Page 7: Kansas State University Department of Computing and Information Sciences Friday, July 13, 2001 Mantena V. Raju Department of Computing and Information

Kansas State University

Department of Computing and Information Sciences

Parallel Delaunay TriangulationParallel Delaunay Triangulation