Randomized Geometric Algorithms
Randomized Geometric Algorithms is a class of algorithms in computational geometry that solve problems on geometric objects by incorporating random choices into their design or execution to obtain provable performance guarantees—typically on expected running time or simplicity of analysis—that are superior or more robust than those of comparable deterministic geometric algorithms.
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Geometric Range SearchingRandomized Closest Pair AlgorithmsRandomized Computational TopologyRandomized Convex Hull AlgorithmsRandomized Coreset ConstructionRandomized Covering AlgorithmsRandomized Delaunay TriangulationRandomized Geometric Data StructuresRandomized Geometric OptimizationRandomized Half-space Range Searching
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Optimal Search in Planar Subdivisions
David Kirkpatrick · SIAM Journal on Computing · 1983 · 751 citations
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Tel Aviv University
Tel Aviv, Israel
28 h-index
88 publications
3.4K citations
Carnegie Mellon University
Pittsburgh, United States
22 h-index
38 publications
3.7K citations
Princeton University
Princeton, United States
21 h-index
40 publications
8.8K citations
Duke University
Durham, United States
20 h-index
36 publications
1.8K citations

University of Waterloo
Waterloo, Canada
20 h-index
33 publications
1.4K citations
Max Planck Institute for Informatics
Saarbrücken, Germany
19 h-index
39 publications
1.9K citations
New York University
New York, United States
19 h-index
41 publications
1.9K citations
Courant Institute of Mathematical Sciences
New York, United States
19 h-index
34 publications
1.5K citations
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