A linear-time algorithm for computing the Voronoi diagram of a convex polygon
Discrete & Computational Geometry
How to net a lot with little: small &egr;-nets for disks and halfspaces
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
An optimal algorithm for intersecting three-dimensional convex polyhedra
SIAM Journal on Computing
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Improved Approximation Algorithms for Geometric Set Cover
Discrete & Computational Geometry
Optimal Expected-Case Planar Point Location
SIAM Journal on Computing
Delaunay Triangulation of Imprecise Points Simplified and Extended
WADS '09 Proceedings of the 11th International Symposium on Algorithms and Data Structures
Self-improving algorithms for convex hulls
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Self-improving algorithms for coordinate-wise maxima
Proceedings of the twenty-eighth annual symposium on Computational geometry
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We study the problem of two-dimensional Delaunay triangulation in the self-improving algorithms model [1]. We assume that the n points of the input each come from an independent, unknown, and arbitrary distribution. The first phase of our algorithm builds data structures that store relevant information about the input distribution. The second phase uses these data structures to efficiently compute the Delaunay triangulation of the input. The running time of our algorithm matches the information-theoretic lower bound for the given input distribution, implying that if the input distribution has low entropy, then our algorithm beats the standard Ω(n log n) bound for computing Delaunay triangulations. Our algorithm and analysis use a variety of techniques: ε-nets for disks, entropy-optimal point-location data structures, linear-time splitting of Delaunay triangulations, and information-theoretic arguments.