The power of geometric duality
BIT - Ellis Horwood series in artificial intelligence
Computational geometry: an introduction
Computational geometry: an introduction
Applications of random sampling in computational geometry, II
Discrete & Computational Geometry - Selected papers from the fourth ACM symposium on computational geometry, Univ. of Illinois, Urbana-Champaign, June 6 8, 1988
Reporting points in halfspaces
Computational Geometry: Theory and Applications
Fixed-dimensional linear programming queries made easy
Proceedings of the twelfth annual symposium on Computational geometry
Size-estimation framework with applications to transitive closure and reachability
Journal of Computer and System Sciences
On range reporting, ray shooting and k-level construction
SCG '99 Proceedings of the fifteenth annual symposium on Computational geometry
Proceedings of the sixteenth annual symposium on Computational geometry
Computational Geometry: Theory and Applications
An optimal randomized algorithm for maximum Tukey depth
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Low-Dimensional Linear Programming with Violations
SIAM Journal on Computing
On approximating the depth and related problems
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
A general approach for cache-oblivious range reporting and approximate range counting
Proceedings of the twenty-fifth annual symposium on Computational geometry
Data Structures for Approximate Orthogonal Range Counting
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
Absolute approximation of Tukey depth: Theory and experiments
Computational Geometry: Theory and Applications
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We improve the previous results by Aronov and Har-Peled (SODA'05) and Kaplan and Sharir (SODA'06) and present a randomized data structure of O(n) expected sizewhich can answer 3D approximate halfspace range counting queries in O(log n/k) expected time, where k is the actual value of the count. This is the first optimal method for the problem in the standard decision tree model; moreover, unlike previous methods, the new method is Las Vegas instead of Monte Carlo.In addition, we describe new results for several related problems, includingapproximate Tukey depth queries in 3D, approximate regression depthqueries in 2D, and approximate linear programming with violations inlow dimensions.