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Convex Hull Algorithms for Dynamic Data Kanat Tangwongsan Joint work with Guy Blelloch and Umut Acar (TTI-C)

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Presentation on theme: "Convex Hull Algorithms for Dynamic Data Kanat Tangwongsan Joint work with Guy Blelloch and Umut Acar (TTI-C)"— Presentation transcript:

1 Convex Hull Algorithms for Dynamic Data Kanat Tangwongsan Joint work with Guy Blelloch and Umut Acar (TTI-C)

2 The Convex Hull Problem Extensively studied: Graham Scan, Gift-Wrapping, Incremental Hull, Overmars, Quick-Hull, Ultimate Hull. Matched Lower-bound already! Input: S Output: CH(S)

3 Convex Hull ++ : Dynamic Case Maintain the Hull under insertion and deletion. insert delete

4 Convex Hull # : Kinetic Case each point has velocity ( v ) maintain the hull (efficiently)

5 Known Solutions and Issues Dynamic Convex Hull Overmars, Brodal, Mulmuley, Schwarzkopf, Preparata, Hershberger and Suri, Chan complicated!! hard to compose algorithms together Kinetic Convex Hull Guibas, de Berg, … None existed for 3 or higher dimensions

6 Self-Adjusting Computation main f g h k m f g h k m Memory Some parts of the input change Idea: Record who reads what and when

7 SAC vs. Dynamic Convex Hull Automatic “dynamization” Allow composition of programs Down-side: not always efficient! Stability (=input-sensitivity) [Acar et al. SODA’04] Our Job: Design a stable static convex hull algorithm Focus: 3-D case.

8 Our Approach Self-Adjusting Tree Suitable Convex Hull in 3D + Dynamic Convex Hull in 3D k i j Face Store (i,j) as key and k as data Give orientation to edges

9 Self-Adjusting Binary Tree Time Initial Run: Re-execution:

10 Kinetic Convex Hull 1-D Case S(t) = {x 1 (t), x 2 (t), …, x n (t)}, where x i (t) = x i (0) + v i t Maintain min and max Observation: Value Time x1x1 x2x2 x3x3

11 Idea: Certificates Each comparison is associated with a certificate, where Self-Adjusting Computation account for changes in order of expiration times certificate = comparsion result + expiration time

12 Summary Focus: 3D {dynamic, kinetic} convex hull Dynamic Convex Hull Amortized expected O(log n) in certain models. Worst-case bound: O(n log n) More performance analysis to come Real-time bound: can it be better than O(n) ? Practical performance: implemented, being evaluated. Kinetic Convex Hull in progress!

13 Questions?

14 Thank you!!


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