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Convex Hull Algorithms for Dynamic Data

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Graham Scan, Gift-Wrapping, Incremental Hull, Overmars, Quick-Hull, Ultimate Hull. ... Idea: Certificates. Each comparison is associated with a certificate, where ... – PowerPoint PPT presentation

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Title: Convex Hull Algorithms for Dynamic Data


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

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

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
Idea Record who reads what and when
main
main
g
f
g
f
h
h
k
m
k
m
Memory
Some parts of the input change
7
SAC vs. Dynamic Convex Hull
  • Automatic dynamization
  • Allow composition of programs
  • Down-side not always efficient!
  • Stability (input-sensitivity) Acar et al.
    SODA04
  • 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

j
Face
Give orientation to edges
i
Store (i,j) as key and k as data
k
9
Self-Adjusting Binary Tree
Initial Run
Time
Re-execution
10
Kinetic Convex Hull
  • 1-D Case
  • S(t) x1(t), x2(t), , xn(t), where xi(t)
    xi(0) vit
  • Maintain min and max
  • Observation

x1
Value
x2
x3
Time
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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