Unformatted text preview: UMass Lowell Computer Science 91.504 Advanced Algorithms Computational Geometry
Prof. Karen Daniels
Spring, 2010 Spring Lecture 3 O'Rourke Chapter 3: p 2D Convex Hulls
Thursday, 2/18/10 Chapter 3 Ch t
2D C Convex H ll Hulls Definitions Gift Wrapping Graham Scan QuickHull Incremental Divide d Divide andConquer Di id andC Lower Bound in (nlgn) Convexity & Convex Hulls
source: O'Rourke, Computational Geometry in C A convex combination of points x1, ..., xk is a sum of the form 1x1+...+ kxk where i 0 i and 1 + L + k = 1
Convex hull of a set of points is the set of all convex combinations of points in the set. i t i th t
We will construct boundary of convex hull. nonconvex polygon source: 91.503 textbook Cormen et al. convex hull of a point set Naive Algorithms for Extreme Points
Algorithm: Algorithm: INTERIOR POINTS for each i do for each j = i do for each k = j = i do for f each L = k = j = i d h do if pL in triangle(pi, pj, pk) then pL is nonextreme O(n4) This is essentially de Berg et al.'s SLOWCONVEXHULL in their Chapter 1. al.'s Algorithm: EXTREME EDGES Algorithm: g for each i do for each j = i do for each k = j = i do if pk is not (left or on) (pi, pj) then (pi , pj) is not extreme O(n3) source: O'Rourke, Computational Geometry in C Algorithms: 2D Gift Wrapping
Use one extreme edge as an anchor for finding the next g Algorithm: Algorithm: GIFT WRAPPING index of the lowest point i0 i i0 repeat for each j = i Compute counterclockwise angle from previous hull edge k index of point with smallest Output (pi , pk) as a hull edge i k until i = i0 O(n2) source: O'Rourke, Computational Geometry in C Gift Wrapping source: 91.503 textbook Cormen et al. 33.9 Output Sensitivity: O(n2) runtime is actually O(nh) Sensitivity: runwhere h is the number of vertices of the convex hull. Algorithms: 2D QuickHull
Concentrate on points close to hull boundary Named f similarity to N d for i il it t Quicksort
a b A c
finds one of upper or lower hull Algorithm: Algorithm: QUICK HULL function QuickHull(a,b,S) if S = 0 return() else c index of point with max distance from ab A points strictly right of (a c) (a,c) B points strictly right of (c,b) return QuickHull(a,c,A) + (c) + QuickHull(c,b,B) O(n2) source: O'Rourke, Computational Geometry in C Graham's Algorithm
source: O'Rourke, Computational Geometry in C Points sorted angularly provide "star"starshaped" starting point Prevent "dents" as you go via dents convexity testing
p0 Algorithm: Algorithm: GRAHAM SCAN, Version B Find rightmost lowest point; label it p0. Sort all other points angularly about p0. In case of tie, delete point(s) closer to p0. Stack S (p1, p0) = (pt, pt1); t indexes top i 2 while i < n do if pi is strictly left of pt1pt then Push(pi, S) and set i i +1 else Pop(S) "multipop" O(nlgn) Graham Scan source: 91.503 textbook Cormen et al. Graham Scan 33.7 source: 91.503 textbook Cormen et al. Graham Scan 33.7 source: 91.503 textbook Cormen et al. Graham Scan source: 91.503 textbook Cormen et al. Graham Scan source: 91.503 textbook Cormen et al. Algorithms: 2D Incremental
source: O'Rourke, Computational Geometry in C Add points, one at a time i i
update hull for each new point Key step becomes adding a single point to an existing hull.
Find 2 tangents g
Results of 2 consecutive LEFT tests differ Idea can be extended to 3D.
This is essentially de Berg et al.'s CONVEXHULL in their Chapter 1. al.'s Algorithm: Algorithm: INCREMENTAL ALGORITHM ConvexHull{p0 , p1 , p2 } {p Let H2 for k 3 to n  1 do ConvexHull{ Hk1 U pk } Hk O(n2)
can be improved to O(nlgn) source: O'Rourke, Computational Geometry in C Algorithms: 2D Divide and Conquer DivideandConquer
B DivideandDivideandConquer in a geometric q g setting O(n) merge step is the challenge
Find Fi d upper and l d lower tangents t t Lower tangent: find rightmost pt of A & leftmost pt of B; then "walk it downwards" A Idea Id can be extended to 3D. b t d d t 3D
Algorithm: DIVIDEandAlgorithm: DIVIDEandCONQUER Sort points by x coordinate Divide points into 2 sets A and B: A contains left n/2 points B contains right n/2 points t i i ht /2 i t Compute ConvexHull(A) and ConvexHull(B) recursively Merge ConvexHull(A) and ConvexHull(B) O(nlgn) Lower Bound of (nlgn)
source: O'Rourke, Computational Geometry in C WorstWorstcase time to find convex hull of n points in algebraic decision tree model is in (nlgn) Proof uses sorting reduction:
Given unsorted list of n numbers: (x1,x2 ,..., xn) Form unsorted set of points: (xi, xi2) f each xi F t d t f i t ( for h Convex hull of points produces sorted list! list!
Parabola: every p y point is on convex hull Reduction is O(n) (which is in o(nlgn)) Finding convex hull of n points is therefore at least as hard as sorting n points, so worstcase points worsttime is in (nlgn) Parabola for sorting 2,1,3
How does this relate to outputsensitive results? output ...
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 Spring '10
 DANIELS
 Algorithms, Convex hull, textbook Cormen, Algorithms Computational Geometry

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