Chan's algorithm computes the convex hull of a set of points in O(n log h) time, where h is the number of points on the hull, combining the divide and conquer efficiency of algorithms like Quickhull with the output sensitivity of Jarvis march. It works by partitioning the points into small groups, computing each group's hull with a simple O(n log n) method, and merging the results using a gift wrapping style step whose group size is doubled across repeated guesses until the true hull size is found. Timothy Chan published the algorithm in 1996, and it is regarded as asymptotically optimal for the convex hull problem.
Facts
Time Complexity Classification
Design Technique Time Complexity
Time Complexity (category)Linearithmic Time -- O(n log n) 2 Connections
Credited To
Source Wikipedia: Chan's algorithm
In Field
Source Wikipedia: Chan's algorithm
Invented By
Timothy M. Chan published his optimal output-sensitive convex hull algorithm in 1996.
Uses Design Technique
Sources
1. Wikipedia: Chan's algorithm
Lead section, naming sentence
named after Timothy M. Chan
Algorithm section, complexity sentence
The algorithm takes O(n log h) time, where h is the number of vertices of the output (the convex hull).
entity record, description (design-technique)
Chan's algorithm computes the convex hull of a set of points in O(n log h) time, where h is the number of points on the hull, combining the divide and conquer efficiency of algorithms like Quickhull with the output sensitivity of Jarvis march.
Credited To: Timothy M. Chan, Lead section
In computational geometry, Chan's algorithm, named after Timothy M.
- In Field: Algorithms and Complexity Theory, Lead paragraph
In Group: Convex Hull Algorithms, Lead section
to compute the convex hull of a set
View the Source2. Chan's Algorithm (Wikipedia)
Wikipedia infobox: time complexity linearithmicQuote, Wikipedia infobox: time complexity linearithmic
linearithmic
View the Source Wikidata: Chan's Algorithm
Wikidata Q2025538, class allow-list match (w-wdresolver-0926)View the Source Reader Challenges (0)
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