Introduction to algorithms and data structures for multi-dimensional data processing. The topics include Convex Hulls, Intersection Detection, Polygon Triangulation, Linear Programming, Range Searching, Proximity, Voronoi Diagram and Delaunay Triangulation, Arrangements and Geometric Duality, VC Dimension and Sampling, Locality Sensitive Hashing, High Dimensional Nearest Neighbor Search, Lower Bounds, Combinatorial Geometry.
The tentative lecture schedule .
This is a graduate level course, and students are expected to be proficient in algorithm analysis, proofs of correctness, and basic data structures.
The textbook for the course is Computational Geometry, by de Berg, van Kreveld, Overmars, and Schwarzkopf.
The course pizza page .
Each student will be assigned to scribe one of the lectures, and submit a detailed,
well-researched and polished report for that lecture, for 20% of the grade.
The remaining 80% of the grade will be split between homework assignments and a final exam.
Scribe Schdule (A Sample Scribe File and source )