This is an introductory course on computational geometry and its applications. We will discuss techniques for reasoning about geometric data, and for designing computationally efficient algorithms to process these data. The main topics covered in the course include the following: Convex Hulls, Object Intersection, Polygon triangulation, Range Searching, Planar Point Location, Proximity and Voronoi Diagram, Delaunay Triangulation, Arrangements, Sampling and Epsilon Nets, Paradoxical behavior of Higher Dimensions, Metric Embeddings.
This is a graduate level course, and students are expected to know the basic concepts of algorithm analysis (asymptotic notation, worst-case analysis) and data structures (linked lists, trees, priority queues).
Students can expect 3-4 written homework assignments, a midterm and a final exam. They will also be expected to read additional advanced material, not covered in lectures, from research papers.
Click here for the tentative schedule of lectures and exams.
The textbook for the course is Computational Geometry, by de Berg, van Kreveld, Overmars, and Schwarzkopf. This is a fairly well-written introductory textbook. In addition, my own lecture slides will be available below. However, keep in mind that these slides are not an excuse to skip lectures. The lectures typically involve worked examples and interaction in the form of questions and answers, which can't be captured in the slides.