|Website:||website containing additional information|
|Period:||period 2 (week 46 through 5, i.e., 11-11-2019 through 31-1-2020; retake week 16)
|Participants:||see Osiris Docent|
|Schedule:||Official schedule representation can be found in Osiris|
In many areas of computer science -- robotics, computer graphics and
virtual reality, and geographic information systems are some examples --
it is necessary to store, analyze, and create or manipulate spatial data.
This course deals with the algorithmic aspects of these tasks: we study
the design and analysis of geometric algorithms and data structures.
|Literature:||M. de Berg, O. Cheong, M. van Kreveld, and M. Overmars.
Computational Geometry: Algorithms and Applications (3rd edition).
Springer-Verlag, Heidelberg, 2008.
|Course form:||Two lectures of in total 4 hours per week. |
|Exam form:||Homework exams that will be distributed thrice (5%, 25% and 25%), and in the exam week there will be a final exam (45%). Each item has to be scored with at least a 5 to pass the course, and the weighted average, rounded, must be at least a 6. In addition, there will be a bonus assignment worth 0.5pts. The maximum achievable grade is a 10.
The final exam is "closed book". If you do not make or fail (<=4) two or more of the homework exams, you fail the course. If you fail one homework exam, you get a new, third homework exam based on later chapters of the book to replace the failed homework. |
|Minimum effort to qualify for 2nd chance exam:||To qualify for the second opportunity of the final exam, you must have a grade of 5 or higher for three homework exams.
|Description:||Computer graphics, robot motion planning, computer games, simulations, geographic information systems, and CAD/CAM systems all make use of geometric algorithms to perform various tasks. The course on geometric algorithms takes a fundamental viewpoint and discusses the design and analysis of geometric algorithms. We will study various algorithmic techniques and geometric concepts that are useful to solve geometric problems efficiently. These include plane sweep, randomized incremental construction, and multi-level data structures; geometric concepts include Voronoi diagrams and Delaunay triangulations, arrangements, and duality. We will apply these techniques to solve a variety of problems: convex-hull computation, line-segment intersection, polygon triangulation, low-dimensional linear programming, range searching, and point location are some examples. Each problem we study is motivated by a practical problem from one of the application areas.