The students get familiar with recent developments in the area of mathematics of operations research. At the end of the course, the student
The setup of the course targets students who would like to get acquainted with some of the most recent highlights in the field, and is therefore particularly suited for students who are potentially interested in becoming a PhD student in this area.
- knows about recent developments in this area and where and how they are used,
- is able to understand and to apply recent research results from the field,
- is able to prepare and give a lecture including creating examples based on recent research results and defining and grading assignments.
The aim of this course is to get advanced knowledge of contemporary research topics in the area of mathematics of operations research. We choose some of the most recent and relevant highlights of the field, for instance, recent breakthroughs in complex networks, random graphs, algorithms, or game theory. To this end, every year a choice of topics is made from very recent textbooks or research articles. Essential for this course is the active participation of the students. In particular, the students give part of the lectures.|
After a few introductory lectures on recent highlights of operations research, available ideas for lectures are presented and distributed. Student lectures are either an individual assignment or done in pairs, depending on the number of students. Each student lecture comprises of the lecture itself plus an assignment that has to be solved by fellow students. The lecture typically contains example(s) of applications of the recent result. The assignments must be handed in, and they are graded by the student who gave the lecture. The grade is composed of the quality of the student lecture(s), the overall participation during the course, and the performance in terms of the exercises.
Desired (not obligatory):
Mathematical Optimization (201500372),
Discrete Optimization (191581100),
Continuous Optimization (191581200)
Measure and Probability (201800321)
Stochastic Processes (201800339)