The students get familiar with recent developments in the area of mathematics of operations research. At the end of the course, the student
- knows about recent developments in this area and where and how they are used,
- is able to read recent research papers,
- is able to prepare and give a lecture including setting up assignments and grading them.
The aim of this course is to get advanced knowledge of some of the contemporary research topics in the area of mathematics of operations research. The course can cover, for instance, recent research results on complex networks, random graphs, analysis of algorithms, approximation algorithms, or game theory. However, depending on the participants and precise composition of the teaching staff, every year a choice of topics is made from recent textbooks and/or recent journal or conference publications. Essential for this course is an active participation of the students. In particular, it is the students that give part of the lectures. This year the topics of the course are (i) algorithmic techniques for big data analysis and (ii) algorithmic game theory.|
After a first introduction, the available topics 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 the fellow students. 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 Programming (191580251),
Discrete Optimization (191581100),
Continuous Optimization (191581200)
Measure and Probability (191570401)
Stochastic Processes (191531750)