Discrete Mathematics, Optimization, and Convexity
We are researchers at TUM interested in mathematical optimization problems that typically involve a large number of discrete decisions to be made. Such problems occur in a variety of real-world problems arising in logistics, scheduling, (social) networks, routing, chip design, data analysis, or tomography.
Besides practical applications, we are highly interested in theoretical foundations of discrete optimization. Keywords that describe our focus are:
- algorithmic game theory
- approximation algorithms
- complexity theory
- combinatorial optimization
- convex geometry
- extended formulations
- graph algorithms
- geometric representations
- linear and integer programming
- network design
- polyhedral combinatorics
We also offer various courses on such topics, particularly designed for students in Mathematics or Computer Science.