Discrete Mathematics and Optimization provides the mathematical tools required for the analysis and solution of problems that are of a combinatorial nature. Such problems often have origins in (pure) mathematics, adjacent areas like computer science and quantum physics, or practical applications such as logistics.
The specialisation has a strong theoretical component, but also offers the opportunity to look into real-life applications in the field of (healthcare) logistics, machine learning, energy etc.
We work on:
- mixed integer linear programming
- convex geometry and optimization
- combinatorial optimization
- semidefinite programming
- discrete and computational geometry
- geometry of lattices
- algorithms and complexity
- robust optimization / optimization under uncertainty
- extremal combinatorics
- finite geometry
- quantum optimization
- quantum error correction
