After successful completion of the course, students are able to analyze optimization problems which occur inside the firm. Students know how to structure optimization programs with / without constraints, how to solve them analytically or how to implement them with R programming language.
Static Optimization:
- Lagrange Theorie
- Kuhn Tucker Theorie
- Spezial cases: Lineare Optimization and the portfolio problem
Dynamiic Optimization:
- The Bellman-prinziple
- Dynamic programming, deterministic and und stochastic
Lectures, applied Problems, home exercises, interactive online material
midterm exam: 30%
final exam: 30%
unannounced short tests: 20%
2 home exercises: 10% each
retake exam (subtitutes either midterm of final): 30%