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
maximum total score of 240 can be earned:
midterm exam: 72
final exam: 72
2 home exercises: 30 each
non-announced interim tests: 36 in total
retake exam (subtitutes either midterm of final): 72(participating in all three tests, the test with the lowest score is removed)