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)