This course provides a comprehensive overview of optimization techniques such as linear and integer programming, and non-linear programming, with applications in game theory and economics. Students learn optimization techniques from mathematics and operations research, and how to apply them in models from game theory and economic theory.

Topics in optimization include duality theorems in LP, branch and bound and cutting plane algorithms in IP, and Kuhn-Tucker conditions for NLP.

Topics in game theory and economics include computation of Nash equilibrium and refinements and mechanism design.

The course will be based on chapters from standard textbooks plus additional readers.

Recommended literature for background reading:
* Hans Peters : Game Theory : A Multi-Leveled Approach. Springer-Verlag.
* Stephen Boyd and Lieven Vandenberghe : Convex Optimization. Cambridge University Press.
* Roger Myerson : Game Theory : Analaysis of Conflict. Harvard University Press.
* L.J. Vanderbei : Linear Programming - Foundations and Extensions. 4th Edition, Springer.
* Jorge Nocedal and Stephen J. Wright : Numerical Optimization. 2nd Edition, Springer.

Only Master students can take this course. Exchange students need to have obtained a BSc degree in Economics, International Business, Econometrics, or a related topic. Familiarity with the basic concepts of optimization and linear programming will be helpful. A solid basis in mathematics and calculus is also recommendable.