 Learning goals
After following the course the student is able to:
• Explain the way the Gauss algorithm provides a proof of main theorems in matrix theory.
• Describe how the FourierMotzkin algorithm leads to a constructive proof of the Farkas Lemma.
• Explain the basis ideas behind strong duality of linear programs and the minmax theorem for matrix games.
• Describe and sketch the special properties of convex sets and convex functions.
• Describe the theoretical basis for the different methods for solving smooth (unconstrained) minimization problems and explain the working of these algorithms.
• To apply the theoretical results to solve concrete (exercise) problems connected with the learned topics as well as to apply the proofs in a modified context.
 Content
The course aims to provide an introduction into algorithmic principles
and fundamental ideas of linear and nonlinear optimization.
The course enables the student to follow more advanced courses in continuous and discrete optimization.
Optimization problems appear in many real life situations. Examples are: How can we assign the frequencies in a wireless network such that the
network is used in an optimal way. How to choose the geometry of a design such that the device is both reliable and cheap. What is the
best location for a new power plant.
The course starts with the topic of algorithmic methods for solving linear
 Assumed previous knowledge   Required materialsCourse materialScript: Mathematical Optimization 
 Recommended materialsInstructional modesTestsTest


 