This lecture introduce selected topics from convex analysis and convergence of distributions. The course will treat separation results, subgradients and convex conjugates as well as applications of those. In addition, weak convergence, a.s. convergence, convergence in probability and possibly Wasserstein convergence.

 

After completing this class the student will have the ability to:
 - Perform some convex optimisation tasks in finite dimensions.
 - Have a deepened understanding of separation arguments and some of their applications.
 - Additional understanding of various types of convergences used for statistical and financial applications.

 

In line with WU regulations for lectures in PI format full attendance is required (at most one lecture can be missed)

The course consists of several parts blended together: On-site lecturing and discussions. Self-study of the course material (Lecture notes/slides). Solving exercises in groups. Exercise solutions are discussed in class.

 

In class participation (20%) (on site)

Exercise Series (30%) (remote take home)

Final exam (50%)  (written, on site)

An an overall score of at least 50% is necessary for passing.


 

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