2135 Advanced Mathematical Methods
Assist.Prof. Priv.Doz.Dr. Paul Eisenberg
Contact details
Weekly hours
Language of instruction
09/01/23 to 09/22/23
Registration via LPIS
Notes to the course
Day Date Time Room
Monday 10/02/23 02:00 PM - 05:00 PM D2.0.030
Monday 10/09/23 02:00 PM - 05:00 PM D2.0.030
Monday 10/16/23 02:00 PM - 05:00 PM D2.0.030
Monday 10/23/23 02:00 PM - 05:00 PM D2.0.030
Monday 10/30/23 02:00 PM - 05:00 PM D2.0.030
Monday 11/06/23 02:00 PM - 05:00 PM D5.1.002
Monday 11/13/23 02:00 PM - 05:00 PM D2.0.030
Monday 11/27/23 02:00 PM - 04:00 PM D3.0.225

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.


Learning outcomes

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.


Attendance requirements

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

Teaching/learning method(s)

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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Last edited: 2023-10-02