This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2021 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|SS 2021||WS 2020/1||SS 2020||WS 2019/20||SS 2012||WS 2011/2|
MA3502 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|150 h||45 h||5 CP|
Content, Learning Outcome and Preconditions
Für Studierende für Lehramt an Gymnasien: MA9935 Einführung in die Mathematik 1 LG, MA9936 Einführung in die Mathematik 2 LG, MA9937 Analysis 1 LG, MA9938 Analysis 2 LG, MA9939 Lineare Algebra 1 LG, MA9940 Lineare Algebra 2 LG, MA2501 Algorithmic Discrete Mathematics, MA3501 Linear Optimization (or MA2504 Fundamentals of Convex Optimization)
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
Please keep in mind that course announcements are regularly only completed in the semester before.
|VO||2||Discrete Optimization (Combinatorial Optimization: Advanced)||Friedrich, U.||
Thu, 12:00–14:00, virtuell
|UE||1||Exercises to Discrete Optimization (Combinatorial Optimization: Advanced)||Friedrich, U.||dates in groups||
Learning and Teaching Methods
The module is offered as lectures with accompanying practice sessions. In the lectures, the contents will be presented in a talk with demonstrative examples, as well as through discussion with the students. The lectures should motivate the students to carry out their own analysis of the themes presented and to independently study the relevant literature. Corresponding to each lecture, practice sessions will be offered, in which exercise sheets and solutions will be available. In this way, students can deepen their understanding of the methods and concepts taught in the lectures and independently check their progress.
Papadimitriou, Steiglitz: Combinatorial Optimization, Dover 2001.
Description of exams and course work
Students will have to analyze different discrete optimization problems. They should be able to use techniques and concepts presented in the lecture and trained in the exercises and should show an understanding of standard algorithms to solve integer linear optimization problems.
The exam may be repeated at the end of the semester.