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.
EI74351 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)|
|180 h||60 h||6 CP|
Content, Learning Outcome and Preconditions
Convex analysis: convex sets, convex functions.
Linear programming: extremal points and directions, simplex algorithm.
Optimality conditions: Fritz John conditions, Karush-Kuhn-Tucker conditions, constraint qualifications.
Lagrangian duality: duality theorems.
Algorithms: general concept, unconstrained optimization, constrained optimization.
Solutions for the dual problem: sub gradient method, cutting plane algorithm.
Interior-point method: barrier functions, IP algorithm.
Applications: problems form multi-user information theory, resource allocation, parameter optimization in layered and distributed communication systems.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
Wed, 13:15–14:45, N1070
Fri, 11:30–13:00, N1095
Learning and Teaching Methods
In addition to the individual methods of the students, consolidated knowledge is aspired by repeated lessons in exercises and tutorials.
During the lectures, students are instructed in a teacher-centric style. The exercises are held in a student-centenered way.
- Lecture notes
- Exercises with solutions as download.
- M. S. Bazare, H. D. Serail, and C. M. Shetty. Nonlinear Programming: Theory and Algorithms. Wiley, 2006.
- D. Bertsekas and A. Nedic. Convex Analysis and Optimization. Athena Scientific, 2003.
- S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge, 2004.
Description of exams and course work
There is a possibility to take the exam in the following semester.