Module Number: EI7256
Duration: 1 Semester
Occurence: Summer semester
Number of ECTS: 6
Professor in charge: Wolfang Utschick
Contact hours: 150
Self-studying hours: 30
- final examination (evaluation of basic theoreticl concepts behind the individual projects and implementation-related issues)
- pass/fail credit requirement: 6 programming projects (in-depth understanding by programming related algorithms and evaluating practical scenarios)
Exam type: oral
Exam duration: 30 min.
Possibility of re-taking: in the next semester: Yes; at the end of the semester: No
Written paper: Yes
Working knowledge about convex optimization theory and algorithms as presented in the Optimization in Communications Engineering course. Working knowledge in fundamentals of communications engineering and signal processing. Working knowledge in the programming language MATLAB.
This laboratory provides insights and practical instructions for designing algorithms in the field of communications engineering and signal processing based on mathematical optimization theory by a series of successive teaching and hands-on units. Each unit includes the understanding and analysis of a typical problem of the addressed application scenarios, its mathematical modeling and the design and implementation of an adequate solution. Designed algorithms from a previous unit of the laboratory are supposed to be reused.
The addressed topics will cover
- disciplined convex problems rules
- gradient methods
- linear programming
- interior-point method
- Lagrangian dualty
- dual decpomostion method
- subgradient method
- cutting-plane methods
- conic optimization
- non-convex optimization
At the end of the module students are able to understand and analyse typical optimization problems in the field of communications engineering and signal processing by means of mathematical modeling from a mathematical optimization perspective and to apply, analyse, evaluate and create algorithms for the numerical solution of these optimization problems. The students are further able to apply state-of-the-art general purpose solvers for convex optimization problems.
Provided with written instructions for each laboratory unit the students work out a thome a solution for the given problem, consisting of a mathematical model, a solution concept and the conception and implementation of the algorithm. Teaming up is supported. After the presentation and discussion of the outcomes the next unit starts. In the final unit of the laboratory the students deal with a more complex problem of the introduced application scenarios.
Presentations and laboratory instructions
The following references are recommended:
- W. Utschick and L. Gerdes: Optimization in Communciations Engineering, Lecture Notes, 2012
- M. S. Bazaara, H. D. Sherali 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