Convex Optimization in Signal Processing and Communications
| Module Number | 18-pe-2020 |
| Degree Programs | MSc ETiT |
| Credit Points (CP) | 6 |
| Language | English |
| Form of Teaching | Lectures (2 SWS) + tutorials (1 SWS) + Course Project |
| Form of Examination | Written exam, duration 120 min |
Teaching Content
This graduate course introduces the basic theory of convex optimization and illustrates its use with many recent applications in communication systems and signal processing.
Specific topics covered in this course:
- Convex sets and convex functions, convex problems and classes of convex problems (LP, QP, SOCP, SDP, GP)
- Lagrange duality and KKT conditions
- Basics of numerical algorithms and interior point methods
- Optimization tools
- Convex inner and outer approximations for non-convex problems
- Sparse optimization
- Distributed optimization
- Mixed integer linear and non-linear programming
- Applications
The materials for the course as well as all current information will be provided in an accompanying Moodle course.
