Convex Optimization (3V/1U)

WS 2016/17

Lecturer: Prof. Dr.-Ing. C. Ebenbauer
Credits: 3V, 1Ü


Prof. Dr.-Ing. Christian Ebenbauer


Christian Feller

Time and place (3h lecture + 1h exercise)

Wednesdays: 9:45-11:15 in V 9.41
Thursdays: 14:00-16:00 in V 9.41
First lecture: October 26, 2016

Course description

Over the past 15 years, convex optimization has become an important tool in many areas of engineering and applied sciences, such as systems theory and control, mechanics, signal processing, communication, combinatorics and graph theory, machine learning, operations research, electronic circuit design and biology. This course gives an introduction to the theory and application of convex optimization. The software used in the course is Matlab in combination with YALMIP. Some of the covered topics are:

  • Convex sets and functions
  • Linear and quadratic programming (LP/QP)
  • Semidefinite programming (SDP)
  • Linear matrix inequalities (LMIs)
  • Duality theory
  • Relaxation techniques
  • Numerical algorithms
  • Applications

The overall goal of the course is to learn how to use and assess convex optimization techniques (in particular LP and SDP) in order to solve optimization problems. We will also highlight the broad range of applications where these techniques can be applied.

Structure of the courseConvex optimization thinking


No specific course prerequisites are required. The course is given in English.

Additional information, exercises and further course material is avaible on the ILIAS-page of the course.



To the top of the page