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389.122 Convex Optimization for Signal Processing and Communications
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2022S, VO, 2.0h, 3.0EC
Quinn ECTS survey


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VO Lecture
  • Format: Presence

Learning outcomes

After successful completion of the course, students are able to recognise and formulate convex optimisation programs for applications of signal processing, machine learnung, and communications, and to solve them. They are able to formulate the corresponding dual problem and the Karush-Kuhn-Tucker conditions. They are able to solve simple convex optimization programs numerically with the modelling language "cvx".


Subject of course


Convex optimization theory deals with how to optimally and efficiently solve a class of optimization problems. Although the theory of convex optimization theory dates back to the early twentieth century, it has found a rapidly increasing number of applications in the engineering sciences during the 1990s. This is largely due to the development of efficient algorithms for the solution of large classes of convex optimization problems but also due to an increased awareness of the theory. Today, many of the papers published in the signal processing and communications literature apply tools from convex optimization in solving and analyzing the relevant problems. The theory of convex optimisation is one of the mathematical foundations for machine learning. Thus, an understanding of convex optimization is necessary to understand the recent literature in either field. The theory part of the course will follow the book "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe. Applications and example will be taken directly from the recent literature on signal processing and communications.

Course topics

  • the mathematical theory of convex functions and sets
  • the concept of duality and generalized inequalities
  • classical types of optimization problems
  • algorithms for solving convex optimization problems
  • applications in signal processing, machine learning, and communications

Teaching methods

Conventional lectures on the blackboard supported by electronic media. The theory part closely follows the text book.

Mode of examination


Additional information

The lecture is held each Wednesday at 1:30pm in Sem. 389 (CG0118).

First class: March 2, 2022 at 1pm



Course dates

Wed13:30 - 15:0002.03.2022 - 29.06.2022Sem 389 Vorlesung
Convex Optimization for Signal Processing and Communications - Single appointments
Wed02.03.202213:30 - 15:00Sem 389 Vorlesung
Wed09.03.202213:30 - 15:00Sem 389 Vorlesung
Wed16.03.202213:30 - 15:00Sem 389 Vorlesung
Wed23.03.202213:30 - 15:00Sem 389 Vorlesung
Wed30.03.202213:30 - 15:00Sem 389 Vorlesung
Wed06.04.202213:30 - 15:00Sem 389 Vorlesung
Wed27.04.202213:30 - 15:00Sem 389 Vorlesung
Wed04.05.202213:30 - 15:00Sem 389 Vorlesung
Wed11.05.202213:30 - 15:00Sem 389 Vorlesung
Wed18.05.202213:30 - 15:00Sem 389 Vorlesung
Wed25.05.202213:30 - 15:00Sem 389 Vorlesung
Wed01.06.202213:30 - 15:00Sem 389 Vorlesung
Wed08.06.202213:30 - 15:00Sem 389 Vorlesung
Wed15.06.202213:30 - 15:00Sem 389 Vorlesung
Wed22.06.202213:30 - 15:00Sem 389 Vorlesung
Wed29.06.202213:30 - 15:00Sem 389 Vorlesung

Examination modalities

oral exam


Course registration

Begin End Deregistration end
03.03.2022 00:00 02.07.2022 00:00


Study CodeObligationSemesterPrecon.Info
710 FW Elective Courses - Electrical Engineering Elective


Stephen Boyd and Lieven Vandenberghe, "Convex Optimization," Cambridge Univ. Press, 2004 (ISBN 0521833787).

Online available as pdf at

Previous knowledge

The students are required to have a working knowledge of linear algebra and basic calculus. No previous knowledge of convex optimization is required.