Please be advised: Due to database maintenance between 6.30 - 8-30 today, if unforseen errors occur please accept our apologies for any inconvenience.

# 389.122 Convex Optimization for Signal Processing and Communications This course is in all assigned curricula part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_21",{id:"j_id_21",showEffect:"fade",hideEffect:"fade",target:"isAllSteop"});});This course is in at least 1 assigned curriculum part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_23",{id:"j_id_23",showEffect:"fade",hideEffect:"fade",target:"isAnySteop"});}); 2024S 2023S 2022S 2021S 2020S 2019S 2018S 2017S 2016S 2015S 2014S 2013S 2012S 2008W

2022S, VO, 2.0h, 3.0EC

## Properties

• 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

Motivation

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

Oral

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

First class: March 2, 2022 at 1pm

## Course dates

DayTimeDateLocationDescription
Wed13:30 - 15:0002.03.2022 - 29.06.2022Sem 389 Vorlesung
Convex Optimization for Signal Processing and Communications - Single appointments
DayDateTimeLocationDescription
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

oral exam

## Course registration

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

## Curricula

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

## Literature

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

Online available as pdf at http://www.stanford.edu/~boyd/cvxbook/

## 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.

English