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401-3904-22L 5 Credits BSC , DR , MSC D-ITET , D-INFK , D-MATH
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Convex Optimization

Lecturers & Examiners: Dr. Adam Andrzej Kurpisz
VVZ CR 4.8

Last Updated: 2026-06-01 11:33:15

Abstract

Introduction to Convex Optimization with a focus on algorithms and the numerous applications of Convex Optimization.

Objective

The main goal of this course is to obtain a solid understanding of classical Convex Optimization techniques and their numerous applications, including in Data Science, Machine Learning, and, more generally, in science and engineering. Apart from building up a solid foundational understanding of Convex Optimization, students also get hands-on experience through regular coding exercises. This aims at providing a holistic view on the process of identifying, modeling, and solving a wide range of computational questions that can be cast as Convex Optimization problems.

Content

Key topics include: - Introduction to Convex Optimization. - Subclasses of Convex Optimization: Semidefinite Programming, Second-Order Cone Programming and Geometric Programming. - Applications of Convex Optimization in science and engineering. - Algorithms for Convex Optimization.

Resources

Lecture Notes

A script will be provided.

Literature

- Boyd, S., \& Vandenberghe, L. (2004). Convex Optimization. Cambridge: Cambridge University Press. doi:10.1017/CBO9780511804441

General Information

Language
English
Levels
BSC , DR , MSC
Frequency
Yearly recurring

Examination

Type
end-of-semester examination
Mode
written 180 minutes
Aids
None

Course Components

Type Title Time & Place Hours
lecture with exercise Convex Optimization
Groups are selected in myStudies. Lecture Mon 14-16 Exercises Thu 16-17 or Fri 12-13
  • Mon 14:15-16:00 (HG E 1.1)
  • Thu 16:15-17:00 (ML H 41.1)
  • Fri 12:15-13:00 (ML F 38)
3 h weekly

Offered In