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CS 440 — Continuous Optimization
(dt. Kontinuierliche Optimierung)

Level, degree of commitment Advanced module, depends on importing study program
Forms of teaching and learning,
Lecture (4 SWS), recitation class (2 SWS),
270 hours (90 h attendance, 180 h private study)
Credit points,
formal requirements
9 CP
Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises.
Examination type: Written or oral examination (individual examination)
The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Data Science.
Origin B.Sc. Data Science
One semester,
each winter semester
Person in charge of the module's outline Prof. Dr. Christian Rieger


Introduction to Continuous Optimization. Fundamentals of nonlinear optimization: Kuhn-Tucker theory, minimization of nonlinear functions; minimization of nonlinear functions with constraints, numerical methods.

Qualification Goals

Students will be able to,

  • use sound knowledge of the theory and practice of basic methods of optimization,
  • recognize and assess the relevance of optimization methods to practical problems from various application areas such as nonlinear regression, machine learning, or parameter optimization,
  • model and solve optimization problems for practical problems,
  • to proceed according to mathematical working methods (developing mathematical intuition and its formal justification, abstraction, proof),
  • speak freely about scientific content, both in front of an audience and in a discussion.


None. The competences taught in the following modules are recommended: either Linear Algebra I and Analysis I and Analysis II or Basic Linear Algebra and Basic Real Analysis and Basics of Advanced Mathematics.


The module can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Mathematics
  • B.Sc. Business Mathematics
  • M.Sc. Mathematics
  • M.Sc. Business Informatics
  • M.Sc. Business Mathematics
  • LAaG Mathematics
  • BA Minor Mathematics

When studying B.Sc. Data Science, this module must be completed in the study area Basic and Continuing Modules in Mathematics.

The module can also be used in other study programs (export module).

Recommended Reading

  • Wird jeweils in der Modulankündigung angegeben.
  • Standardwerke sind z.B.
  • Alt, W.: Nichtlineare Optimierung, Vieweg, 2002
  • Jarre, F., Stoer, J.: Nonlinear Programming, Springer, 2004
  • Fletcher, R.: Practical Methods of Optimization, 2nd Edition, John Wiley & Sons, 1987
  • Nocedal, J., Wright, S.: Numerical Optimization, Springer, 2002

Please note:

This page describes a module according to the latest valid module guide in Winter semester 2023/24. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

  • Winter 2016/17 (no corresponding element)
  • Summer 2018 (no corresponding element)
  • Winter 2018/19 (no corresponding element)
  • Winter 2019/20 (no corresponding element)
  • Winter 2020/21 (no corresponding element)
  • Summer 2021 (no corresponding element)
  • Winter 2021/22 (no corresponding element)
  • Winter 2022/23 (no corresponding element)
  • Winter 2023/24

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.