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CS 440 — Continuous Optimization
(dt. Kontinuierliche Optimierung)

Level, degree of commitment Advanced module, compulsory elective module
Forms of teaching and learning,
workload		 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)
Language,
Grading		 German,
The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Data Science.
Duration,
frequency		 One semester,
each winter semester
Person in charge of the module's outline Prof. Dr. Christian Rieger

Contents

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.

Prerequisites

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.


Applicability

Module imported from B.Sc. Data Science.

It 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 LAaG Mathematics, this module can be attended in the study area Advanced Modules.

The module is assigned to Applied Mathematics. Further information on eligibility can be found in the description of the study area.


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.