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This entry is from Winter semester 2019/20 and might be obsolete. No current equivalent could be found.

Nonlinear Optimization
(dt. Nichtlineare Optimierung)

Level, degree of commitment Specialization 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
Language,
Grading		 German,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Business Mathematics.
Duration,
frequency		 One semester,
Regularly alternating with other courses in the research area of optimization
Person in charge of the module's outline Prof. Dr. Thomas Surowiec

Contents

Fundamentals of nonlinear optimization: Kuhn-Tucker theory, minimization of nonlinear functions; minimization of nonlinear functions with constraints

Fundamentals of nonlinear optimization: Kuhn-Tucker theory, minimization of nonlinear functions; minimization of nonlinear functions with constraints


Qualification Goals

The students shall

  • acquire a sound knowledge of the theory and practice of basic methods of optimization
  • learn to recognize and assess the relevance of optimization methods for practical problems from different application areas such as parameter optimization, nonlinear regression, approximation, or optimal control,
  • acquire the ability to model and solve optimization problems in practical situations,
  • practice mathematical methods (development of mathematical intuition and its formal justification, training of the ability to abstract, proof methods),
  • improve their oral communication skills in the recitation classes by practicing free speech in front of an audience and during 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 or Basic Real Analysis and Basics of Advanced Mathematics.


Applicability

Module imported from M.Sc. Business Mathematics.

It can be attended at FB12 in study program(s)

  • B.Sc. Mathematics
  • B.Sc. Business Mathematics
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Mathematics
  • M.Sc. Business Mathematics
  • LAaG Mathematics

When studying M.Sc. Data Science, this module can be attended in the study area Advanced and Specialization Modules in Mathematics.

Die Wahlmöglichkeit des Moduls ist dadurch beschränkt, dass es dem Schwerpunkt Scientific Computing zugeordnet ist.


Recommended Reading

  • 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 2019/20. 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:

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.