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This entry is from Winter semester 2021/22 and might be obsolete. No current equivalent could be found.

Linear Optimization
(dt. Lineare Optimierung)

Level, degree of commitment Advanced module, required 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 B.Sc. Business Mathematics.
Duration,
frequency		 One semester,
each winter semester
Person in charge of the module's outline Prof. Dr. Thomas Surowiec

Contents

Basics of convex geometry and duality theory, numerical methods such as simplex methods, dual simplex methods or inner-point methods. Statements on the complexity of the procedures.


Qualification Goals

Unchecked automatic translation:
The students shall

  • learn the structural basics of linear optimization problems in order to understand the basic operation of the methods,
  • recognize the importance of central concepts, for example from duality theory, for the discussion of optimization problems,
  • learn to select problem-adapted procedures,
  • acquire the basic knowledge for advanced modules on general optimization problems,
  • practice mathematical working methods (development of mathematical intuition and its formal justification, training of the ability to abstract, proof techniques),
  • improve their oral communication skills in the exercises 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 or Basic Linear Algebra, either Analysis I or Basic Real Analysis.


Applicability

Module imported from B.Sc. Business Mathematics.

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

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

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


Recommended Reading

  • Nocedal, J., Wright, S.: Numerical Optimization, Springer, 1999;
  • Borgwardt, K.K.: Optimierung, Operations Research und Spieltheorie, Birkhäuser, Basel, 2001.



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2021/22. 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.