DE

Modul

Mathematical Programming [M-WIWI-101473]

Credits
9
Recurrence
Jedes Semester
Duration
1 Semester
Language
German/English
Level
4
Version
7

Responsible

Organisation

  • KIT-Fakultät für Wirtschaftswissenschaften

Part of

Bricks

Identifier Name LP
T-WIWI-102724 Nonlinear Optimization I 4.5
T-WIWI-102715 Operations Research in Supply Chain Management 4.5
T-WIWI-102725 Nonlinear Optimization II 4.5
T-WIWI-111247 Mathematics for High Dimensional Statistics 4.5
T-WIWI-102855 Parametric Optimization 4.5
T-WIWI-110162 Optimization Models and Applications 4.5
T-WIWI-102720 Mixed Integer Programming II 4.5
T-WIWI-102726 Global Optimization I 4.5
T-WIWI-102719 Mixed Integer Programming I 4.5
T-WIWI-103124 Multivariate Statistical Methods 4.5
T-WIWI-103638 Global Optimization I and II 9
T-WIWI-103637 Nonlinear Optimization I and II 9
T-WIWI-102727 Global Optimization II 4.5
T-WIWI-112109 Topics in Stochastic Optimization 4.5
T-WIWI-106548 Advanced Stochastic Optimization 4.5
T-WIWI-106549 Large-scale Optimization 4.5
T-WIWI-102856 Convex Analysis 4.5
T-WIWI-111587 Multicriteria Optimization 4.5
T-WIWI-102723 Graph Theory and Advanced Location Models 4.5

Competence Certificate

The assessment is carried out as partial exams (according to Section 4(2), 1 or 2 of the examination regulation) of the single courses of this module, whose sum of credits must meet the minimum requirement of credits of this module. The assessment procedures are described for each course of the module seperately.

The overall grade of the module is the average of the grades for each course weighted by the credits and truncated after the first decimal.

Competence Goal

The student

  • names and describes basic notions for advanced optimization methods, in particular from continuous and mixed integer programming,
  • knows the indispensable methods and models for quantitative analysis,
  • models and classifies optimization problems and chooses the appropriate solution methods to solve also challenging optimization problems independently and, if necessary, with the aid of a computer,
  • validates, illustrates and interprets the obtained solutions,
  • identifies drawbacks of the solution methods and, if necessary, is able to makes suggestions to adapt them to practical problems.

Prerequisites

There is no compulsory course in the module.

Content

The modul focuses on theoretical foundations as well as solution algorithms for optimization problems with continuous and mixed integer decision variables.

Workload

The total workload for this module is approximately 270 hours.