## Module Handbook

• Dynamischer Default-Fachbereich geändert auf MAT

# Module MAT-52-16A-M-7

## Module Identification

Module Number Module Name CP (Effort)
MAT-52-16A-M-7 Introduction to Robust Optimization 4.5 CP (135 h)

## Basedata

CP, Effort 4.5 CP = 135 h 1 Sem. irreg. [7] Master (Advanced) [EN] English Schöbel, Anita, Prof. Dr. (PROF | DEPT: MAT) Krumke, Sven Oliver, Prof. Dr. (PROF | DEPT: MAT) Ruzika, Stefan, Prof. Dr. (PROF | DEPT: MAT) Schöbel, Anita, Prof. Dr. (PROF | DEPT: MAT) [MAT-OPT] Optimisation [MAT-88.105-SG] M.Sc. Mathematics [NORM] Active

## Notice

This module is part of the module [MAT-52-16-M-7] "Robust Optimization".

## Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
2V+1U MAT-52-16A-K-7
Introduction to Robust Optimization
P 42 h 93 h - - PL1 4.5 irreg.
• About [MAT-52-16A-K-7]: Title: "Introduction to Robust Optimization"; Presence-Time: 42 h; Self-Study: 93 h

## Examination achievement PL1

• Form of examination: oral examination (20-30 Min.)
• Examination Frequency: irregular (by arrangement)

## Evaluation of grades

The grade of the module examination is also the module grade.

## Contents

The following concepts for robust optimization are presented:
• Strict robustness,
• MinMax regret robustness,
• Recovery robustness,
• Light robustness.

For these concepts formulations and algorithms for different problem classes (e.g., for linear, non-linear, integer, combinatorial optimization problems) and for different uncertainty sets are developed.

## Competencies / intended learning achievements

Upon successful completion of this module, the students are familiar with different methods to solve and model the uncertainty in optimization. They know and understand the complexities of the approaches and are able to solve the problems with the help of specific algorithms. In particular, for problems with a suitable structure, they are able to construct reformulations as non-uncertain optimization problems. Moreover, they can critically assess the possibilities and limitations of the use of the concepts. They understand the proofs presented in the lecture and are able to reproduce and explain them.

In the exercise classes, the students have deepened their knowledge and gained a skilled, precise and independent handling of the terms, propositions and methods taught in the lecture.

## Literature

• A. Ben-Tal, L. El Ghaoui, A. Nemirovski: Robust Optimization,
• P. Kouvelis, G. Yu: Robust Discrete Optimization and Its Applications.

None

## References to Module / Module Number [MAT-52-16A-M-7]

Module-Pool Name
[MAT-52-MPOOL-7] Specialisation Mathematical Optimisation (M.Sc.)
[MAT-AM-MPOOL-7] Applied Mathematics (Advanced Modules M.Sc.)