- modelling with robust optimization,
- modelling and concepts of uncertainty set,
- model reformulations in solvable problems.
- problem complexities,
- linear, non-linear and integer optimization in robust optimization,
- application to combinational problems,
- approaches from robust optimization: Soyster approach for uncertain problems; strict robustness; robust regularization; minimax regret approach; adjustable robustness; approach by Bertsimas and Sim; recoverable robustness.
Module MAT-52-16-M-7
Robust Optimization (M, 9.0 LP)
Module Identification
Module Number | Module Name | CP (Effort) |
---|---|---|
MAT-52-16-M-7 | Robust Optimization | 9.0 CP (270 h) |
Basedata
CP, Effort | 9.0 CP = 270 h |
---|---|
Position of the semester | 1 Sem. irreg. |
Level | [7] Master (Advanced) |
Language | [EN] English |
Module Manager | |
Lecturers | |
Area of study | [MAT-OPT] Optimisation |
Reference course of study | [MAT-88.105-SG] M.Sc. Mathematics |
Livecycle-State | [NORM] Active |
Courses
Type/SWS | Course Number | Title | Choice in Module-Part | Presence-Time / Self-Study | SL | SL is required for exa. | PL | CP | Sem. | |
---|---|---|---|---|---|---|---|---|---|---|
4V+2U | MAT-52-16-K-7 | Robust Optimization
| P | 84 h | 186 h | - | - | PL1 | 9.0 | irreg. |
- About [MAT-52-16-K-7]: Title: "Robust Optimization"; Presence-Time: 84 h; Self-Study: 186 h
Examination achievement PL1
- Form of examination: oral examination (20-30 Min.)
- Examination Frequency: irregular (by arrangement)
- Examination number: 86387 ("Robust Optimization")
Evaluation of grades
The grade of the module examination is also the module grade.
Contents
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 algorithms. 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. Moreover, they have learned to apply algorithms and models to practical problems by the use of appropriate, higher programming languages and MIP solvers.
Literature
- A. Ben-Tal, L. El Ghaoui, A. Nemirovski: Robust Optimization,
- P. Kouvelis, G. Yu: Robust Discrete Optimization and Its Applications.
Requirements for attendance (informal)
Modules:
- [MAT-10-1-M-2] Fundamentals of Mathematics (M, 28.0 LP)
- [MAT-14-13-M-3] Linear and Network Programming (M, 9.0 LP)
- [MAT-50-11-M-4] Integer Programming: Polyhedral Theory and Algorithms (M, 9.0 LP)
Requirements for attendance (formal)
None
References to Module / Module Number [MAT-52-16-M-7]
Module-Pool | Name | |
---|---|---|
[MAT-52-MPOOL-7] | Specialisation Mathematical Optimisation (M.Sc.) | |
[MAT-AM-MPOOL-7] | Applied Mathematics (Advanced Modules M.Sc.) |