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| Detailinformationen |
| Quellcurriculum |
Masterstudium Economic and Business Analytics 2025W |
| Lernergebnisse |
Kompetenzen |
| (*)Students are able to understand and use advanced methods and techniques of mathematical programming, in MILP. They know how to develop sophisticated solution algorithms based on MILP to solve real-world problems from various application areas, including business analytics.
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| Fertigkeiten |
Kenntnisse |
(*)- Learning Outcome 3 (LO3): Construct proofs and produce new results with respect to the considered theoretical foundations
- Learning Outcome 4 (LO4): Apply advanced concepts of MILP like valid inequalities, facets, decomposition methods in practice using solver software
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(*)- Learning Outcome 1 (LO1): Understand advanced concepts of MILP like valid inequalities, facets, decomposition methods, and generalizations like semidefinite/convex optimization
- Learning Outcome 2 (LO2): Understand how modern MILP solver software can be interfaced and enhanced to design sophisticated solution algorithms
Course topics:
- Formulations
- Duality theory
- Cutting plane algorithms and branch-and-cut
- Valid inequalities, facets and polyhedral theory
- Benders decomposition
- Dantzig-Wolfe decomposition
- Lagrangian relaxation
- Semidefinite/convex optimization
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| Beurteilungskriterien |
(*)Regular homework exercises that must be submitted online via Moodle. Students will be selected to present and explain their homework in class. The homework exercises are worth 40 points.
Exam at the end of the semester with 60 points. There is a possibility to repeat it in case of negative results or scheduling issues (retry exam). The exam consists of theoretical and practical questions. It lasts 90 minutes.
Final grades will be given as follows:
| Points | Grade |
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| 87,5 - 100 | 1 |
| 75 - 87 | 2 |
| 62,5 - 74,5 | 3 |
| 50 - 62,0 | 4 |
| 0 - 49,5 | 5 |
Both the homework exercises and the exam cover all the learning outcomes.
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| Lehrmethoden |
(*)The course uses a combination of different teaching methods in order to
- maximize the motivation and attention of the students.
- address the learning objectives in the didactically best way.
This includes the following
- Teacher-centred information inputs, supported by slides and literature
- Development of content in collaboration with the students on the computer and the black board
- Presentation of homework exercises by students to ensure comprehension of the content, followed by joint discussions with the whole group
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)- Slides
- In-class exercises with solutions
- Reading material
- L. Wolsey, Integer Programming, current edition
- M. Conforti, G. Cornuejols, G. Zambelli, Integer Programming, current edition
- H. P. Williams, Model Building in Mathematical Programming, current edition
- S. Boyd, L. Vandenberghe, Convex Optimization
- Pointers to additional literature
(All content is provided via Moodle)
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| Lehrinhalte wechselnd? |
Nein |
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