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040114 UK Optimization under Uncertainty (MA) (2021S)
Continuous assessment of course work
Labels
REMOTE
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Th 11.02.2021 09:00 to Mo 22.02.2021 12:00
- Registration is open from Th 25.02.2021 09:00 to Fr 26.02.2021 12:00
- Deregistration possible until We 31.03.2021 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Monday 01.03. 08:00 - 09:30 Digital
- Monday 08.03. 08:00 - 09:30 Digital
- Monday 15.03. 08:00 - 09:30 Digital
- Monday 22.03. 08:00 - 09:30 Digital
- Monday 12.04. 08:00 - 09:30 Digital
- Monday 19.04. 08:00 - 09:30 Digital
- Monday 26.04. 08:00 - 09:30 Digital
- Monday 03.05. 08:00 - 09:30 Digital
- Monday 10.05. 08:00 - 09:30 Digital
- Monday 17.05. 08:00 - 09:30 Digital
- Monday 31.05. 08:00 - 09:30 Digital
- Monday 07.06. 08:00 - 09:30 Digital
- Monday 14.06. 08:00 - 09:30 Digital
- Monday 21.06. 08:00 - 09:30 Digital
- Monday 28.06. 08:00 - 09:30 Digital
Information
Aims, contents and method of the course
Study practically relevant aspects of operations research including in particular the consideration of uncertain input data (stochastic optimization, robust optimization)The main themes are discussed first in form of a lecture. Homeworks then give the opportunity to apply and deepen the teaching material.
Assessment and permitted materials
written exam, blackboard exercises
Minimum requirements and assessment criteria
This course should help graduate students to:
a) develop mathematical models for (real world) optimization problems
b) apply different concepts to treat uncertain input data in optimization and understand the consequences implied by choosing on of these techniquesThe test measures the ability to solve simple examples in the dicussed fields.
a) develop mathematical models for (real world) optimization problems
b) apply different concepts to treat uncertain input data in optimization and understand the consequences implied by choosing on of these techniquesThe test measures the ability to solve simple examples in the dicussed fields.
Examination topics
1) Single stage stochastic optmization, in particular mean-variance optimization, expected utility, acceptability measures
2) Mixed Integer Optimization
3) Recourse problems
4) Markov chains
5) Markov decision processes
2) Mixed Integer Optimization
3) Recourse problems
4) Markov chains
5) Markov decision processes
Reading list
Cornuejols, Pena, Tütüncü (2018), Optimization Methods in Finance, 2nd edition, CambridgeHillier/Lieberman, Introduction to Operations Research, 7th edition
Association in the course directory
Last modified: Fr 12.05.2023 00:12