040114 UK Optimization under Uncertainty (MA) (2024S)
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 Mo 12.02.2024 09:00 to We 21.02.2024 12:00
- Registration is open from Mo 04.03.2024 16:30 to Tu 12.03.2024 23:59
- Deregistration possible until Th 14.03.2024 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Monday
04.03.
08:00 - 09:30
Digital
Monday
11.03.
08:00 - 09:30
Digital
Monday
18.03.
08:00 - 09:30
Digital
Monday
08.04.
08:00 - 09:30
Digital
Monday
15.04.
08:00 - 09:30
Digital
Monday
22.04.
08:00 - 09:30
Digital
Monday
29.04.
08:00 - 09:30
Digital
N
Monday
06.05.
08:00 - 09:30
Digital
Monday
13.05.
08:00 - 09:30
Digital
Monday
27.05.
08:00 - 09:30
Digital
Monday
03.06.
08:00 - 09:30
Digital
Monday
10.06.
08:00 - 09:30
Digital
Monday
17.06.
08:00 - 09:30
Digital
Monday
24.06.
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: Mo 04.03.2024 16:25