Event
Parametric Optimization [WS202550115]
Lecturers
Organisation
- Kontinuierliche Optimierung
Part of
- Brick Parametric Optimization | Industrial Engineering and Management (M.Sc.)
- Brick Parametric Optimization | Economics Engineering (M.Sc.)
- Brick Parametric Optimization | Information Systems (M.Sc.)
- Brick Parametric Optimization | Information Engineering and Management (M.Sc.)
- Brick Parametric Optimization | Economathematics (M.Sc.)
Literature
- J.F. Bonnans, A. Shapiro, Perturbation Analysis of Optimization Problems, Springer, New York, 2000
- W. Dinkelbach, Sensitivitätsanalysen und parametrische Programmierung, Springer, Berlin, 1969
- J. Guddat, F. Guerra Vasquez, H.Th. Jongen, Parametric Optimization: Singularities, Pathfollowing and Jumps, Wiley, Chichester, and Teubner, Stuttgart, 1990
- R.T. Rockafellar, R.J.B. Wets, Variational Analysis, Springer, Berlin, 1998
Appointments
- 05.11.2020 10:00 - 11:30
- 12.11.2020 10:00 - 11:30
- 19.11.2020 10:00 - 11:30
- 26.11.2020 10:00 - 11:30
- 03.12.2020 10:00 - 11:30
- 10.12.2020 10:00 - 11:30
- 17.12.2020 10:00 - 11:30
- 07.01.2021 10:00 - 11:30
- 14.01.2021 10:00 - 11:30
- 21.01.2021 10:00 - 11:30
- 28.01.2021 10:00 - 11:30
- 04.02.2021 10:00 - 11:30
- 11.02.2021 10:00 - 11:30
- 18.02.2021 10:00 - 11:30
Note
Parametric optimization deals with the influence of parameters on the solution of optimization problems. In optimization practice, such investigations play a fundamental role in order to be able to assess the quality of a numerically obtained solution or to make quantitative statements about its parameter dependence. Furthermore, a number of parametric optimization methods exist, and parametric problems occur in applications such as game theory, geometric optimization problems, and robust optimization. The lecture gives a mathematically sound introduction to these topics and is structured as follows:
- Introductory examples and terminology
- Sensitivity
- Stability and regularity conditions
- Applications: semi-infinite optimization and Nash games
Remark:
Prior to the attendance of this lecture, it is strongly recommend to acquire basic knowledge on optimization problems in one of the lectures "Global Optimization I and II" and "Nonlinear Optimization I and II".
Learning objectives:
The student
- knows and understands the fundamentals of parametric optimization,
- is able to choose, design and apply modern techniques of parametric optimization in practice.