| Lecturer (assistant) | |
|---|---|
| Duration | 2 SWS |
| Term | Wintersemester 2025/26 |
| Language of instruction | English |
Admission information
See TUMonline
Note: Registration via TUMonline starting on 22nd September 2025.
Note: Registration via TUMonline starting on 22nd September 2025.
Description
Content of the lecture
1. Introduction to the multi-criteria paradigm for embedded systems design
- Uni-criterion vs multi-criteria
- Modeling and challenges
2. Optimization methods
- Linear programming
- Metaheuristics (e.g. genetic algorithms, simulated annealing)
- Multi-objective optimization for design space exploration
3. Decision making processes
- Voting theory
- Multi-criteria decision analysis
- Game theory
- Decision under risk and uncertainty
During the lecture, the theoretical content will be accompanied by examples illustrating the following concepts: problem abstraction and modeling, algorithm selection and implementation, multi-criteria decision making and analysis. Thereby both functional and non-functional aspects will be considered. More elaborate exercises on these topics will be done in self-study by the students.
1. Introduction to the multi-criteria paradigm for embedded systems design
- Uni-criterion vs multi-criteria
- Modeling and challenges
2. Optimization methods
- Linear programming
- Metaheuristics (e.g. genetic algorithms, simulated annealing)
- Multi-objective optimization for design space exploration
3. Decision making processes
- Voting theory
- Multi-criteria decision analysis
- Game theory
- Decision under risk and uncertainty
During the lecture, the theoretical content will be accompanied by examples illustrating the following concepts: problem abstraction and modeling, algorithm selection and implementation, multi-criteria decision making and analysis. Thereby both functional and non-functional aspects will be considered. More elaborate exercises on these topics will be done in self-study by the students.
Prerequisites
- Data structures
- Basic programming skills in Python or Matlab; alternatively C/C++ or Java
- Basic knowledge of probability and statistics (probability axioms and theorems, e.g. Bayes' Theorem and its applications; typical probability distributions, e.g. exponential, Gaussian, etc.)
- Basic programming skills in Python or Matlab; alternatively C/C++ or Java
- Basic knowledge of probability and statistics (probability axioms and theorems, e.g. Bayes' Theorem and its applications; typical probability distributions, e.g. exponential, Gaussian, etc.)
Teaching and learning methods
The technical content will be introduced by means of lectures with PowerPoint presentations and will be illustrated with small examples that will be included in the slides. The students are encouraged to ask questions. In addition to the individual learning methods of the students, the transfer of the theoretical knowledge to its practical application will be achieved through illustrative examples during the lectures and additional exercises to be done in self study manner. All the course material will be made available to the students through Moodle.