ECTS : 3
Volume horaire : 15
Description du contenu de l'enseignement :
Course Content:
- Derivatives and Gradients
- Bracketing Techniques
- Local Descent Methods
- First-Order Optimization Methods
- Second-Order Optimization Methods
- Direct Optimization Methods
- Stochastic Optimization Techniques
- Introduction to Advanced Topics: Constrained Optimization, Multi-objective Optimization, and more.
Additionally, the course will provide a foundational introduction to Julia programming, integrated throughout the curriculum.
Compétence à acquérir :
The course provides a broad introduction to optimization with a focus on practical algorithms for the design of engineering systems. The course covers a wide variety of optimization topics, introducing the underlying mathematical problem formulations and the algorithms for solving them.
Mode de contrôle des connaissances :
Bibliographie, lectures recommandées :
-Algorithms for Optimization. Mykel J. Kochenderfer and Tim A. Wheeler
https://mitpress.mit.edu/9780262039420/algorithms-for-optimization/
-Julia Documentation