Ects : 3

Enseignant responsable :

• ANGELO FANELLI

Volume horaire : 15

Description du contenu de l'enseignement :

This course provides a broad introduction to continuous optimization with a focus on practical algorithms for the design of engineering systems. We cover a wide variety of continuous optimization topics, introducing the underlying mathematical problem formulations and the algorithms for solving them. All the algorithms will be implemented in the Julia programming language. The course requires some mathematical maturity and assumes prior exposure to multivariable calculus, linear algebra, and probability concepts, although all these concepts will be reviewed during the course.

Pré-requis recommandés :

The course is intended for advanced undergraduates and graduate students. The course requires some mathematical maturity and assumes prior exposure to multivariable calculus, linear algebra, probability concepts and programming. Some review material is provided during the course. All algorithms will be implemented in the Julia programming language, but no prior knowledge of the language is assumed.

Compétence à acquérir :

Derivatives and Gradient, Bracketing, Local Descent, First-Order Methods, Second-Order Methods, Direct Methods, Stochastic Methods

Mode de contrôle des connaissances :

• Brief Written Examination
• Project-Based Assignment

Bibliographie, lectures recommandées

Mykel J. Kochenderfer and Tim A. Wheeler. Algorithms for Optimization. MIT Press

https://mitpress.mit.edu/9780262039420/algorithms-for-optimization/   https://algorithmsbook.com/optimization/files/optimization.pdf