| Crédit : 3 ECTS |
| Langue du cours : anglais
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Volume horaire
- CM :
15 h
- Volume horaire global (hors stage) :
15 h
Compétences à acquérir
Derivatives and Gradient, Bracketing, Local Descent, First-Order Methods, Second-Order Methods, Direct Methods, Stochastic Methods.
Description du contenu de l'enseignement
This course provides a comprehensive introduction to continuous optimization, with an emphasis on algorithmic methods and their practical implementation in engineering and computational settings. The course covers a broad range of topics in continuous optimization, presenting both the underlying mathematical problem formulations and the design and analysis of algorithms for their solution. Particular attention is devoted to the implementation of optimization algorithms in the Julia programming language. The course requires a solid level of mathematical maturity and assumes prior familiarity with multivariable calculus, linear algebra, and basic probability theory; these concepts will be reviewed as needed throughout the course.
Mode de contrôle des connaissances
- Brief Written Examination
- Project-Based Assignment
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.
Bibliographie, lectures recommandées
Mykel J. Kochenderfer and Tim A. Wheeler.
Algorithms for Optimization. MIT Press, 2019.
Enseignant responsable
ANGELO FANELLI