This information is indicative and can be subject to change.
Numerical methods in optimization
Teacher: Kengy Barty
E-mail: kengy.barty@univ-paris1.fr
ECTS: 2.5
Evaluation: Written exam / report of a numerical project.
Previsional Place and time:
Prerequisites: A good knowledge of differential calculus and Hilbert space properties, Python programming and Notebook Python
Aim of the course: The course addresses numerical techniques in order to determine the optimal solution of optimisation problems. Half of the course focuses on the effective implementation in Python of the algorithms studied.
References: Convex Optimization, Stephen Boyd and Lieven Vandenberghe
Teaching Method Lectures by the teacher and presentation and a numerical project in Pyhton for the students
Numerical methods in optimization
Teacher: Kengy Barty
E-mail: kengy.barty@univ-paris1.fr
ECTS: 2.5
Evaluation: Written exam / report of a numerical project.
Previsional Place and time:
Prerequisites: A good knowledge of differential calculus and Hilbert space properties, Python programming and Notebook Python
Aim of the course: The course addresses numerical techniques in order to determine the optimal solution of optimisation problems. Half of the course focuses on the effective implementation in Python of the algorithms studied.
References: Convex Optimization, Stephen Boyd and Lieven Vandenberghe
Teaching Method Lectures by the teacher and presentation and a numerical project in Pyhton for the students
