Optimization problems are central to almost every field of engineering. Examples range from the dimensioning of components and the minimization of elastic energy in the finite element method to modern AI and machine learning methods. This course introduces the fundamentals of nonlinear optimization of differentiable functions. It gives an overview of the main classes of optimization algorithms, and participants learn which method is best suited to which problem in practice.
In the accompanying exercise sessions, participants will implement the solution methods discussed in the lectures and gain hands-on experience with optimization packages in Python.
Lectures will take place every Wednesday from 12:00 to 13:30, in room V15 S04 C42, starting on 14 October 2026. The exercise sessions follow from 13:30 to 15:00 in the same room.
- verantwortliche Lehrperson: Lukas Jabs
- verantwortliche Lehrperson: Mauli Mirajkar
- verantwortliche Lehrperson: Matti Schneider