Nowadays, numerical optimization is a fundamental component of many applications, e.g. in engineering, finances, biomedical applications, machine learning and many more. Therefore, understanding the underlying principles and available algorithms of numerical optimization can be considered an essential skill for a computer scientist. This course offers an applied introduction, covering a broad range of practically important topics, as for instance: Mathematical modeling of real-world problems, theory of convexity, Lagrange dualism, algorithms for unconstrained and constrained optimization with inequalities (e.g. gradient descent, Newton’s method, trust-region methods, active set approaches, interior point methods, …). A major goal of the course is to train students in appropriately modelling optimization problems, and identifying suitable optimization algorithms, based on the understanding of their specific strengths and weaknesses.
T3 – Advanced Information Processing |
T6 – Data Science
Upon successful completion of this class, a student will be able to:
David Bommes |
The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_1531266.html.
Schedules and Rooms
|Schedule||Thursday, 09:15 - 12:00|
|Location||UniBE, Engehaldenstrasse 8|
|Evaluation type||written exam|