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Semester:
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HS 2022 |
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Type:
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Lecture |
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Lecturer:
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Links:
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KSL |
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.
Literature - S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004 - J. Nocedal, S.J. Wright, Numerical Optimization, Springer, 2006
Form of Teaching The course "Applied Optimization" will be offered in hybrid format this semester. Additionally to face-to-face lectures, there will be the option of remote attendance via a live-stream, or lecture recordings on ILIAS.
Please register to the course on ILIAS in order to get access to all important information and course material.