Optimization problems need to be solved in almost every domain. Nurses and doctors, for example, need to be assigned to shifts in a hospital without violating given constraints, investment portfolios have to be built in order to maximize the return, or the many parameters of a weather forecast model have to be chosen in order to best fit previously measured data. In the context of machine learning, optimization problems occur frequently as well, for example, in reinforcement learning or the hyperparameter tuning of machine learning algorithms.
This introductory course on optimization aims at teaching the basic knowledge about optimization theory and the design and analysis of optimization algorithms. One goal is to provide the necessary background that will allow the participants to practically address the various optimization problems they might encounter in the future.
The lecture covers both fundamental aspects in discrete optimization (such as greedy algorithms, dynamic programming, heuristics) and continuous optimization (introducing gradient-based methods as well as derivative-free algorithms).
The lecture is given by Anne Auger and Dimo Brockhoff from September till November 2019 in a total of 21hrs.
In case you are interested in pursuing the topic during a Master's thesis project, please consider to take the subsequent advanced lecture on optimization and this list of thesis projects.
| Anne Auger and Dimo Brockhoff | ||||||
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