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.
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 discrete and continuous optimization problems. Its topics range from basic graph theory, greedy algorithms, dynamic programming and approximation algorithms to gradient-based methods and derivative-free algorithms. Each lecture will be complemented by hands-on exercises during which the introduced theoretical and algorithmic concepts are applied to concrete optimization problems.
The main topics of the lecture are:
The lecture is given by Dimo Brockhoff from October till December 2016 in a total of 11x3hrs.
In case you are interested in pursuing the topic during a Master's thesis project, please see this list of thesis projects.
| Dimo Brockhoff |
|