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 complexity theory and dynamic programming over approximation algorithms and heuristics to linear programming, 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 September till December 2015 in a total of 11x3hrs.
| Dimo Brockhoff |
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