First half of the semester: Introduction to the main methods of numerical optimization with focus on stochastic methods such as genetic algorithms, evolutionary strategies, etc. Second half of the semester: Each participant implements a selected optimizer and applies it on a problem of practical interest.
Learning objective
Numerical optimization is of increasing importance for the development of devices and for the design of numerical methods. The students shall learn to select, improve, and combine appropriate procedures for efficiently solving practical problems.
Content
Typical optimization problems and their difficulties are outlined. Well-known deterministic search strategies, combinatorial minimization, and evolutionary algorithms are presented and compared. In engineering, optimization problems are often very complex. Therefore, new techniques based on the generalization and combination of known methods are discussed. To illustrate the procedure, various problems of practical interest are presented and solved with different optimization codes.
Lecture notes
PDF of a short skript (39 pages) plus the view graphs are provided
Prerequisites / Notice
Lecture only in the first half of the semester, exercises in form of small projects in the second half, presentation of the results in the last week of the semester.
