Stochastic optimization techniques

Optimization problems arising in practice mostly contain several random parameters. Hence, in order to get robust optimal solutions with respect to random parameter variations, the available statistical information about the random data should be considered already at the planning phase. Thus, the original problem with random coefficients must be replaced by an appropriate deterministic substitute problem. This proceedings volume of the 4th GAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications" held June 27-29, 2000 at the Federal Armed Forces University Munich, Neubiberg/Munich contains new methods for the approximation and numerical solution of deterministic substitute problems, especially the handling of mean value and probability functions as objective and/or constraint functions. Moreover, many concrete applications from engineering and operations research can be found in this book.