Springer ebook3-Stochastic Optimization Methods

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Stochastic Optimization Methods

Marti, Kurt

2005, XIII, 314 p., 14 illus., Hardcover

ISBN: 978-3-540-22272-9

About this book

Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Deterministic and stochastic approximation methods and their analytical properties are provided: Taylor expansion, regression and response surface methods, probability inequalities, First Order Reliability Methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation methods, differentiation of probability and mean value functions. Convergence results of the resulting iterative solution procedures are given.

Written for:

Researchers

Keywords:

Optimization

Optimization Problems

Stochastic Approximation

Stochastic Optimization

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关键词：Optimization Stochastic Stochast Springer Methods Methods Optimization Springer