Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insenistive with respect to random parameter variations, appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures, differentiation formulas for probabilities and expectations.
Kurt Marti
Stochastic Optimization Methods [PDF ebook]
Stochastic Optimization Methods [PDF ebook]
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语言 英语 ● 格式 PDF ● 网页 340 ● ISBN 9783540794585 ● 文件大小 5.3 MB ● 出版者 Springer Berlin ● 市 Heidelberg ● 国家 DE ● 发布时间 2008 ● 版 2 ● 下载 24 个月 ● 货币 EUR ● ID 2234261 ● 复制保护 社会DRM