| 书目名称 | Stochastic Optimization Methods | | 副标题 | Applications in Engi | | 编辑 | Kurt Marti | | 视频video | | | 概述 | Features optimization problems that in practice involve random model parameters.Provides applications from the fields of robust optimal control / design in case of stochastic uncertainty.Contains nume | | 图书封面 |  | | 描述 | .This book examines optimization problems that in practice involve random model parameters. It outlines the computation of robust optimal solutions, i.e., optimal solutions that are insensitive to random parameter variations, where 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 corresponding deterministic problems..Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. 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, and differentiation formulas for probabilities and expectations..The fourth edition of this classic text has been carefully and thoroughly revised. It includes new chapters on the solution of stochastic linear prog | | 出版日期 | Book 2024Latest edition | | 关键词 | calculus; model; optimization problems; regression; response surface methodology; stochastic approximatio | | 版次 | 4 | | doi | https://doi.org/10.1007/978-3-031-40059-9 | | isbn_softcover | 978-3-031-40061-2 | | isbn_ebook | 978-3-031-40059-9 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 19:35:51
