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Stochastic Optimization Methods

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 stoc...

Hardcover: 340 pages
Publisher: Springer; 2nd ed. 2008 edition (June 5, 2008)
Language: English
ISBN-10: 3540794573
ISBN-13: 978-3540794578
Product Dimensions: 6.1 x 0.8 x 9.2 inches
Amazon Rank: 9068009
Format: PDF ePub djvu ebook

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astic 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.