[share_ebook] Stochastic Optimization Methods


Author: Kurt Marti

Date: 2011-06-18

Pages: 353

Publisher: Springer

Category: Business


Posted on 2011-10-18, by sharebookfree.

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Stochastic Optimization Methods


Stochastic Optimization Methods
Kurt Marti | Springer | 2011-06-18 | 353 pages | English | PDF

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.


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