Stochastic Optimization Methods - Kurt Marti
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Piegāde 12-18 darba dienu laikā
30 dienu atgriešanas politika
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 unc ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Kurt Marti |
|---|---|
| Izdevējs | Springer Berlin Heidelberg |
| Izlaides gads | 2010 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783642098369 |