Stochastic Optimization Methods

Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, appropriate deterministic substitute problems are needed. Based on the probability distri...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Marti, Kurt (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Marti, Kurt.  |e author. 
245 1 0 |a Stochastic Optimization Methods  |h [electronic resource] /  |c by Kurt Marti. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2008. 
300 |a XIII, 340 p.  |b online resource. 
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505 0 |a Basic Stochastic Optimization Methods -- Decision/Control Under Stochastic Uncertainty -- Deterministic Substitute Problems in Optimal Decision Under Stochastic Uncertainty -- Differentiation Methods -- Differentiation Methods for Probability and Risk Functions -- Deterministic Descent Directions -- Deterministic Descent Directions and Efficient Points -- Semi-Stochastic Approximation Methods -- RSM-Based Stochastic Gradient Procedures -- Stochastic Approximation Methods with Changing Error Variances -- Reliability Analysis of Structures/Systems -- Computation of Probabilities of Survival/Failure by Means of Piecewise Linearization of the State Function. 
520 |a Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive 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. 
650 0 |a Business. 
650 0 |a Operations research. 
650 0 |a Decision making. 
650 0 |a Mathematical optimization. 
650 0 |a Engineering. 
650 0 |a Computational intelligence. 
650 1 4 |a Business and Management. 
650 2 4 |a Operation Research/Decision Theory. 
650 2 4 |a Optimization. 
650 2 4 |a Engineering, general. 
650 2 4 |a Computational Intelligence. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783540794578 
856 4 0 |u http://dx.doi.org/10.1007/978-3-540-79458-5  |z Full Text via HEAL-Link 
912 |a ZDB-2-SBE 
950 |a Business and Economics (Springer-11643)