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...
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| Format: | eBook |
| Language: | English |
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Berlin, Heidelberg
Springer Berlin Heidelberg
2008, 2008
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| Edition: | 2nd ed. 2008 |
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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