Stochastic Optimization Methods
Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data, and...
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| Format: | eBook |
| Language: | English |
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Berlin, Heidelberg
Springer Berlin Heidelberg
2005, 2005
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| Edition: | 1st ed. 2005 |
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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
- Technical Applications
- Approximation of the Probability of Failure/Survival in Plastic Structural Analysis and Optimal Plastic Design