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Stochastic optimization methods
Annotation 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 probabi...
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Main Author: | |
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Corporate Author: | |
Format: | eBook |
Language: | English |
Published: |
Berlin ; London :
Springer,
©2008.
Berlin ; London : [2008] |
Edition: | 2nd ed. |
Physical Description: |
1 online resource (xiii, 340 pages) : illustrations. |
Subjects: | |
Online Access: | SpringerLink - Click here for access |
Contents:
- pt. I. Basic Stochastic Optimization Methods
- 1. Decision/Control Under Stochastic Uncertainty
- 2. Deterministic Substitute Problems in Optimal Decision Under Stochastic Uncertainty
- pt. II. Differentiation Methods
- 3. Differentiation Methods for Probability and Risk Functions
- pt. III. Deterministic Descent Directions
- 4. Deterministic Descent Directions and Efficient Points
- pt. IV. Semi-Stochastic Approximation Methods
- 5. RSM-Based Stochastic Gradient Procedures
- 6. Stochastic Approximation Methods with Changing Error Variances
- pt. V. Reliability Analysis of Structures/Systems
- 7. Computation of Probabilities of Survival/Failure by Means of Piecewise Linearization of the State Function
- pt. VI. Appendix
- A. Sequences, Series and Products
- B. Convergence Theorems for Stochastic Sequences
- C. Tools from Matrix Calculus.