Probabilistic and Randomized Methods for Design under Uncertainty
In many engineering design and optimization problems, the presence of uncertainty in the data is a central and critical issue. Different fields of engineering use different ways to describe this uncertainty and adopt a variety of techniques to devise designs that are at least partly insensitive or r...
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| Outros Autores: | , |
| Formato: | Recurso Electrónico livro electrónico |
| Idioma: | English |
| Publicado em: |
London :
Springer London : Imprint: Springer,
2006.
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| Edição: | 1st ed. 2006. |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1007/b138725 |
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Sumário:
- Chance-Constrained and Stochastic Optimization
- Scenario Approximations of Chance Constraints
- Optimization Models with Probabilistic Constraints
- Theoretical Framework for Comparing Several Stochastic Optimization Approaches
- Optimization of Risk Measures
- Robust Optimization and Random Sampling
- Sampled Convex Programs and Probabilistically Robust Design
- Tetris: A Study of Randomized Constraint Sampling
- Near Optimal Solutions to Least-Squares Problems with Stochastic Uncertainty
- The Randomized Ellipsoid Algorithm for Constrained Robust Least Squares Problems
- Randomized Algorithms for Semi-Infinite Programming Problems
- Probabilistic Methods in Identification and Control
- A Learning Theory Approach to System Identification and Stochastic Adaptive Control
- Probabilistic Design of a Robust Controller Using a Parameter-Dependent Lyapunov Function
- Probabilistic Robust Controller Design: Probable Near Minimax Value and Randomized Algorithms
- Sampling Random Transfer Functions
- Nonlinear Systems Stability via Random and Quasi-Random Methods
- Probabilistic Control of Nonlinear Uncertain Systems
- Fast Randomized Algorithms for Probabilistic Robustness Analysis.