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 robust to uncertainty.\ Probabilistic and Randomized Methods for Design under Uncertainty examines uncertain systems in control engineering and general decision or optimization problems for which...
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Part I Chance-Constrained and Shastic Optimization\ Scenario Approximations of Chance Constraints\ Optimization Models with Probabilistic Constraints\ Theoretical Framework for Comparing Several Shastic Optimization Approaches\ Optimization of Risk Measures\ Part II 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 Shastic Uncertainty\ The Randomized Ellipsoid Algorithm for Constrained Robust Least Squares Problems\ Randomized Algorithms for Semi-Infnite Programming Problems\ Part III Probabilistic Methods in Identifcation and Control\ A Learning Theory Approach to System Identifcation and Shastic 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\ References
