Probabilistic and Randomized Methods for Design under Uncertainty (Hardcover, 2006 ed.)

Part I 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 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 Stochastic 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 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 References