Probabilistic and Randomized Methods for Design Under Uncertainty

By: Giuseppe Calafiore (Editor), Fabrizio Dabbene (Editor)

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Hardcover | 2 December 2005

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472 Pages

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23.5 x 15.88 x 3.18

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In many engineering design and optimization problems, the presence of uncertainty in the data is a critical issue. There are different ways to describe this uncertainty and to devise designs that are partly insensitive or robust to it.

This book examines uncertain systems in control engineering and general decision or optimization problems for which data is uncertain. Written by leading researchers in optimization and robust control; it highlights the interactions between these two fields.

Part I describes theory and solution methods for probability-constrained and stochastic optimization problems;
Part II focuses on numerical methods for solving randomly perturbed convex programs and semi-infinite optimization problems by probabilistic techniques;
Part III details the theory and applications of randomized techniques to the analysis and design of robust control systems.

It will interest researchers, academics and postgraduates in control engineering and operations research as well as professionals working in operations research.

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Published: 14th March 2012

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List of Contributors	p. xi
Chance-Constrained and Stochastic Optimization
Scenario Approximations of Chance Constraints	p. 3
Optimization Models with Probabilistic Constraints	p. 49
Theoretical Framework for Comparing Several Stochastic Optimization Approaches	p. 99
Optimization of Risk Measures	p. 119
Robust Optimization and Random Sampling
Sampled Convex Programs and Probabilistically Robust Design	p. 161
Tetris: A Study of Randomized Constraint Sampling	p. 189
Near Optimal Solutions to Least-Squares Problems with Stochastic Uncertainty	p. 203
The Randomized Ellipsoid Algorithm for Constrained Robust Least Squares Problems	p. 223
Randomized Algorithms for Semi-Infinite Programming Problems	p. 243
Probabilistic Methods in Identification and Control
A Learning Theory Approach to System Identification and Stochastic Adaptive Control	p. 265
Probabilistic Design of a Robust Controller Using a Parameter-Dependent Lyapunov Function	p. 303
Probabilistic Robust Controller Design: Probable Near Minimax Value and Randomized Algorithms	p. 317
Sampling Random Transfer Functions Constantino
Nonlinear Systems Stability via Random and QuasiRandom Methods	p. 365
Probabilistic Control of Nonlinear Uncertain Systems	p. 381
Fast Randomized Algorithms for Probabilistic Robustness Analysis	p. 415
References	p. 433
Index	p. 455
Table of Contents provided by Publisher. All Rights Reserved.

Probabilistic and Randomized Methods for Design Under Uncertainty

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