Ebook: Robust Optimization-Directed Design

Robust design—that is, managing design uncertainties such as model uncertainty or parametric uncertainty—is the often unpleasant issue crucial in much multidisciplinary optimal design work. Recently, there has been enormous practical interest in strategies for applying optimization tools to the development of robust solutions and designs in several areas, including aerodynamics, the integration of sensing (e.g., laser radars, vision-based systems, and millimeter-wave radars) and control, cooperative control with poorly modeled uncertainty, cascading failures in military and civilian applications, multi-mode seekers/sensor fusion, and data association problems and tracking systems. The contributions to this book explore these different strategies. The expression "optimization-directed” in this book’s title is meant to suggest that the focus is not agonizing over whether optimization strategies identify a true global optimum, but rather whether these strategies make significant design improvements.

Audience

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Robust design—that is, managing design uncertainties such as model uncertainty or parametric uncertainty—is the often unpleasant issue crucial in much multidisciplinary optimal design work. Recently, there has been enormous practical interest in strategies for applying optimization tools to the development of robust solutions and designs in several areas, including aerodynamics, the integration of sensing (e.g., laser radars, vision-based systems, and millimeter-wave radars) and control, cooperative control with poorly modeled uncertainty, cascading failures in military and civilian applications, multi-mode seekers/sensor fusion, and data association problems and tracking systems. The contributions to this book explore these different strategies. The expression "optimization-directed” in this book’s title is meant to suggest that the focus is not agonizing over whether optimization strategies identify a true global optimum, but rather whether these strategies make significant design improvements.

 

Audience

 

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Robust design—that is, managing design uncertainties such as model uncertainty or parametric uncertainty—is the often unpleasant issue crucial in much multidisciplinary optimal design work. Recently, there has been enormous practical interest in strategies for applying optimization tools to the development of robust solutions and designs in several areas, including aerodynamics, the integration of sensing (e.g., laser radars, vision-based systems, and millimeter-wave radars) and control, cooperative control with poorly modeled uncertainty, cascading failures in military and civilian applications, multi-mode seekers/sensor fusion, and data association problems and tracking systems. The contributions to this book explore these different strategies. The expression "optimization-directed” in this book’s title is meant to suggest that the focus is not agonizing over whether optimization strategies identify a true global optimum, but rather whether these strategies make significant design improvements.

 

Audience

 

Content:
Front Matter....Pages i-ix
A Multigrid Approach to Optimal Control Computations for Navier-Stokes Flows....Pages 3-23
Control System Radii and Robustness Under Approximation....Pages 25-61
Equilibrium Analysis for a Network Market Model....Pages 63-72
Distributed Solution of Optimal Control Problems Governed by Parabolic Equations....Pages 73-94
Modeling and Implementation of Risk-Averse Preferences in Stochastic Programs Using Risk Measures....Pages 95-116
Shape Optimization of Electrodes for Piezoelectric Actuators....Pages 117-134
Robust Static Super-Replication of Barrier Options in the Black-Scholes model....Pages 135-155
Numerical Techniques in Relaxed Optimization Problems....Pages 157-178
Combining Model and Test Data for Optimal Determination of Percentiles and Allowables: CVaR Regression Approach, Part I....Pages 179-207
Combining Model and Test Data for Optimal Determination of Percentiles and Allowables: CVaR Regression Approach, Part II....Pages 209-246
Semidefinite Programming for Sensor Network and Graph Localization....Pages 247-275

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Robust design—that is, managing design uncertainties such as model uncertainty or parametric uncertainty—is the often unpleasant issue crucial in much multidisciplinary optimal design work. Recently, there has been enormous practical interest in strategies for applying optimization tools to the development of robust solutions and designs in several areas, including aerodynamics, the integration of sensing (e.g., laser radars, vision-based systems, and millimeter-wave radars) and control, cooperative control with poorly modeled uncertainty, cascading failures in military and civilian applications, multi-mode seekers/sensor fusion, and data association problems and tracking systems. The contributions to this book explore these different strategies. The expression "optimization-directed” in this book’s title is meant to suggest that the focus is not agonizing over whether optimization strategies identify a true global optimum, but rather whether these strategies make significant design improvements.

 

Audience

 

Content:
Front Matter....Pages i-ix
A Multigrid Approach to Optimal Control Computations for Navier-Stokes Flows....Pages 3-23
Control System Radii and Robustness Under Approximation....Pages 25-61
Equilibrium Analysis for a Network Market Model....Pages 63-72
Distributed Solution of Optimal Control Problems Governed by Parabolic Equations....Pages 73-94
Modeling and Implementation of Risk-Averse Preferences in Stochastic Programs Using Risk Measures....Pages 95-116
Shape Optimization of Electrodes for Piezoelectric Actuators....Pages 117-134
Robust Static Super-Replication of Barrier Options in the Black-Scholes model....Pages 135-155
Numerical Techniques in Relaxed Optimization Problems....Pages 157-178
Combining Model and Test Data for Optimal Determination of Percentiles and Allowables: CVaR Regression Approach, Part I....Pages 179-207
Combining Model and Test Data for Optimal Determination of Percentiles and Allowables: CVaR Regression Approach, Part II....Pages 209-246
Semidefinite Programming for Sensor Network and Graph Localization....Pages 247-275
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