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.
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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.
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multigrid approach, relaxed optimization problems, sensor network localization, network market model, mean absolute deviation, regression module, error perspective plot, sensor network localization problem, optimal electrode shapes, failure load distribution, experimental test information, graph localization, parabolic optimal control problems, supplied dataset, hedge error, system radii, static hedge portfolio, volatility interval, test point data, robust optimization problem, coherent risk measures, integrating various sources, scale biases, stochastic programming problems, test data points Key Phrases - Capitalized Phrases (CAPs): (learn more)
Alexandre Trindade, New York, Robust Static Super-Replication of Barrier Options, Numerical Techniques, Yunfei Feng, Weijian Wang, Distributed Solution of Optimal Control Problems, Conditional Value-at-Risk, Monte Carlo, Optimal Control Computations, Lecture Notes, University of Florida, Operations Research, Control Opt, Black Scholes, Summary Remarks, Kluwer Academic Publishers, Technical Report, Research Report, Rice University, Princeton University Press, American Optimal Decisions, Journal of Risk, Power Systems, Control Letters Browse Sample Pages:
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- Domain Decomposition Methods by Andrea Toselli on page 93, and page 94
- Quantile Regression (Econometric Society Monographs) by Roger Koenker on page 201, and page 202
- Handbooks in Operations Research and Management Science: Stochastic Programming (Hanbooks in Operations Research and Management Series) by A. Ruszczynski on page 97, and page 116
- Variational Analysis (Grundlehren der mathematischen Wissenschaften) by R. Tyrrell Rockafellar on page 68, and page 72
- Probabilistic Constrained Optimization: Methodology and Applications (Nonconvex Optimization and Its Applications (closed)) by S. Uryasev on page 116, and page 207
See all 40 books this book cites
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