Cantitate/Preț
Produs

Robust Optimization-Directed Design: Nonconvex Optimization and Its Applications, cartea 81

Editat de Andrew J. Kurdila, Panos M. Pardalos, Michael Zabarankin
en Limba Engleză Hardback – 2 dec 2005
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.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 55974 lei  39-44 zile
  Springer Us – 19 noi 2014 55974 lei  39-44 zile
Hardback (1) 63306 lei  6-8 săpt.
  Springer Us – 2 dec 2005 63306 lei  6-8 săpt.

Din seria Nonconvex Optimization and Its Applications

Preț: 63306 lei

Preț vechi: 74477 lei
-15% Nou

Puncte Express: 950

Preț estimativ în valută:
12119 12597$ 10048£

Carte tipărită la comandă

Livrare economică 06-20 februarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9780387282633
ISBN-10: 0387282637
Pagini: 275
Ilustrații: IX, 275 p.
Dimensiuni: 156 x 234 x 18 mm
Greutate: 0.58 kg
Ediția:2006
Editura: Springer Us
Colecția Springer
Seria Nonconvex Optimization and Its Applications

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

A Multigrid Approach to Optimal Control Computations for Navier-Stokes Flows.- Control System Radii and Robustness Under Approximation.- Equilibrium Analysis for a Network Market Model.- Distributed Solution of Optimal Control Problems Governed by Parabolic Equations.- Modeling and Implementation of Risk-Averse Preferences in Stochastic Programs Using Risk Measures.- Shape Optimization of Electrodes for Piezoelectric Actuators.- Robust Static Super-Replication of Barrier Options in the Black-Scholes model.- Numerical Techniques in Relaxed Optimization Problems.- Combining Model and Test Data for Optimal Determination of Percentiles and Allowables: CVaR Regression Approach, Part I.- Combining Model and Test Data for Optimal Determination of Percentiles and Allowables: CVaR Regression Approach, Part II.- Semidefinite Programming for Sensor Network and Graph Localization.

Textul de pe ultima copertă

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
 

Caracteristici

Presents state-of-the-art research in uncertainity modeling, robust design, optimal control and stochastic optimization Includes supplementary material: sn.pub/extras