000 03668nam a22005175i 4500
001 978-0-387-28654-9
003 DE-He213
005 20161121231024.0
007 cr nn 008mamaa
008 100301s2006 xxu| s |||| 0|eng d
020 _a9780387286549
_9978-0-387-28654-9
024 7 _a10.1007/0-387-28654-3
_2doi
050 4 _aQA402.5-402.6
072 7 _aPBU
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519.6
_223
245 1 0 _aRobust Optimization-Directed Design
_h[electronic resource] /
_cedited by Andrew J. Kurdila, Panos M. Pardalos, Michael Zabarankin.
264 1 _aBoston, MA :
_bSpringer US,
_c2006.
300 _aIX, 275 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aNonconvex Optimization and Its Applications,
_x1571-568X ;
_v81
505 0 _aA 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.
520 _aRobust 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 .
650 0 _aMathematics.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 0 _aSystem theory.
650 0 _aMathematical optimization.
650 1 4 _aMathematics.
650 2 4 _aOptimization.
650 2 4 _aApplications of Mathematics.
650 2 4 _aSystems Theory, Control.
700 1 _aKurdila, Andrew J.
_eeditor.
700 1 _aPardalos, Panos M.
_eeditor.
700 1 _aZabarankin, Michael.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387282633
830 0 _aNonconvex Optimization and Its Applications,
_x1571-568X ;
_v81
856 4 0 _uhttp://dx.doi.org/10.1007/0-387-28654-3
912 _aZDB-2-SMA
999 _c507410
_d507410