000			03578nam a22004815i 4500
001			978-0-387-28654-9
003			DE-He213
005			20260521091848.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387286549
020			_a99780387286549
024	7		_a10.1007/0-387-28654-3 _2doi
040			_cCICY
082	0	4	_a519.6 _223
100	1		_aKurdila, Andrew J. _eeditor.
245	1	0	_aRobust Optimization-Directed Design _h[recurso electrónico] / _cedited by Andrew J. Kurdila, Panos M. Pardalos, Michael Zabarankin.
264		1	_aBoston, MA : _bSpringer US, _c2006.
300			_aVIII, 272 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _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	_aSYSTEMS 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		_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 _zVer el texto completo en las instalaciones del CICY
942			_2ddc _cER
999			_c32775 _d32775