000			03684nam a22004935i 4500
001			978-0-387-32698-6
003			DE-He213
005			20250710083949.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387326986 _a99780387326986
024	7		_a10.1007/0-387-32698-7 _2doi
082	0	4	_a519.6 _223
100	1		_aFiedler, M. _eauthor.
245	1	0	_aLinear Optimization Problems with Inexact Data _h[recurso electrónico] / _cby M. Fiedler, J. Nedoma, J. Ramík, J. Rohn, K. Zimmermann.
264		1	_aBoston, MA : _bSpringer US, _c2006.
300			_aXV, 214 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aMatrices -- Solvability of systems of interval linear equations and inequalities -- Interval linear programming -- Linear programming with set coefficients -- Fuzzy linear optimization -- Interval linear systems and optimization problems over max-algebras.
520			_aLinear programming attracted the interest of mathematicians during and after World War II when the first computers were constructed and methods for solving large linear programming problems were sought in connection with specific practical problems-for example, providing logistical support for the U.S. Armed Forces or modeling national economies. Early attempts to apply linear programming methods to solve practical problems failed to satisfy expectations. There were various reasons for the failure. One of them, which is the central topic of this book, was the inexactness of the data used to create the models. This phenomenon, inherent in most pratical problems, has been dealt with in several ways. At first, linear programming models used "average" values of inherently vague coefficients, but the optimal solutions of these models were not always optimal for the original problem itself. Later researchers developed the stochastic linear programming approach, but this too has its limitations. Recently, interest has been given to linear programming problems with data given as intervals, convex sets and/or fuzzy sets. The individual results of these studies have been promising, but the literature has not presented a unified theory. Linear Optimization Problems with Inexact Data attempts to present a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework. Audience This book is intended for postgraduate or graduate students in the areas of operations research, optimization theory, linear algebra, interval analysis, reliable computing, and fuzzy sets. The book will also be useful for researchers in these respective areas.
650		0	_aMATHEMATICS.
650		0	_aMATRIX THEORY.
650		0	_aMATHEMATICAL OPTIMIZATION.
650		0	_aOPERATIONS RESEARCH.
650	1	4	_aMATHEMATICS.
650	2	4	_aOPTIMIZATION.
650	2	4	_aLINEAR AND MULTILINEAR ALGEBRAS, MATRIX THEORY.
650	2	4	_aGAME THEORY, ECONOMICS, SOCIAL AND BEHAV. SCIENCES.
650	2	4	_aOPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING.
700	1		_aNedoma, J. _eauthor.
700	1		_aRamík, J. _eauthor.
700	1		_aRohn, J. _eauthor.
700	1		_aZimmermann, K. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387326979
856	4	0	_uhttp://dx.doi.org/10.1007/0-387-32698-7 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c57195 _d57195