000 03679nam a22003735i 4500
001 285772
003 MX-SnUAN
005 20160429154422.0
007 cr nn 008mamaa
008 150903s2010 xxu| o |||| 0|eng d
020 _a9781441912916
_99781441912916
024 7 _a10.1007/9781441912916
_2doi
035 _avtls000338255
039 9 _a201509030323
_bVLOAD
_c201404300342
_dVLOAD
_y201402060906
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aHD30.23
100 1 _aMurty, Katta G.
_eautor
_9314122
245 1 0 _aOptimization for Decision Making :
_bLinear and Quadratic Models /
_cby Katta G. Murty.
264 1 _aBoston, MA :
_bSpringer US :
_bImprint: Springer,
_c2010.
300 _axxvI, 482 páginas 47 ilustraciones
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aInternational Series in Operations Research & Management Science,
_x0884-8289 ;
_v137
500 _aSpringer eBooks
505 0 _aLinear Equations, Inequalities, Linear Programming: A Brief Historical Overview -- Formulation Techniques Involving Transformations of Variables -- Intelligent Modeling Essential to Get Good Results -- Polyhedral Geometry -- Duality Theory and Optimality Conditions for LPs -- Revised Simplex Variants of the Primal and Dual Simplex Methods and Sensitivity Analysis -- Interior Point Methods for LP -- Sphere Methods for LP -- Quadratic Programming Models.
520 _aOptimization for Decision Making: Linear and Quadratic Models is a first-year graduate level text that illustrates how to formulate real world problems using linear and quadratic models; how to use efficient algorithms – both old and new – for solving these models; and how to draw useful conclusions and derive useful planning information from the output of these algorithms. While almost all the best known books on LP are essentially mathematics books with only very simple modeling examples, this book emphasizes the intelligent modeling of real world problems, and the author presents several illustrative examples and includes many exercises from a variety of application areas. Additionally, where other books on LP only discuss the simplex method, and perhaps existing interior point methods, this book also discusses a new method based on using the sphere which uses matrix inversion operations sparingly and may be well suited to solving large-scale LPs, as well as those that may not have the property of being very sparse. Individual chapters present a brief history of mathematical modeling; methods for formulating real world problems; three case studies that illustrate the need for intelligent modeling; classical theory of polyhedral geometry that plays an important part in the study of LP; duality theory, optimality conditions for LP, and marginal analysis; variants of the revised simplex method; interior point methods; sphere methods; and extensions of sphere method to convex and nonconvex quadratic programs and to 0-1 integer programs through quadratic formulations. End of chapter exercises are provided throughout, with additional exercises available online.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781441912909
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4419-1291-6
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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