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008 150903s2008 xxu| o |||| 0|eng d
020 _a9780387749327
_99780387749327
024 7 _a10.1007/9780387749327
_2doi
035 _avtls000332556
039 9 _a201509030231
_bVLOAD
_c201404122200
_dVLOAD
_c201404091931
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040 _aMX-SnUAN
_bspa
_cMX-SnUAN
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050 4 _aQA402.5-402.6
100 1 _aChinneck, John W.
_eautor
_9303713
245 1 0 _aFeasibility and Infeasibility in Optimization :
_bAlgorithms and Computational Methods /
_cby John W. Chinneck.
264 1 _aBoston, MA :
_bSpringer US,
_c2008.
300 _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 and Management Science,
_x0884-8289 ;
_v118
500 _aSpringer eBooks
505 0 _aSeeking Feasibility -- Preliminaries -- Seeking Feasibility in Linear Programs -- Seeking Feasibility in Mixed-Integer Linear Programs -- A Brief Tour of Constraint Programming -- Seeking Feasibility in Nonlinear Programs -- Analyzing Infeasibility -- Isolating Infeasibility -- Finding the Maximum Feasible Subset of Linear Constraints -- Altering Constraints to Achieve Feasibility -- Applications -- Other Model Analyses -- Data Analysis -- Miscellaneous Applications -- Epilogue.
520 _aConstrained optimization models are core tools in business, science, government, and the military with applications including airline scheduling, control of petroleum refining operations, investment decisions, and many others. Constrained optimization models have grown immensely in scale and complexity in recent years as inexpensive computing power has become widely available. Models now frequently have many complicated interacting constraints, giving rise to a host of issues related to feasibility and infeasibility. For example, it is sometimes difficult to find any feasible point at all for a large model, or even to accurately determine if one exists, e.g. for nonlinear models. If the model is feasible, how quickly can a solution be found? If the model is infeasible, how can the cause be isolated and diagnosed? Can a repair to restore feasibility be carried out automatically? Researchers have developed numerous algorithms and computational methods in recent years to address such issues, with a number of surprising spin-off applications in fields such as artificial intelligence and computational biology. Over the same time period, related approaches and techniques relating to feasibility and infeasibility of constrained problems have arisen in the constraint programming community. Feasibility and Infeasibility in Optimization is a timely expository book that summarizes the state of the art in both classical and recent algorithms related to feasibility and infeasibility in optimization, with a focus on practical methods. All model forms are covered, including linear, nonlinear, and mixed-integer programs. Connections to related work in constraint programming are shown. Part I of the book addresses algorithms for seeking feasibility quickly, including new methods for the difficult cases of nonlinear and mixed-integer programs. Part II provides algorithms for analyzing infeasibility by isolating minimal infeasible (or maximum feasible) subsets of constraints, or by finding the best repair for the infeasibility. Infeasibility analysis algorithms have arisen primarily over the last two decades, and the book covers these in depth and detail. Part III describes applications in numerous areas outside of direct infeasibility analysis such as finding decision trees for data classification, analyzing protein folding, radiation treatment planning, automated test assembly, etc. A main goal of the book is to impart an understanding of the methods so that practitioners can make immediate use of existing algorithms and software, and so that researchers can extend the state of the art and find new applications. The book is of interest to researchers, students, and practitioners across the applied sciences who are working on optimization problems.
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:
_z9780387749310
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-74932-7
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c279412
_d279412