| 000 | 05356nam a22005895i 4500 | ||
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| 001 | 978-0-387-74932-7 | ||
| 003 | DE-He213 | ||
| 005 | 20161121231208.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2008 xxu| s |||| 0|eng d | ||
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_a9780387749327 _9978-0-387-74932-7 |
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| 024 | 7 |
_a10.1007/978-0-387-74932-7 _2doi |
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| 050 | 4 | _aQA402.5-402.6 | |
| 072 | 7 |
_aPBU _2bicssc |
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| 072 | 7 |
_aMAT003000 _2bisacsh |
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| 082 | 0 | 4 |
_a519.6 _223 |
| 100 | 1 |
_aChinneck, John W. _eauthor. |
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| 245 | 1 | 0 |
_aFeasibility and Infeasibility in Optimization _h[electronic resource] : _bAlgorithms and Computational Methods / _cby John W. Chinneck. |
| 264 | 1 |
_aBoston, MA : _bSpringer US, _c2008. |
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| 300 |
_aXXII, 274 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aInternational Series in Operations Research and Management Science, _x0884-8289 ; _v118 |
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| 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. | ||
| 650 | 0 | _aMathematics. | |
| 650 | 0 | _aOperations research. | |
| 650 | 0 | _aDecision making. | |
| 650 | 0 | _aMathematical models. | |
| 650 | 0 | _aMathematical optimization. | |
| 650 | 0 | _aIndustrial engineering. | |
| 650 | 0 | _aProduction engineering. | |
| 650 | 0 | _aEngineering economics. | |
| 650 | 0 | _aEngineering economy. | |
| 650 | 0 | _aEconometrics. | |
| 650 | 1 | 4 | _aMathematics. |
| 650 | 2 | 4 | _aOptimization. |
| 650 | 2 | 4 | _aOperation Research/Decision Theory. |
| 650 | 2 | 4 | _aMathematical Modeling and Industrial Mathematics. |
| 650 | 2 | 4 | _aEngineering Economics, Organization, Logistics, Marketing. |
| 650 | 2 | 4 | _aIndustrial and Production Engineering. |
| 650 | 2 | 4 | _aEconometrics. |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9780387749310 |
| 830 | 0 |
_aInternational Series in Operations Research and Management Science, _x0884-8289 ; _v118 |
|
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-0-387-74932-7 |
| 912 | _aZDB-2-SMA | ||
| 950 | _aMathematics and Statistics (Springer-11649) | ||
| 999 |
_c509960 _d509960 |
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