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001 978-1-4614-0237-4
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008 110614s2011 xxu| s |||| 0|eng d
020 _a9781461402374
_9978-1-4614-0237-4
024 7 _a10.1007/978-1-4614-0237-4
_2doi
050 4 _aQA402-402.37
050 4 _aT57.6-57.97
072 7 _aKJT
_2bicssc
072 7 _aKJM
_2bicssc
072 7 _aBUS049000
_2bisacsh
072 7 _aBUS042000
_2bisacsh
082 0 4 _a519.6
_223
100 1 _aBirge, John R.
_eauthor.
245 1 0 _aIntroduction to Stochastic Programming
_h[electronic resource] /
_cby John R. Birge, François Louveaux.
264 1 _aNew York, NY :
_bSpringer New York,
_c2011.
300 _aXXV, 485 p. 44 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Operations Research and Financial Engineering,
_x1431-8598
505 0 _aIntroduction and Examples -- Uncertainty and Modeling Issues -- Basic Properties and Theory -- The Value of Information and the Stochastic Solution -- Two-Stage Recourse Problems -- Multistage Stochastic Programs -- Stochastic Integer Programs -- Evaluating and Approximating Expectations -- Monte Carlo Methods -- Multistage Approximations -- Sample Distribution Functions -- References.
520 _aThe aim of stochastic programming is to find optimal decisions in problems  which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods. The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition: "The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998)     
650 0 _aMathematics.
650 0 _aMathematical optimization.
650 0 _aMathematical statistics.
650 1 4 _aMathematics.
650 2 4 _aOperations Research, Management Science.
650 2 4 _aStatistics and Computing/Statistics Programs.
650 2 4 _aOptimization.
700 1 _aLouveaux, François.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461402367
830 0 _aSpringer Series in Operations Research and Financial Engineering,
_x1431-8598
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-0237-4
912 _aZDB-2-SMA
999 _c106229
_d106229