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020 _a9781493937035
082 _a519.7
_bBIR
100 _aBirge, John R.
_912440
245 _aIntroduction to stochastic programming
250 _a2nd
260 _bSpringer
_aNew York
_c2011
300 _axxv, 485 p.
365 _aEURO
_b59.99
490 _aSpringer series in operations research and financial engineering
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.
650 _aStochastic programming
_912441
650 _aLinear programming
_9874
650 _aMathematical optimization
_9647
650 _aMathematical statistics
_9837
700 _aLouveaux, Francois
_912442
942 _2ddc
_cBK