00000cmm a22000004a 4500 UP-99796217611212380 Buklod 20250205113225.0 m go j cr |nu|||auu|a 250205s2011 nyu o j eng d 9781461402374 (eBook) 1461402379 (eBook) (iLib)UPD-00215704142 DMLR rda. eng DMLUC T 57.79 B57 2011eb Birge, John R. author. Introduction to stochastic programming [electronic resource] John R. Birge ; Franðcois Louveaux. Second edition. New York, New York Springer New York 2011. 1 online resource (xxv, 485 pages) illustrations. text rdacontent computer rdamedia online resource rdacarrier Springer series in operations research. Introduction 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. IP-based subscription, access limited to within on-campus computer network. Access via Electronic Resources of the UPD University Library Website. The 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). Electronic reproduction. New York SpringerLink 2011. Available via World Wide Web through SpringerLink. Stochastic programming. Louveaux, François author. SpringerLink (Online service). Electronic Resource Available for UP System via SpringerLink. https://doi.org/10.1007/978-1-4614-0237-4 FO UPD DMLR T 57.79 B57 2011eb Electronic Resource