Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Engineering) 2nd Edition by John R. Birge (PDF)

Description

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)

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Typical Spinger book. Way too much text and too little illustration or examples. Meh….They really should bring their textbooks into the 21st century with color, illustrations, pictures, and even more digital tie-in. This is dry as cardboard, and math doesn’t have to be like that.

⭐If you’re trying to make the transition from LP to Stochastic LP, this book won’t help much.It doesn’t even try to make the material relatable or accessible to someone new to the field. It is very thin on examples. Much of the content isn’t explained at all. After a semester struggling with this book I ask what were the authors trying to accomplish?

⭐hard to follow: poor explanations, many steps in derivations are skipped, inconsistent notation, ambiguous sentences, strange English language use, etc.

⭐I had a positive expectative about the book before, but now I am secure that it was an excellent purchase. The book is clear and presents all contents as detailed as needed for both a new learner and an expert.

⭐Ce livre constitue une excellente introduction à l’optimisation stochastique. Malheureusement, bien que neuf, la couverture du livre que j’ai reçu était abîmée…

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