Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Enginee
By addebook • Jul 3rd, 2008 • Category: Mathematics •
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Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Engineering)
Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Engineering)
By John R. Birge, Francois Louveaux
Publisher: Springer
Number Of Pages: 448
Publication Date: 2000-02-02
ISBN-10 / ASIN: 0387982175
ISBN-13 / EAN: 9780387982175
Binding: Hardcover
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. Conversely, it is 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. The first chapters introduce some worked examples of stochastic programming and demonstrate how a stochastic model is formally built. Subsequent chapters develop the properties of stochastic programs and the basic solution techniques used to solve them. Three chapters cover approximation and sampling techniques and the final chapter presents a case study in depth. A wide range of students from operations research, industrial engineering, and related disciplines will find this a well-paced and wide-ranging introduction to this subject.
Summary: Formalism doesn’t equal good introduction.
Rating: 2
Given that there are not many books in the area of stochastic programming Birge et al have written a book that will be a necessary reference for the time being. The first third of the book does provide a good introduction to the basics of SP but after that a level of formalism dominates that makes one wonder if she is reading from an arcane optimization journal. The later two thirds of the book is really nothing more than an amalgam of results pulled from the literature (journals). As such, little motivation is provided for the major results that are for the most part just juxtaposed on after another. One wonders why such a journalistic style would be used for an introductory text. After all the subject should not be presented as a springer-verlag MATH text in a field like algebraic topology where a theorem-proof format is legimate. Thus, until a better introductory text comes along that blends more of the practical engineering aspects with the theory we must be content with the current state of the art.
Summary: A must own guide to Stochastic Programming
Rating: 5
Introduction to Stochastic Programming is a must own book for anyone working in OR, IE, MS, etc. As stochasticity becomes more and more important in the field, this book becomes increasingly valuable. “Introduction” is a bit of a stretch. It starts from ground zero of Stochastic Programming, but is very heavy on the math. If you aren’t solid with your LP and probability, then a brush up is definately in order. This book is not for the faint of heart. Nevertheless, Birge and Louveaux do an OUTSTANDING job. The examples are clear, easy to follow (assuming you’re not math phobic) and very relevant. They go through different formulations of stochastic programms (recourse, chance constrained, etc.). The book discusses formulation, algorithms, and applications. There are not many books out there on Stochastic Programming…and this is really the only one you need to own.
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