Ebook Download Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Engineering)
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Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Engineering)
Ebook Download Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Engineering)
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Review
From the reviews of the second edition:“Help the students to understand how to model uncertainty into mathematical optimization problems, what uncertainty brings to the decision process and which techniques help to manage uncertainty in solving the problems. … certainly attract also the wide spectrum of readers whose main interest lies in possible exploitation of stochastic programming methodology and will help them to find their own way to treat actual problems using stochastic programming methods. As a whole, the three main building blocks of stochastic programming … are well represented and balanced.†(Jitka DupaÄová, Zentralblatt MATH, Vol. 1223, 2011)
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From the Back Cover
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)  Â
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Product details
Series: Springer Series in Operations Research and Financial Engineering
Hardcover: 485 pages
Publisher: Springer; 2nd ed. 2011 edition (June 27, 2011)
Language: English
ISBN-10: 1461402360
ISBN-13: 978-1461402367
Product Dimensions:
7 x 1.1 x 10 inches
Shipping Weight: 2.4 pounds (View shipping rates and policies)
Average Customer Review:
2.0 out of 5 stars
3 customer reviews
Amazon Best Sellers Rank:
#382,713 in Books (See Top 100 in Books)
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?
The author is certainly well recognized in the field. However, I found the book a bit difficult to read. I felt the author could have described things in greater detail and depth. That is, he seemingly left a lot for the reader to infer and derive for himself.
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.
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