Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Engineering)

Birge, John R. (Author), Loveaux, François (Author)

ISBN 10: 0387982175 / ISBN 13: 9780387982175

Published by Springer, 1997

About this Item

Book has mild rubbing to covers with sunfading to spine area and top edges, a reading crease along spine, light wear to edges and corners, previous owner's name to front endpaper and outer textblock, some highlighting and writing to text. in good condition due to markings to pages; a solidly-bound reading/reference copy with bright pages. Seller Inventory # 1000648

Bibliographic Details

Title: Introduction to Stochastic Programming (...

Publisher: Springer

Publication Date: 1997

Binding: Hardcover

Condition: Good

About this title

Synopsis:

This rapidly developing field encompasses 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 present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for 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 early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject.

From the Publisher:

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. 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.