Author: Publisher:
ISBN: 9780444879912
Category :
Languages : en
Pages : 0
Book Description
Stochastic Programming 84
Author: Stochastic Programming 84
Author: András PrékopaPublisher: North Holland
ISBN:
Category : Mathematics
Languages : en
Pages : 208
Book Description
Stochastic Programming
Author: András PrékopaPublisher: Springer
ISBN: 9789048145522
Category : Mathematics
Languages : en
Pages : 0
Book Description
Stochastic programming - the science that provides us with tools to design and control stochastic systems with the aid of mathematical programming techniques - lies at the intersection of statistics and mathematical programming. The book Stochastic Programming is a comprehensive introduction to the field and its basic mathematical tools. While the mathematics is of a high level, the developed models offer powerful applications, as revealed by the large number of examples presented. The material ranges form basic linear programming to algorithmic solutions of sophisticated systems problems and applications in water resources and power systems, shipbuilding, inventory control, etc. Audience: Students and researchers who need to solve practical and theoretical problems in operations research, mathematics, statistics, engineering, economics, insurance, finance, biology and environmental protection.
Stochastic Programming 84. Pt. I.
Author: Stochastic Programming 84. Pt. II.
Author: Stochastic Programming
Author: Stochastic Programming 84
Author: András PrékopaPublisher:
ISBN: 9783642009273
Category : Computer science
Languages : en
Pages : 181
Book Description
Introduction to Stochastic Programming
Author: John R. BirgePublisher: Springer Science & Business Media
ISBN: 1461402379
Category : Business & Economics
Languages : en
Pages : 500
Book 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)
Author: 
Author: Andras Prékopa