Stochastic Linear Programming Algorithms (Hardcover)

Janos Mayer

ISBN 10: 9056991442 / ISBN 13: 9789056991449

Published by Taylor & Francis Ltd, London, 1998

About this Item

Hardcover. A computationally oriented comparison of solution algorithms for two stage and jointly chance constrained stochastic linear programming problems, this is the first book to present comparative computational results with several major stochastic programming solution approaches.The following methods are considered: regularized decomposition, stochastic decomposition and successive discrete approximation methods for two stage problems; cutting plane methods, and a reduced gradient method for jointly chance constrained problems.The first part of the book introduces the algorithms, including a unified approach to decomposition methods and their regularized counterparts. The second part addresses computer implementation of the methods, describes a testing environment based on a model management system, and presents comparative computational results with the various algorithms. Emphasis is on the computational behavior of the algorithms. A computationally oriented comparison of solution algorithms for two stage and jointly chance constrained stochastic linear programming problems, this resource presents comparative computational results with several major stochastic programming solution approaches. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9789056991449

Bibliographic Details

• Title: Stochastic Linear Programming Algorithms (Hardcover)
• Publisher: Taylor & Francis Ltd, London
• Publication Date: 1998
• Language: English
• ISBN 10: 9056991442
• ISBN 13: 9789056991449
• Binding: Hardcover
• Edition: 1st Edition
• Condition: new

About this title

Synopsis

A computationally oriented comparison of solution algorithms for two stage and jointly chance constrained stochastic linear programming problems, this is the first book to present comparative computational results with several major stochastic programming solution approaches. The following methods are considered: regularized decomposition, stochastic decomposition and successive discrete approximation methods for two stage problems; cutting plane methods, and a reduced gradient method for jointly chance constrained problems. The first part of the book introduces the algorithms, including a unified approach to decomposition methods and their regularized counterparts. The second part addresses computer implementation of the methods, describes a testing environment based on a model management system, and presents comparative computational results with the various algorithms. Emphasis is on the computational behavior of the algorithms.

About the Author

Janos Mayer