Stochastic Linear Programming Algorithms : A Comparison Based on a Model Management System
by
Janos Mayer
Book Details
Format
Hardback or Cased Book
Book Series
Optimization Theory and Applications
ISBN-10
9056991442
ISBN-13
9789056991449
Publisher
Taylor & Francis Ltd
Imprint
Taylor & Francis Ltd
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Feb 25th, 1998
Print length
163 Pages
Weight
522 grams
Product Classification:
StochasticsComputer programming / software developmentMathematical theory of computation
Ksh 16,550.00
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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.
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.
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.
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