Description

Book 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.



Table of Contents
1. Stochastic Linear Programming Models 2. Stochastic Linear Programming Algorithms 3. Implementation. The Testing Environment 4. Computational Results 5. Algorithmic Concepts in Convex Programming

Stochastic Linear Programming Algorithms: A

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    A Hardback by Janos Mayer

    £999.99

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      Book details

      Publisher Taylor & Francis
      Imprint Taylor & Francis Ltd
      Published 25 February 1998
      ISBN-13 9789056991449
      978-9056991449
      ISBN-10 9056991442
      Also in:
      Computer science

      Description

      Book 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.



      Table of Contents
      1. Stochastic Linear Programming Models 2. Stochastic Linear Programming Algorithms 3. Implementation. The Testing Environment 4. Computational Results 5. Algorithmic Concepts in Convex Programming

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