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Stochastic Linear Programming Algorithms: A Comparison Based on a Model Management System

Janos Mayer



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Taylor & Francis Ltd
25 February 1998
Stochastics; Computer programming & software development; Mathematical theory of computation
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.
By:   Janos Mayer
Imprint:   Taylor & Francis Ltd
Country of Publication:   United Kingdom
Volume:   v.1
Dimensions:   Height: 254mm,  Width: 191mm,  Spine: 16mm
Weight:   517g
ISBN:   9789056991449
ISBN 10:   9056991442
Series:   Optimization Theory & Applications
Pages:   163
Publication Date:   25 February 1998
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

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