Stochastic Linear Programming Algorithms
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A01=Janos Mayer
advanced stochastic programming methods
Author_Janos Mayer
Category=PBWL
Category=UM
Category=UYA
chance constraint analysis
computational experiments
convex programming
decomposition techniques
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GENSLP
jointly chance constrained problems
numerical algorithm comparison
QDECOM
reduced gradient approach
regularized outer approximation methods
SDECOM
stochastic linear programming algorithms
stochastic linear programming models
two stage optimisation
Product details
- ISBN 9789056991449
- Weight: 512g
- Dimensions: 191 x 254mm
- Publication Date: 25 Feb 1998
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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.
