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Stochastic Linear Programming Algorithms

A Comparison Based on a Model Management System

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.

This section is devoted to solution approaches in stochastic linear programming.
We do not intend to give a complete survey. Our aim is to summarize some of the
main approaches with a detailed presentation only for those methods, which ...

Stochastic Linear Programming

Models, Theory, and Computation

Peter Kall and János Mayer are distinguished scholars and professors of Operations Research and their research interest is particularly devoted to the area of stochastic optimization. Stochastic Linear Programming is a definitive presentation and discussion of the theoretical properties of the models, the conceptual algorithmic approaches, and the computational issues relating to the implementation of these methods to solve problems that are stochastic in nature.

From this short sketch of the subject called SLP, which is by far not complete with
respect to the various special problem formulations to be dealt with, we may
already conclude that a basic toolkit of linear and nonlinear programming
methods ...