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Scenario Reduction Techniques in Stochastic Programming

  • Werner Römisch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5792)

Abstract

Stochastic programming problems appear as mathematical models for optimization problems under stochastic uncertainty. Most computational approaches for solving such models are based on approximating the underlying probability distribution by a probability measure with finite support. Since the computational complexity for solving stochastic programs gets worse when increasing the number of atoms (or scenarios), it is sometimes necessary to reduce their number. Techniques for scenario reduction often require fast heuristics for solving combinatorial subproblems. Available techniques are reviewed and open problems are discussed.

Keywords

Stochastic Program Scenario Reduction Stochastic Programming Model Stochastic Programming Problem Multistage Stochastic Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Werner Römisch
    • 1
  1. 1.Department of MathematicsHumboldt-University BerlinBerlinGermany

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