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Sensitivity method for basis inverse representation in multistage stochastic linear programming problems

Abstract

A version of the simplex method for solving stochastic linear control problems is presented. The method uses a compact basis inverse representation that extensively exploits the original problem data and takes advantage of the supersparse structure of the problem. Computational experience indicates that the method is capable of solving large problems.

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This research was supported by Programs CPBP02.15 and RPI.02.

Communicated by O. L. Mangasarian

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Gondzio, J., Ruszczyński, A. Sensitivity method for basis inverse representation in multistage stochastic linear programming problems. J Optim Theory Appl 74, 221–242 (1992). https://doi.org/10.1007/BF00940892

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Key Words

  • Linear programming
  • stochastic programming
  • simplex method