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Inverse Stochastic Linear Programming

Gorkem Saka(gorkems***at***ie.pitt.edu)
Andrew J. Schaefer(schaefer***at***ie.pitt.edu)
Lewis Ntaimo(ntaimo***at***tamu.edu)

Abstract: Inverse optimization perturbs objective function to make an initial feasible solution optimal with respect to perturbed objective function while minimizing cost of perturbation. We extend inverse optimization to two-stage stochastic linear programs. Since the resulting model grows with number of scenarios, we present two decomposition approaches for solving these problems.

Keywords: Inverse Optimization, Stochastic Programming,Decomposition Algorithms

Category 1: Stochastic Programming

Citation: Unpublished: 07-1, University of Pittsburgh, Pittsburgh PA 15261, January 2007.

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Entry Submitted: 01/05/2007
Entry Accepted: 01/05/2007
Entry Last Modified: 01/05/2007

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