Inverse Stochastic Linear Programming

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.


Unpublished: 07-1, University of Pittsburgh, Pittsburgh PA 15261, January 2007.



View Inverse Stochastic Linear Programming