M. Kopa, J. Dupačová
Solutions of stochastic optimization problems are often influenced by the model misspecification and simplifications, or by errors due to approximations, estimations, and incomplete information. The obtained optimal solutions should be then carefully analyzed. We shall deal with output analysis, robustness, and stress testing with respect to uncertainty or perturbations of input data for stochastic optimization problems via the contamination techniques. We focus on problems with decision dependent randomness. Applying the contamination techniques we present lower and upper bonds for optimal value function for several different decision dependent randomness problems.
Keywords: stochastic programming, decision dependent randomness, stress testing
Scheduled
WC1 Stochastic optimization
June 1, 2016 12:00 PM
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