Quantitative stability in stochastic programming: the method of probability metrics. (English) Zbl 1082.90080

Summary: Quantitative stability of optimal values and solution sets to stochastic programming problems is studied when the underlying probability distribution varies in some metric space of probability measures. We give conditions that imply that a stochastic program behaves stable with respect to a minimal information (m.i.) probability metric that is naturally associated with the data of the program. Canonical metrics bounding the m.i. metric are derived for specific models, namely for linear two-stage, mixed-integer two-stage and chance-constrained models. The corresponding quantitative stability results as well as some consequences for asymptotic properties of empirical approximations extend earlier results in this direction. In particular, rates of convergence in probability are derived under metric entropy conditions. Finally, we study stability properties of stable investment portfolios having minimal risk with respect to the spectral measure and stability index of the underlying stable probability distribution.


90C15 Stochastic programming
60B10 Convergence of probability measures
60E05 Probability distributions: general theory
90C31 Sensitivity, stability, parametric optimization
91B28 Finance etc. (MSC2000)
Full Text: DOI Link