Metric regularity and quantitative stability in stochastic programs with probabilistic constraints

Introducing probabilistic constraints leads in general to nonconvex, nonsmo oth or even discontinuous optimization models. In this paper, necessary and sufficient conditions for metric regularity of(several joint) probabilisti c constraints are derived using recent results from nonsmooth analysis. The conditions apply to fairly general constraints and extend earlier work in this direction. Further. a verifiable sufficient condition for quadratic gr owth of the objective function in a more specific convex stochastic program is indicated and applied in order to obtain a new result on quantitative s tability of solution sets when the underlying probability distribution is s ubjected to perturbations. This is used to derive bounds for the deviation of solution sets when the probability measure is replaced by empirical esti mates.