Eventual convexity of chance constrained feasible sets

Abstract

In decision-making problems where uncertainty plays a key role and decisions have to be taken prior to observing uncertainty, chance constraints are a strong modelling tool for defining safety of decisions. These constraints request that a random inequality system depending on a decision vector has to be satisfied with a high probability. The characteristics of the feasible set of such chance constraints depend on the constraint mapping of the random inequality system, the underlying law of uncertainty and the probability level. One characteristic of particular interest is convexity. Convexity can be shown under fairly general conditions on the underlying law of uncertainty and on the constraint mapping, regardless of the probability-level. In some situations, convexity can only be shown when the probability-level is high enough. This is defined as eventual convexity. In this paper, we will investigate further how eventual convexity can be assured for specially structured chance constraints involving Copulae. The Copulae have to exhibit generalized concavity properties. In particular, we will extend recent results and exhibit a clear link between the generalized concavity properties of the various mappings involved in the chance constraint for the result to hold. Various examples show the strength of the provided generalization.

Keywords:

• chance-constrained-programming
• Copulae
• joint-chance-constraints
• convexity
• probabilistic constraint

AMS Subject Classifications:

• MSC 49M37
• MSC 52A30
• MSC 65K05
• MSC 90C15