WIAS Preprint No. 2783, (2020)

Dynamic probabilistic constraints under continuous random distributions


  • González Grandón, Tatiana
  • Henrion, René
    ORCID: 0000-0001-5572-7213
  • Pérez-Aros, Pedro

2010 Mathematics Subject Classification

  • 90C15 49K45


  • Dynamic probabilistic constraints, chance constraints, continuous distributions, decision rules, stochastic programming




The paper investigates analytical properties of dynamic probabilistic constraints (chance constraints). The underlying random distribution is supposed to be continuous. In the first part, a general multistage model with decision rules depending on past observations of the random process is analyzed. Basic properties like (weak sequential) (semi-) continuity of the probability function or existence of solutions are studied. It turns out that the results differ significantly according to whether decision rules are embedded into Lebesgue or Sobolev spaces. In the second part, the simplest meaningful two-stage model with decision rules from L 2 is investigated. More specific properties like Lipschitz continuity and differentiability of the probability function are considered. Explicitly verifiable conditions for these properties are provided along with explicit gradient formulae in the Gaussian case. The application of such formulae in the context of necessary optimality conditions is discussed and a concrete identification of solutions presented.

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