On convex probabilistic programming with discrete distributions

D. Dentcheva, A. Prékopa, A. Ruszczyński

Research output: Contribution to journalConference articlepeer-review

20 Scopus citations

Abstract

We consider convex stochastic programming problems with probabilistic constraints involving integer-valued random variables. The concept of a p-efficient point of a probability distribution is used to derive various equivalent problem formulations. Next we introduce the concept of r-concave discrete probability distributions and analyse its relevance for problems under consideration. These notions are used to derive lower and upper bounds for the optimal value of probabilistically constrained convex stochastic programming problems with discrete random variables.

Original languageEnglish
Pages (from-to)1997-2009
Number of pages13
JournalNonlinear Analysis, Theory, Methods and Applications
Volume47
Issue number3
DOIs
StatePublished - Aug 2001
Event3rd World Congress of Nonlinear Analysts - Catania, Sicily, Italy
Duration: 19 Jul 200026 Jul 2000

Keywords

  • Convex Programming
  • Discrete Distributions
  • Probabilistic Constraints
  • Stochastic Programming

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