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 language | English |
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Pages (from-to) | 1997-2009 |
Number of pages | 13 |
Journal | Nonlinear Analysis, Theory, Methods and Applications |
Volume | 47 |
Issue number | 3 |
DOIs | |
State | Published - Aug 2001 |
Event | 3rd World Congress of Nonlinear Analysts - Catania, Sicily, Italy Duration: 19 Jul 2000 → 26 Jul 2000 |
Keywords
- Convex Programming
- Discrete Distributions
- Probabilistic Constraints
- Stochastic Programming