ISSN:
1436-4646
Keywords:
Key words: probabilistic programming – discrete distributions – generalized concavity – column generation Mathematics Subject Classification (1991): 90C15, 90C11, 65K05, 49M27
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mathematics
Notes:
Abstract. We consider 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 stochastic programming problems with discrete random variables. The results are illustrated with numerical examples.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/PL00011393
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