András Prékopa, RUTCOR, Rutgers University, Piscataway
Andrzej Ruszczynski, RUTCOR, Rutgers University, Piscataway
(SPEPS)
Springer (Berlin [u.a.])
To export the complete metadata set as Endote or Bibtex format please click to the appropriate link.
| edoc-Server der Humboldt-Universität zu Berlin |
| Author(s): |
Darinka Dentcheva, RUTCOR, Rutgers University, Piscataway András Prékopa, RUTCOR, Rutgers University, Piscataway Andrzej Ruszczynski, RUTCOR, Rutgers University, Piscataway | Title: | Concavity and Efficient Points of Discrete Distributions in Probabilistic Programming |
| Date of Acceptance: | 29.11.1999 |
| Submission Date: | 03.11.1999 |
| Series Title: |
Stochastic Programming E-Print Series (SPEPS) |
| Editors: | Julie L. Higle; Werner Römisch; Surrajeet Sen |
| Keywords (eng): | discrete distributions, probabilistic programming, generalized concavity, column generation |
| Appeared in: |
Mathematical Programming 1.2000 (Vol. 89)
Springer (Berlin [u.a.]) |
| Metadata export:
To export the complete metadata set as Endote or Bibtex format please click to the appropriate link.
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Endnote Bibtex |
| Abstract (eng): | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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