The CONEstrip algorithm
- Author
- Erik Quaeghebeur (UGent)
- Organization
- Abstract
- Uncertainty models such as sets of desirable gambles and (conditional) lower previsions can be represented as convex cones. Checking the consistency of and drawing inferences from such models requires solving feasibility and optimization problems. We consider finitely generated such models. For closed cones, we can use linear programming; for conditional lower prevision-based cones, there is an efficient algorithm using an iteration of linear programs. We present an efficient algorithm for general cones that also uses an iteration of linear programs.
- Keywords
- feasibility, inference, convex cones, consistency, linear programming, PROBABILITY
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-3007274
- MLA
- Quaeghebeur, Erik. “The CONEstrip Algorithm.” Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing, edited by Rudolf Kruse et al., vol. 190, Springer, 2013, pp. 45–54, doi:10.1007/978-3-642-33042-1_6.
- APA
- Quaeghebeur, E. (2013). The CONEstrip algorithm. In R. Kruse, M. R. Berthold, C. Moewes, M. Á. Gil, P. Grzegorzewski, & O. Hryniewicz (Eds.), Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing (Vol. 190, pp. 45–54). https://doi.org/10.1007/978-3-642-33042-1_6
- Chicago author-date
- Quaeghebeur, Erik. 2013. “The CONEstrip Algorithm.” In Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing, edited by Rudolf Kruse, Michael R Berthold, Christian Moewes, María Ángeles Gil, Przemysław Grzegorzewski, and Olgierd Hryniewicz, 190:45–54. Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-33042-1_6.
- Chicago author-date (all authors)
- Quaeghebeur, Erik. 2013. “The CONEstrip Algorithm.” In Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing, ed by. Rudolf Kruse, Michael R Berthold, Christian Moewes, María Ángeles Gil, Przemysław Grzegorzewski, and Olgierd Hryniewicz, 190:45–54. Berlin, Germany: Springer. doi:10.1007/978-3-642-33042-1_6.
- Vancouver
- 1.Quaeghebeur E. The CONEstrip algorithm. In: Kruse R, Berthold MR, Moewes C, Gil MÁ, Grzegorzewski P, Hryniewicz O, editors. Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing. Berlin, Germany: Springer; 2013. p. 45–54.
- IEEE
- [1]E. Quaeghebeur, “The CONEstrip algorithm,” in Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing, Konstanz, Germany, 2013, vol. 190, pp. 45–54.
@inproceedings{3007274,
abstract = {{Uncertainty models such as sets of desirable gambles and (conditional) lower previsions can be represented as convex cones. Checking the consistency of and drawing inferences from such models requires solving feasibility and optimization problems. We consider finitely generated such models. For closed cones, we can use linear programming; for conditional lower prevision-based cones, there is an efficient algorithm using an iteration of linear programs. We present an efficient algorithm for general cones that also uses an iteration of linear programs.}},
author = {{Quaeghebeur, Erik}},
booktitle = {{Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing}},
editor = {{Kruse, Rudolf and Berthold, Michael R and Moewes, Christian and Gil, María Ángeles and Grzegorzewski, Przemysław and Hryniewicz, Olgierd}},
isbn = {{9783642330414}},
issn = {{2194-5357}},
keywords = {{feasibility,inference,convex cones,consistency,linear programming,PROBABILITY}},
language = {{eng}},
location = {{Konstanz, Germany}},
pages = {{45--54}},
publisher = {{Springer}},
title = {{The CONEstrip algorithm}},
url = {{http://doi.org/10.1007/978-3-642-33042-1_6}},
volume = {{190}},
year = {{2013}},
}
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