A constrained optimization problem under uncertainty

Erik Quaeghebeur; Keivan Shariatmadar; Gert de Cooman

doi:10.1142/9789814324700_0120

A constrained optimization problem under uncertainty

Erik Quaeghebeur
, Keivan Shariatmadar
, Gert de Cooman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

Abstract

We investigate a constrained optimization problem for which there is uncer-tainty about a constraint parameter. Our aim is to reformulate it as a (con-strained) optimization problem without uncertainty. This is done by recasting the original problem as a decision problem under uncertainty. We give results for a number of different types of uncertainty models—linear and vacuous pre-visions, and possibility distributions—and for two different optimality criteria for decision problems under uncertainty—maximinity and maximality.

Original language	English
Title of host publication	Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010
Editors	Da Ruan, Tianrui Li, Yang Xu, Guoqing Chen, Etienne E. Kerre
Publisher	World Scientific
Pages	791-796
Number of pages	6
ISBN (Electronic)	9814324698, 9789814324694
DOIs	https://doi.org/10.1142/9789814324700_0120
Publication status	Published - 2010
Externally published	Yes
Event	Computational Intelligence Foundations and Applications - 9th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2010 - Emei, Chengdu, China Duration: 2 Aug 2010 → 4 Aug 2010

Publication series

Name	Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010

Conference

Conference	Computational Intelligence Foundations and Applications - 9th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2010
Country/Territory	China
City	Emei, Chengdu
Period	2/08/10 → 4/08/10

Funding

Erik Quaeghebeur was supported by a Fellowship of the Belgian American Educational Foundation. This research was supported by the IWT SBO project 60043, “Fuzzy Finite Element Method”.

Keywords

Constrained optimization
Linear prevision
Maximality
Maximinity
Possibility distribution
Vacuous prevision

Access to Document

10.1142/9789814324700_0120

Cite this

Quaeghebeur, E., Shariatmadar, K., & de Cooman, G. (2010). A constrained optimization problem under uncertainty. In D. Ruan, T. Li, Y. Xu, G. Chen, & E. E. Kerre (Eds.), Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010 (pp. 791-796). (Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010). World Scientific. https://doi.org/10.1142/9789814324700_0120

Quaeghebeur, Erik ; Shariatmadar, Keivan ; de Cooman, Gert. / A constrained optimization problem under uncertainty. Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010. editor / Da Ruan ; Tianrui Li ; Yang Xu ; Guoqing Chen ; Etienne E. Kerre. World Scientific, 2010. pp. 791-796 (Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010).

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title = "A constrained optimization problem under uncertainty",

abstract = "We investigate a constrained optimization problem for which there is uncer-tainty about a constraint parameter. Our aim is to reformulate it as a (con-strained) optimization problem without uncertainty. This is done by recasting the original problem as a decision problem under uncertainty. We give results for a number of different types of uncertainty models—linear and vacuous pre-visions, and possibility distributions—and for two different optimality criteria for decision problems under uncertainty—maximinity and maximality.",

keywords = "Constrained optimization, Linear prevision, Maximality, Maximinity, Possibility distribution, Vacuous prevision",

author = "Erik Quaeghebeur and Keivan Shariatmadar and \{de Cooman\}, Gert",

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booktitle = "Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010",

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note = "Computational Intelligence Foundations and Applications - 9th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2010 ; Conference date: 02-08-2010 Through 04-08-2010",

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Quaeghebeur, E, Shariatmadar, K & de Cooman, G 2010, A constrained optimization problem under uncertainty. in D Ruan, T Li, Y Xu, G Chen & EE Kerre (eds), Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010. Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010, World Scientific, pp. 791-796, Computational Intelligence Foundations and Applications - 9th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2010, Emei, Chengdu, China, 2/08/10. https://doi.org/10.1142/9789814324700_0120

A constrained optimization problem under uncertainty. / Quaeghebeur, Erik; Shariatmadar, Keivan; de Cooman, Gert.
Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010. ed. / Da Ruan; Tianrui Li; Yang Xu; Guoqing Chen; Etienne E. Kerre. World Scientific, 2010. p. 791-796 (Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - We investigate a constrained optimization problem for which there is uncer-tainty about a constraint parameter. Our aim is to reformulate it as a (con-strained) optimization problem without uncertainty. This is done by recasting the original problem as a decision problem under uncertainty. We give results for a number of different types of uncertainty models—linear and vacuous pre-visions, and possibility distributions—and for two different optimality criteria for decision problems under uncertainty—maximinity and maximality.

KW - Constrained optimization

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KW - Maximality

KW - Maximinity

KW - Possibility distribution

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Quaeghebeur E, Shariatmadar K, de Cooman G. A constrained optimization problem under uncertainty. In Ruan D, Li T, Xu Y, Chen G, Kerre EE, editors, Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010. World Scientific. 2010. p. 791-796. (Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010). doi: 10.1142/9789814324700_0120

A constrained optimization problem under uncertainty

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