A constrained optimization problem under uncertainty

Erik Quaeghebeur, Keivan Shariatmadar, Gert de Cooman

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-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 languageEnglish
Title of host publicationComputational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010
EditorsDa Ruan, Tianrui Li, Yang Xu, Guoqing Chen, Etienne E. Kerre
PublisherWorld Scientific
Pages791-796
Number of pages6
ISBN (Electronic)9814324698, 9789814324694
DOIs
Publication statusPublished - 2010
Externally publishedYes
EventComputational Intelligence Foundations and Applications - 9th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2010 - Emei, Chengdu, China
Duration: 2 Aug 20104 Aug 2010

Publication series

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

Conference

ConferenceComputational Intelligence Foundations and Applications - 9th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2010
CountryChina
CityEmei, Chengdu
Period2/08/104/08/10

Keywords

  • Constrained optimization
  • Linear prevision
  • Maximality
  • Maximinity
  • Possibility distribution
  • Vacuous prevision

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