The CONEstrip algorithm

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9 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationSynergies of Soft Computing and Statistics for Intelligent Data Analysis
EditorsR. Kruse, M. R. Berthold, C. Moewes, M. Á. Gil, P. Grzegorzewski, O. Hryniewicz
PublisherSpringer
Pages45-54
Number of pages10
ISBN (Print)9783642330414
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012 - Konstanz, Germany
Duration: 4 Oct 20126 Oct 2012

Publication series

NameAdvances in Intelligent Systems and Computing
Volume190 AISC
ISSN (Print)2194-5357

Conference

Conference6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012
CountryGermany
CityKonstanz
Period4/10/126/10/12

Keywords

  • Consistency
  • convex cones
  • feasibility
  • inference
  • linear programming

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