@inproceedings{b180c29a5f7242f1a939c06d21a0c1f9,

title = "The CONEstrip algorithm",

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 = "Consistency, convex cones, feasibility, inference, linear programming",

author = "Erik Quaeghebeur",

year = "2013",

doi = "10.1007/978-3-642-33042-1_6",

language = "English",

isbn = "9783642330414",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "45--54",

editor = "R. Kruse and Berthold, {M. R.} and C. Moewes and Gil, {M. {\'A}.} and { Grzegorzewski}, P. and O. Hryniewicz",

booktitle = "Synergies of Soft Computing and Statistics for Intelligent Data Analysis",

address = "Germany",

note = "6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012 ; Conference date: 04-10-2012 Through 06-10-2012",

}