Towards using the chordal graph polytope in learning decomposable models

Milan Studený, James Cussens

Research output: Contribution to journalArticlepeer-review


The motivation for this paper is the integer linear programming approach to learning the structure of a decomposable graphical model. We have chosen to represent decomposable models by means of special zero–one vectors, named characteristic imsets. Our approach leads to the study of a special polytope, defined as the convex hull of all characteristic imsets for chordal graphs, named the chordal graph polytope. In this theoretical paper, we introduce a class of clutter inequalities (valid for the vectors in the polytope) and show that all of them are facet-defining for the polytope. We dare to conjecture that they lead to a complete polyhedral description of the polytope. Finally, we propose a linear programming method to solve the separation problem with these inequalities for the use in a cutting plane approach.
Original languageEnglish
Pages (from-to)259-281
Number of pages23
JournalInternational Journal of Approximate Reasoning
Early online date8 Jun 2017
Publication statusPublished - Sept 2017

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