Abstract:In this paper we give a review of the method of imsets introduced by Studený 1 from a geometric point of view. Elementary imsets span a polyhedral cone and its dual cone is the cone of supermodular functions. We review basic facts on the structure of these cones. Then we derive some new results on the following topics: i) extreme rays of the cone of standardized supermodular functions, ii) faces of the cones, iii) small relations among elementary imsets, and iv) some computational results on Markov basis for t… Show more
“…By (15), one also has S ∈ S m π = S π . Since the conditions (a)-(b) define a linear space in R Υ×N and both x m and y satisfy them, for any ε ∈ R, the vector z ε must satisfy them as well.…”
Section: Proof Of the Main Resultsmentioning
confidence: 96%
“…Further considerations are done with a fixed set S ⊆ N. One can certainly find and fix π ∈ Υ with S ∈ C π . By (15), one also has S ∈ S m π = S π . Since the conditions (a)-(b) define a linear space in R Υ×N and both x m and y satisfy them, for any ε ∈ R, the vector z ε must satisfy them as well.…”
Section: Proof Of the Main Resultsmentioning
confidence: 96%
“…To verify (19) realize that, for τ, π ∈ Υ and S ∈ C τ ∩ C π , (15) gives C τ ⊆ S m τ = S τ and C π ⊆ S m π = S π and then the condition (b) for y implies i∈S y(τ, i) = i∈S y(π, i), which was desired.…”
Section: Proof Of the Main Resultsmentioning
confidence: 99%
“…Extreme supermodular functions also establish quite an important class of inequalities that are used in (integer) linear programming approach to learning Bayesian network structure; see [36, § 3.1] or [5, § 7]. There were attempts to classify extreme supermodular functions and the operations with them [35,15]; see also the open problems from § 9.1.2 of [34]. 3.…”
Section: Introductionmentioning
confidence: 99%
“…In connection with the application of extreme supermodular functions in testing conditional independence implication by means of a computer a catalogue of all (types of) extreme supermodular functions over 5 variables has been created [6]. The paper [2] contains several interesting results on (linear) operations with supermodular functions which preserve extremality. The aim of this technical report is to gather the results on operations with set functions preserving supermodularity and put the observations from [2] in this context.…”
We give a necessary and sufficient condition for extremality of a supermodular function based on its min-representation by means of (vertices of) the corresponding core polytope. The condition leads to solving a certain simple linear equation system determined by the combinatorial core structure. This result allows us to characterize indecomposability in the class of generalized permutohedra. We provide an in-depth comparison between our result and the description of extremality in the supermodular/submodular cone achieved by other researchers.
“…By (15), one also has S ∈ S m π = S π . Since the conditions (a)-(b) define a linear space in R Υ×N and both x m and y satisfy them, for any ε ∈ R, the vector z ε must satisfy them as well.…”
Section: Proof Of the Main Resultsmentioning
confidence: 96%
“…Further considerations are done with a fixed set S ⊆ N. One can certainly find and fix π ∈ Υ with S ∈ C π . By (15), one also has S ∈ S m π = S π . Since the conditions (a)-(b) define a linear space in R Υ×N and both x m and y satisfy them, for any ε ∈ R, the vector z ε must satisfy them as well.…”
Section: Proof Of the Main Resultsmentioning
confidence: 96%
“…To verify (19) realize that, for τ, π ∈ Υ and S ∈ C τ ∩ C π , (15) gives C τ ⊆ S m τ = S τ and C π ⊆ S m π = S π and then the condition (b) for y implies i∈S y(τ, i) = i∈S y(π, i), which was desired.…”
Section: Proof Of the Main Resultsmentioning
confidence: 99%
“…Extreme supermodular functions also establish quite an important class of inequalities that are used in (integer) linear programming approach to learning Bayesian network structure; see [36, § 3.1] or [5, § 7]. There were attempts to classify extreme supermodular functions and the operations with them [35,15]; see also the open problems from § 9.1.2 of [34]. 3.…”
Section: Introductionmentioning
confidence: 99%
“…In connection with the application of extreme supermodular functions in testing conditional independence implication by means of a computer a catalogue of all (types of) extreme supermodular functions over 5 variables has been created [6]. The paper [2] contains several interesting results on (linear) operations with supermodular functions which preserve extremality. The aim of this technical report is to gather the results on operations with set functions preserving supermodularity and put the observations from [2] in this context.…”
We give a necessary and sufficient condition for extremality of a supermodular function based on its min-representation by means of (vertices of) the corresponding core polytope. The condition leads to solving a certain simple linear equation system determined by the combinatorial core structure. This result allows us to characterize indecomposability in the class of generalized permutohedra. We provide an in-depth comparison between our result and the description of extremality in the supermodular/submodular cone achieved by other researchers.
In that paper, we recall the notion of the multidegree for D-modules, as exposed in a previous paper [2], with a slight simplification. A particular emphasis is given on hypergeometric systems, used to provide interesting and computable examples.
The aim of the talk will be to explain how the statistical task to learn so-called Bayesian network structure from data leads to the study of a special polyhedron, and to report on what was found out about that polyhedron so far.
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