This class gathers the algorithms related to convexity in a graph. It implements the following methods:
ConvexityProperties.hull() | Returns the convex hull of a set of vertices |
ConvexityProperties.hull_number() | Computes the hull number of a graph and a corresponding generating set. |
These methods can be used through the ConvexityProperties object returned by Graph.convexity_properties().
AUTHORS:
- Nathann Cohen
Bases: object
This class gathers the algorithms related to convexity in a graph.
Definitions
A set \(S \subseteq V(G)\) of vertices is said to be convex if for all \(u,v\in S\) the set \(S\) contains all the vertices located on a shortest path between \(u\) and \(v\). Alternatively, a set \(S\) is said to be convex if the distances satisfy \(\forall u,v\in S, \forall w\in V\backslash S : d_{G}(u,w) + d_{G}(w,v) > d_{G}(u,v)\).
The convex hull \(h(S)\) of a set \(S\) of vertices is defined as the smallest convex set containing \(S\).
It is a closure operator, as trivially \(S\subseteq h(S)\) and \(h(h(S)) = h(S)\).
What this class contains
As operations on convex sets generally involve the computation of distances between vertices, this class’ purpose is to cache that information so that computing the convex hulls of several different sets of vertices does not imply recomputing several times the distances between the vertices.
In order to compute the convex hull of a set \(S\) it is possible to write the following algorithm.
For any pair `u,v` of elements in the set `S`, and for any vertex `w` outside of it, add `w` to `S` if `d_{G}(u,w) + d_{G}(w,v) = d_{G}(u,v)`. When no vertex can be added anymore, the set `S` is convex
The distances are not actually that relevant. The same algorithm can be implemented by remembering for each pair \(u, v\) of vertices the list of elements \(w\) satisfying the condition, and this is precisely what this class remembers, encoded as bitsets to make storage and union operations more efficient.
Note
Possible improvements
When computing a convex set, all the pairs of elements belonging to the set \(S\) are enumerated several times.
Nothing says these recommandations will actually lead to any actual improvements. There are just some ideas remembered while writing this code. Trying to optimize may well lead to lost in efficiency on many instances.
EXAMPLE:
sage: from sage.graphs.convexity_properties import ConvexityProperties
sage: g = graphs.PetersenGraph()
sage: CP = ConvexityProperties(g)
sage: CP.hull([1,3])
[1, 2, 3]
sage: CP.hull_number()
3
TESTS:
sage: ConvexityProperties(digraphs.Circuit(5))
Traceback (most recent call last):
...
ValueError: This is currenly implemented for Graphs only.Only minor updates are needed if you want to makeit support DiGraphs too.
Returns the convex hull of a set of vertices.
INPUT:
EXAMPLE:
sage: from sage.graphs.convexity_properties import ConvexityProperties
sage: g = graphs.PetersenGraph()
sage: CP = ConvexityProperties(g)
sage: CP.hull([1,3])
[1, 2, 3]
Computes the hull number and a corresponding generating set.
The hull number \(hn(G)\) of a graph \(G\) is the cardinality of a smallest set of vertices \(S\) such that \(h(S)=V(G)\).
INPUT:
COMPLEXITY:
This problem is NP-Hard [CHZ02], but seems to be of the “nice” kind. Update this comment if you fall on hard instances \(:-)\)
ALGORITHM:
This is solved by linear programming.
As the function \(h(S)\) associating to each set \(S\) its convex hull is a closure operator, it is clear that any set \(S_G\) of vertices such that \(h(S_G)=V(G)\) must satisfy \(S_G \not \subseteq C\) for any proper convex set \(C \subsetneq V(G)\). The following formulation is hence correct
Of course, the number of convex sets – and so the number of constraints – can be huge, and hard to enumerate, so at first an incomplete formulation is solved (it is missing some constraints). If the answer returned by the LP solver is a set \(S\) generating the whole graph, then it is optimal and so is returned. Otherwise, the constraint corresponding to the set \(h(S)\) can be added to the LP, which makes the answer \(S\) infeasible, and another solution computed.
This being said, simply adding the constraint corresponding to \(h(S)\) is a bit slow, as these sets can be large (and the corresponding constrait a bit weak). To improve it a bit, before being added, the set \(h(S)\) is “greedily enriched” to a set \(S'\) with vertices for as long as \(h(S')\neq V(G)\). This way, we obtain a set \(S'\) with \(h(S)\subseteq h(S')\subsetneq V(G)\), and the constraint corresponding to \(h(S')\) – which is stronger than the one corresponding to \(h(S)\) – is added.
This can actually be seen as a hitting set problem on the complement of convex sets.
EXAMPLE:
The Hull number of Petersen’s graph:
sage: from sage.graphs.convexity_properties import ConvexityProperties
sage: g = graphs.PetersenGraph()
sage: CP = ConvexityProperties(g)
sage: CP.hull_number()
3
sage: generating_set = CP.hull_number(value_only = False)
sage: CP.hull(generating_set)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
REFERENCE:
[CHZ02] | F. Harary, E. Loukakis, C. Tsouros The geodetic number of a graph Mathematical and computer modelling vol. 17 n11 pp.89–95, 1993 |