# Some useful functions for the matroid class.¶

For direct access to the methods newlabel(), setprint() and get_nonisomorphic_matroids(), type:

sage: from sage.matroids.advanced import *


See also sage.matroids.advanced.

AUTHORS:

• Stefan van Zwam (2011-06-24): initial version
sage.matroids.utilities.get_nonisomorphic_matroids(MSet)

Return non-isomorphic members of the matroids in set MSet.

For direct access to get_nonisomorphic_matroids, run:

sage: from sage.matroids.advanced import *


INPUT:

• MSet – an iterable whose members are matroids.

OUTPUT:

A list containing one representative of each isomorphism class of members of MSet.

EXAMPLES:

sage: from sage.matroids.advanced import *
sage: L = matroids.Uniform(3, 5).extensions()
sage: len(list(L))
32
sage: len(get_nonisomorphic_matroids(L))
5

sage.matroids.utilities.lift_cross_ratios(A, lift_map=None)

Return a matrix which arises from the given matrix by lifting cross ratios.

INPUT:

• A – a matrix over a ring source_ring.
• lift_map – a python dictionary, mapping each cross ratio of A to some element of a target ring, and such that lift_map[source_ring(1)] = target_ring(1).

OUTPUT:

• Z – a matrix over the ring target_ring.

The intended use of this method is to create a (reduced) matrix representation of a matroid M over a ring target_ring, given a (reduced) matrix representation of A of M over a ring source_ring and a map lift_map from source_ring to target_ring.

This method will create a unique candidate representation Z, but will not verify if Z is indeed a representation of M. However, this is guaranteed if the conditions of the lift theorem (see [PvZ2010]) hold for the lift map in combination with the matrix A.

For a lift map $$f$$ and a matrix $$A$$ these conditions are as follows. First of all $$f: S \rightarrow T$$, where $$S$$ is a set of invertible elements of the source ring and $$T$$ is a set of invertible elements of the target ring. The matrix $$A$$ has entries from the source ring, and each cross ratio of $$A$$ is contained in $$S$$. Moreover:

• $$1 \in S$$, $$1 \in T$$;
• for all $$x \in S$$: $$f(x) = 1$$ if and only if $$x = 1$$;
• for all $$x, y \in S$$: if $$x + y = 0$$ then $$f(x) + f(y) = 0$$;
• for all $$x, y \in S$$: if $$x + y = 1$$ then $$f(x) + f(y) = 1$$;
• for all $$x, y, z \in S$$: if $$xy = z$$ then $$f(x)f(y) = f(z)$$.

Any ring homomorphism $$h: P \rightarrow R$$ induces a lift map from the set of units $$S$$ of $$P$$ to the set of units $$T$$ of $$R$$. There exist lift maps which do not arise in this manner. Several such maps can be created by the function lift_map().

EXAMPLES:

sage: from sage.matroids.advanced import lift_cross_ratios, lift_map, LinearMatroid
sage: R = GF(7)
sage: to_sixth_root_of_unity = lift_map('sru')
sage: A = Matrix(R, [[1, 0, 6, 1, 2],[6, 1, 0, 0, 1],[0, 6, 3, 6, 0]])
sage: A
[1 0 6 1 2]
[6 1 0 0 1]
[0 6 3 6 0]
sage: Z = lift_cross_ratios(A, to_sixth_root_of_unity)
sage: Z # py2
[ 1  0  1  1  1]
[ 1  1  0  0  z]
[ 0  1 -z -1  0]
sage: Z # py3
[ 1  0  1  1  1]
[ 1  1  0  0  z]
[ 0 -1  z  1  0]
sage: M = LinearMatroid(reduced_matrix = A)
sage: sorted(M.cross_ratios())
[3, 5]
sage: N = LinearMatroid(reduced_matrix = Z)
sage: sorted(N.cross_ratios())
[-z + 1, z]
sage: M.is_isomorphism(N, {e:e for e in M.groundset()})
True

sage.matroids.utilities.lift_map(target)

Create a lift map, to be used for lifting the cross ratios of a matroid representation.

INPUT:

• target – a string describing the target (partial) field.

OUTPUT:

• a dictionary

Depending on the value of target, the following lift maps will be created:

• “reg”: a lift map from $$\GF3$$ to the regular partial field $$(\ZZ, <-1>)$$.
• “sru”: a lift map from $$\GF7$$ to the sixth-root-of-unity partial field $$(\QQ(z), <z>)$$, where $$z$$ is a sixth root of unity. The map sends 3 to $$z$$.
• “dyadic”: a lift map from $$\GF{11}$$ to the dyadic partial field $$(\QQ, <-1, 2>)$$.
• “gm”: a lift map from $$\GF{19}$$ to the golden mean partial field $$(\QQ(t), <-1,t>)$$, where $$t$$ is a root of $$t^2-t-1$$. The map sends $$5$$ to $$t$$.

The example below shows that the latter map satisfies three necessary conditions stated in lift_cross_ratios()

EXAMPLES:

sage: from sage.matroids.utilities import lift_map
sage: lm = lift_map('gm')
sage: for x in lm:
....:     if (x == 1) is not (lm[x] == 1):
....:         print('not a proper lift map')
....:     for y in lm:
....:         if (x+y == 0) and not (lm[x]+lm[y] == 0):
....:             print('not a proper lift map')
....:         if (x+y == 1) and not (lm[x]+lm[y] == 1):
....:             print('not a proper lift map')
....:         for z in lm:
....:             if (x*y==z) and not (lm[x]*lm[y]==lm[z]):
....:                 print('not a proper lift map')

sage.matroids.utilities.make_regular_matroid_from_matroid(matroid)

Attempt to construct a regular representation of a matroid.

INPUT:

• matroid – a matroid.

OUTPUT:

Return a $$(0, 1, -1)$$-matrix over the integers such that, if the input is a regular matroid, then the output is a totally unimodular matrix representing that matroid.

EXAMPLES:

sage: from sage.matroids.utilities import make_regular_matroid_from_matroid
sage: make_regular_matroid_from_matroid(
....:               matroids.CompleteGraphic(6)).is_isomorphic(
....:                                     matroids.CompleteGraphic(6))
True

sage.matroids.utilities.newlabel(groundset)

Create a new element label different from the labels in groundset.

INPUT:

• groundset – A set of objects.

OUTPUT:

A string not in the set groundset.

For direct access to newlabel, run:

sage: from sage.matroids.advanced import *


ALGORITHM:

1. Create a set of all one-character alphanumeric strings.
2. Remove the string representation of each existing element from this list.
3. If the list is nonempty, return any element.
4. Otherwise, return the concatenation of the strings of each existing element, preceded by ‘e’.

EXAMPLES:

sage: from sage.matroids.advanced import newlabel
sage: S = set(['a', 42, 'b'])
sage: newlabel(S) in S
False

sage: S = set('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789')
sage: t = newlabel(S)
sage: len(t)
63
sage: t[0]
'e'

sage.matroids.utilities.sanitize_contractions_deletions(matroid, contractions, deletions)

Return a fixed version of sets contractions and deletions.

INPUT:

• matroid – a Matroid instance.
• contractions – a subset of the groundset.
• deletions – a subset of the groundset.

OUTPUT:

An independent set C and a coindependent set D such that

matroid / contractions \ deletions == matroid / C \ D

Raise an error if either is not a subset of the groundset of matroid or if they are not disjoint.

This function is used by the Matroid.minor() method.

EXAMPLES:

sage: from sage.matroids.utilities import setprint
sage: from sage.matroids.utilities import sanitize_contractions_deletions
sage: M = matroids.named_matroids.Fano()
sage: setprint(sanitize_contractions_deletions(M, 'abc', 'defg'))
[{'a', 'b', 'c'}, {'d', 'e', 'f', 'g'}]
sage: setprint(sanitize_contractions_deletions(M, 'defg', 'abc'))
[{'a', 'b', 'c', 'f'}, {'d', 'e', 'g'}]
sage: setprint(sanitize_contractions_deletions(M, [1, 2, 3], 'efg'))
Traceback (most recent call last):
...
ValueError: [1, 2, 3] is not a subset of the groundset
sage: setprint(sanitize_contractions_deletions(M, 'efg', [1, 2, 3]))
Traceback (most recent call last):
...
ValueError: [1, 2, 3] is not a subset of the groundset
Traceback (most recent call last):
...
ValueError: contraction and deletion sets are not disjoint.

sage.matroids.utilities.setprint(X)

Print nested data structures nicely.

Python’s data structures set and frozenset do not print nicely. This function can be used as replacement for print to overcome this. For direct access to setprint, run:

sage: from sage.matroids.advanced import *


Note

This function will be redundant when Sage moves to Python 3, since the default print will suffice then.

INPUT:

• X – Any Python object

OUTPUT:

None. However, the function prints a nice representation of X.

EXAMPLES:

Output looks much better:

sage: from sage.matroids.advanced import setprint
sage: L = [{1, 2, 3}, {1, 2, 4}, {2, 3, 4}, {4, 1, 3}]
sage: print(L)  # py2
[set([1, 2, 3]), set([1, 2, 4]), set([2, 3, 4]), set([1, 3, 4])]
sage: print(L)  # py3
[{1, 2, 3}, {1, 2, 4}, {2, 3, 4}, {1, 3, 4}]
sage: setprint(L)
[{1, 2, 3}, {1, 2, 4}, {1, 3, 4}, {2, 3, 4}]


Note that for iterables, the effect can be undesirable:

sage: from sage.matroids.advanced import setprint
sage: M = matroids.named_matroids.Fano().delete('efg')
sage: M.bases()
Iterator over a system of subsets
sage: setprint(M.bases())
[{'a', 'b', 'c'}, {'a', 'b', 'd'}, {'a', 'c', 'd'}]


An exception was made for subclasses of SageObject:

sage: from sage.matroids.advanced import setprint
sage: G = graphs.PetersenGraph()
sage: list(G)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
sage: setprint(G)
Petersen graph: Graph on 10 vertices

sage.matroids.utilities.setprint_s(X, toplevel=False)

Create the string for use by setprint().

INPUT:

• X – any Python object
• toplevel – (default: False) indicates whether this is a recursion or not.

OUTPUT:

A string representation of the object, with nice notation for sets and frozensets.

EXAMPLES:

sage: from sage.matroids.utilities import setprint_s
sage: L = [{1, 2, 3}, {1, 2, 4}, {2, 3, 4}, {4, 1, 3}]
sage: setprint_s(L)
'[{1, 2, 3}, {1, 2, 4}, {1, 3, 4}, {2, 3, 4}]'


The toplevel argument only affects strings, to mimic print’s behavior:

sage: X = 'abcd'
sage: setprint_s(X)
"'abcd'"
sage: setprint_s(X, toplevel=True)
'abcd'

sage.matroids.utilities.spanning_forest(M)

Return a list of edges of a spanning forest of the bipartite graph defined by $$M$$

INPUT:

• M – a matrix defining a bipartite graph G. The vertices are the rows and columns, if $$M[i,j]$$ is non-zero, then there is an edge between row $$i$$ and column $$j$$.

OUTPUT:

A list of tuples $$(r_i,c_i)$$ representing edges between row $$r_i$$ and column $$c_i$$.

EXAMPLES:

sage: len(sage.matroids.utilities.spanning_forest(matrix([[1,1,1],[1,1,1],[1,1,1]])))
5
sage: len(sage.matroids.utilities.spanning_forest(matrix([[0,0,1],[0,1,0],[0,1,0]])))
3

sage.matroids.utilities.spanning_stars(M)

Returns the edges of a connected subgraph that is a union of all edges incident some subset of vertices.

INPUT:

• M – a matrix defining a bipartite graph G. The vertices are the rows and columns, if $$M[i,j]$$ is non-zero, then there is an edge between row i and column 0.

OUTPUT:

A list of tuples $$(row,column)$$ in a spanning forest of the bipartite graph defined by M

EXAMPLES:

sage: edges = sage.matroids.utilities.spanning_stars(matrix([[1,1,1],[1,1,1],[1,1,1]]))
sage: Graph([(x+3, y) for x,y in edges]).is_connected()
True

sage.matroids.utilities.split_vertex(G, u, v=None, edges=None)

Split a vertex in a graph.

If an edge is inserted between the vertices after splitting, this corresponds to a graphic coextension of a matroid.

INPUT:

• G – A SageMath Graph.
• u – A vertex in G.
• v – (optional) The name of the new vertex after the splitting. If v is specified and already in the graph, it must be an isolated vertex.
• edges – (optional) An iterable container of edges on u that move to v after the splitting. If None, v will be an isolated vertex. The edge labels must be specified.

EXAMPLES:

sage: from sage.matroids.utilities import split_vertex
sage: G = graphs.BullGraph()
sage: split_vertex(G, u = 1, v = 'a', edges = [(1, 3)])
Traceback (most recent call last):
...
ValueError: the edges are not all incident with u
sage: split_vertex(G, u = 1, v = 'a', edges = [(1, 3, None)])
sage: G.edges() # py2
[(0, 1, None), (0, 2, None), (1, 2, None), (2, 4, None), (3, 'a', None)]
sage: [a for a in G.edge_iterator()] # py3 random
[(0, 1, None), (0, 2, None), (1, 2, None), (2, 4, None), ('a', 3, None)]