Linear matroids#

When \(A\) is an \(r\) times \(E\) matrix, the linear matroid \(M[A]\) has ground set \(E\) and, for independent sets, all \(F\) subset of \(E\) such that the columns of \(M[A]\) indexed by \(F\) are linearly independent.

Construction#

The recommended way to create a linear matroid is by using the Matroid() function, with a representation matrix \(A\) as input. This function will intelligently choose one of the dedicated classes BinaryMatroid, TernaryMatroid, QuaternaryMatroid, RegularMatroid when appropriate. However, invoking the classes directly is possible too. To get access to them, type:

sage: from sage.matroids.advanced import *

See also sage.matroids.advanced. In both cases, it is possible to provide a reduced matrix \(B\), to create the matroid induced by \(A = [ I B ]\):

sage: from sage.matroids.advanced import *
sage: A = Matrix(GF(2), [[1, 0, 0, 1, 1, 0, 1], [0, 1, 0, 1, 0, 1, 1],
....:                    [0, 0, 1, 0, 1, 1, 1]])
sage: B = Matrix(GF(2), [[1, 1, 0, 1], [1, 0, 1, 1], [0, 1, 1, 1]])
sage: M1 = Matroid(A)
sage: M2 = LinearMatroid(A)
sage: M3 = BinaryMatroid(A)
sage: M4 = Matroid(reduced_matrix=B)
sage: M5 = LinearMatroid(reduced_matrix=B)
sage: isinstance(M1, BinaryMatroid)
True
sage: M1.equals(M2)
True
sage: M1.equals(M3)
True
sage: M1 == M4
True
sage: M1.is_field_isomorphic(M5)
True
sage: M2 == M3  # comparing LinearMatroid and BinaryMatroid always yields False
False

Class methods#

The LinearMatroid class and its derivatives inherit all methods from the Matroid and BasisExchangeMatroid classes. See the documentation for these classes for an overview. In addition, the following methods are available:

LinearMatroid
BinaryMatroid has all of the LinearMatroid ones, and
TernaryMatroid has all of the LinearMatroid ones, and
- bicycle_dimension()
- character()
QuaternaryMatroid has all of the LinearMatroid ones, and
- bicycle_dimension()
RegularMatroid has all of the LinearMatroid ones, and
- is_graphic()

AUTHORS:

Rudi Pendavingh, Stefan van Zwam (2013-04-01): initial version

Methods#

class sage.matroids.linear_matroid.BinaryMatroid#

Bases: LinearMatroid

Binary matroids.

A binary matroid is a linear matroid represented over the finite field with two elements. See LinearMatroid for a definition.

The simplest way to create a BinaryMatroid is by giving only a matrix \(A\). Then, the groundset defaults to range(A.ncols()). Any iterable object \(E\) can be given as a groundset. If \(E\) is a list, then E[i] will label the \(i\)-th column of \(A\). Another possibility is to specify a reduced matrix \(B\), to create the matroid induced by \(A = [ I B ]\).

INPUT:

matrix – (default: None) a matrix whose column vectors represent the matroid.
reduced_matrix – (default: None) a matrix \(B\) such that \([I\ \ B]\) represents the matroid, where \(I\) is an identity matrix with the same number of rows as \(B\). Only one of matrix and reduced_matrix should be provided.
groundset – (default: None) an iterable containing the element labels. When provided, must have the correct number of elements: the number of columns of matrix or the number of rows plus the number of columns of reduced_matrix.
ring – (default: None) ignored.
keep_initial_representation – (default: True) decides whether or not an internal copy of the input matrix should be preserved. This can help to see the structure of the matroid (e.g. in the case of graphic matroids), and makes it easier to look at extensions. However, the input matrix may have redundant rows, and sometimes it is desirable to store only a row-reduced copy.
basis – (default: None) When provided, this is an ordered subset of groundset, such that the submatrix of matrix indexed by basis is an identity matrix. In this case, no row reduction takes place in the initialization phase.

OUTPUT:

A BinaryMatroid instance based on the data above.

Note

An indirect way to generate a binary matroid is through the Matroid() function. This is usually the preferred way, since it automatically chooses between BinaryMatroid and other classes. For direct access to the BinaryMatroid constructor, run:

sage: from sage.matroids.advanced import *

EXAMPLES:

sage: A = Matrix(GF(2), 2, 4, [[1, 0, 1, 1], [0, 1, 1, 1]])
sage: M = Matroid(A)
sage: M
Binary matroid of rank 2 on 4 elements, type (0, 6)
sage: sorted(M.groundset())
[0, 1, 2, 3]
sage: Matrix(M)
[1 0 1 1]
[0 1 1 1]
sage: M = Matroid(matrix=A, groundset='abcd')
sage: sorted(M.groundset())
['a', 'b', 'c', 'd']
sage: B = Matrix(GF(2), 2, 2, [[1, 1], [1, 1]])
sage: N = Matroid(reduced_matrix=B, groundset='abcd')
sage: M == N
True

base_ring()#

Return the base ring of the matrix representing the matroid, in this case \(\GF{2}\).

EXAMPLES:

sage: M = matroids.catalog.Fano()
sage: M.base_ring()
Finite Field of size 2

bicycle_dimension()#

Return the bicycle dimension of the binary matroid.

The bicycle dimension of a linear subspace \(V\) is \(\dim(V\cap V^\perp)\). The bicycle dimension of a matroid equals the bicycle dimension of its cocycle-space, and is an invariant for binary matroids. See [Pen2012], [GR2001] for more information.

OUTPUT:

Integer.

EXAMPLES:

sage: M = matroids.catalog.Fano()
sage: M.bicycle_dimension()
3

binary_matroid(randomized_tests=1, verify=True)#

Return a binary matroid representing self.

INPUT:

randomized_tests – Ignored.
verify – Ignored

OUTPUT:

A binary matroid.

ALGORITHM:

self is a binary matroid, so just return self.

See also

M.binary_matroid()

EXAMPLES:

sage: N = matroids.catalog.Fano()
sage: N.binary_matroid() is N
True

brown_invariant()#

Return the value of Brown’s invariant for the binary matroid.

For a binary space \(V\), consider the sum \(B(V):=\sum_{v\in V} i^{|v|}\), where \(|v|\) denotes the number of nonzero entries of a binary vector \(v\). The value of the Tutte Polynomial in the point \((-i, i)\) can be expressed in terms of \(B(V)\), see [Pen2012]. If \(|v|\) equals \(2\) modulo 4 for some \(v\in V\cap V^\perp\), then \(B(V)=0\). In this case, Browns invariant is not defined. Otherwise, \(B(V)=\sqrt{2}^k \exp(\sigma \pi i/4)\) for some integers \(k, \sigma\). In that case, \(k\) equals the bycycle dimension of \(V\), and Browns invariant for \(V\) is defined as \(\sigma\) modulo \(8\).

The Brown invariant of a binary matroid equals the Brown invariant of its cocycle-space.

OUTPUT:

Integer.

EXAMPLES:

sage: M = matroids.catalog.Fano()
sage: M.brown_invariant()
0
sage: M = Matroid(Matrix(GF(2), 3, 8, [[1, 0, 0, 1, 1, 1, 1, 1],
....:                                  [0, 1, 0, 1, 1, 0, 0, 0],
....:                                  [0, 0, 1, 0, 0, 1, 1, 0]]))
sage: M.brown_invariant() is None
True

characteristic()#

Return the characteristic of the base ring of the matrix representing the matroid, in this case \(2\).

EXAMPLES:

sage: M = matroids.catalog.Fano()
sage: M.characteristic()
2

is_binary(randomized_tests=1)#

Decide if self is a binary matroid.

INPUT:

randomized_tests – Ignored.

OUTPUT:

A Boolean.

ALGORITHM:

self is a binary matroid, so just return True.

See also

M.is_binary()

EXAMPLES:

sage: N = matroids.catalog.Fano()
sage: N.is_binary()
True

is_graphic()#

Test if the binary matroid is graphic.

A matroid is graphic if there exists a graph whose edge set equals the groundset of the matroid, such that a subset of elements of the matroid is independent if and only if the corresponding subgraph is acyclic.

OUTPUT:

Boolean.

EXAMPLES:

sage: R10 = matroids.catalog.R10()
sage: M = Matroid(ring=GF(2), reduced_matrix=R10.representation(
....:                                 reduced=True, labels=False))
sage: M.is_graphic()
False
sage: K5 = Matroid(graphs.CompleteGraph(5), regular=True)                   # needs sage.graphs
sage: M = Matroid(ring=GF(2), reduced_matrix=K5.representation(             # needs sage.graphs sage.rings.finite_rings
....:                                 reduced=True, labels=False))
sage: M.is_graphic()                                                        # needs sage.graphs sage.rings.finite_rings
True
sage: M.dual().is_graphic()                                                 # needs sage.graphs
False

ALGORITHM:

In a recent paper, Geelen and Gerards [GG2012] reduced the problem to testing if a system of linear equations has a solution. While not the fastest method, and not necessarily constructive (in the presence of 2-separations especially), it is easy to implement.

is_valid()#

Test if the data obey the matroid axioms.

Since this is a linear matroid over the field \(\GF{2}\), this is always the case.

OUTPUT:

True.

EXAMPLES:

sage: M = Matroid(Matrix(GF(2), [[]]))
sage: M.is_valid()
True

class sage.matroids.linear_matroid.LinearMatroid#

Bases: BasisExchangeMatroid

Linear matroids.

When \(A\) is an \(r\) times \(E\) matrix, the linear matroid \(M[A]\) has ground set \(E\) and set of independent sets

\(I(A) =\{F \subseteq E :\) the columns of \(A\) indexed by \(F\) are linearly independent \(\}\)

The simplest way to create a LinearMatroid is by giving only a matrix \(A\). Then, the groundset defaults to range(A.ncols()). Any iterable object E can be given as a groundset. If E is a list, then E[i] will label the \(i\)-th column of \(A\). Another possibility is to specify a reduced matrix \(B\), to create the matroid induced by \(A = [ I B ]\).

INPUT:

matrix – (default: None) a matrix whose column vectors represent the matroid.
reduced_matrix – (default: None) a matrix \(B\) such that \([I\ \ B]\) represents the matroid, where \(I\) is an identity matrix with the same number of rows as \(B\). Only one of matrix and reduced_matrix should be provided.
groundset – (default: None) an iterable containing the element labels. When provided, must have the correct number of elements: the number of columns of matrix or the number of rows plus the number of columns of reduced_matrix.
ring – (default: None) the desired base ring of the matrix. If the base ring is different, an attempt will be made to create a new matrix with the correct base ring.
keep_initial_representation – (default: True) decides whether or not an internal copy of the input matrix should be preserved. This can help to see the structure of the matroid (e.g. in the case of graphic matroids), and makes it easier to look at extensions. However, the input matrix may have redundant rows, and sometimes it is desirable to store only a row-reduced copy.

OUTPUT:

A LinearMatroid instance based on the data above.

Note

The recommended way to generate a linear matroid is through the Matroid() function. It will automatically choose more optimized classes when present (currently BinaryMatroid, TernaryMatroid, QuaternaryMatroid, RegularMatroid). For direct access to the LinearMatroid constructor, run:

sage: from sage.matroids.advanced import *

EXAMPLES:

sage: from sage.matroids.advanced import *
sage: A = Matrix(GF(3), 2, 4, [[1, 0, 1, 1], [0, 1, 1, 2]])
sage: M = LinearMatroid(A)
sage: M
Linear matroid of rank 2 on 4 elements represented over the Finite
Field of size 3
sage: sorted(M.groundset())
[0, 1, 2, 3]
sage: Matrix(M)
[1 0 1 1]
[0 1 1 2]
sage: M = LinearMatroid(A, 'abcd')
sage: sorted(M.groundset())
['a', 'b', 'c', 'd']
sage: B = Matrix(GF(3), 2, 2, [[1, 1], [1, 2]])
sage: N = LinearMatroid(reduced_matrix=B, groundset='abcd')
sage: M == N
True

base_ring()#

Return the base ring of the matrix representing the matroid.

EXAMPLES:

sage: M = Matroid(matrix=Matrix(GF(5), [[1, 0, 1, 1, 1],
....:                                   [0, 1, 1, 2, 3]]))
sage: M.base_ring()
Finite Field of size 5

characteristic()#

Return the characteristic of the base ring of the matrix representing the matroid.

EXAMPLES:

sage: M = Matroid(matrix=Matrix(GF(5), [[1, 0, 1, 1, 1],
....:                                   [0, 1, 1, 2, 3]]))
sage: M.characteristic()
5

cross_ratio(F, a, b, c, d)#

Return the cross ratio of the four ordered points a, b, c, d after contracting a flat F of codimension 2.

Consider the following matrix with columns labeled by \(\{a, b, c, d\}\).

\[\begin{split}\begin{bmatrix} 1 & 0 & 1 & 1\\ 0 & 1 & x & 1 \end{bmatrix}\end{split}\]

The cross ratio of the ordered tuple \((a, b, c, d)\) equals \(x\). This method looks at such minors where F is a flat to be contracted, and all remaining elements other than a, b, c, d are deleted.

INPUT:

F – A flat of codimension 2
a, b, c, d – elements of the groundset

OUTPUT:

The cross ratio of the four points on the line obtained by contracting F.

EXAMPLES:

sage: M = Matroid(Matrix(GF(7), [[1, 0, 0, 1, 1, 1],
....:                            [0, 1, 0, 1, 2, 4],
....:                            [0, 0, 1, 3, 2, 6]]))
sage: M.cross_ratio([0], 1, 2, 3, 5)
4

sage: M = Matroid(ring=GF(7), matrix=[[1, 0, 1, 1], [0, 1, 1, 1]])
sage: M.cross_ratio(set(), 0, 1, 2, 3)
Traceback (most recent call last):
...
ValueError: points a, b, c, d do not form a 4-point line in M/F

cross_ratios(hyperlines=None)#

Return the set of cross ratios that occur in this linear matroid.

Consider the following matrix with columns labeled by \(\{a, b, c, d\}\).

\[\begin{split}\begin{matrix} 1 & 0 & 1 & 1\\ 0 & 1 & x & 1 \end{matrix}\end{split}\]

The cross ratio of the ordered tuple \((a, b, c, d)\) equals \(x\). The set of all cross ratios of a matroid is the set of cross ratios of all such minors.

INPUT:

hyperlines – (optional) a set of flats of the matroid, of rank \(r - 2\), where \(r\) is the rank of the matroid. If not given, then hyperlines defaults to all such flats.

OUTPUT:

A list of all cross ratios of this linearly represented matroid that occur in rank-2 minors that arise by contracting a flat F in hyperlines (so by default, those are all cross ratios).

See also

M.cross_ratio()

EXAMPLES:

sage: M = Matroid(Matrix(GF(7), [[1, 0, 0, 1, 1, 1],
....:                            [0, 1, 0, 1, 2, 4],
....:                            [0, 0, 1, 3, 2, 5]]))
sage: sorted(M.cross_ratios())
[2, 3, 4, 5, 6]
sage: M = Matroid(graphs.CompleteGraph(5), regular=True)                    # needs sage.graphs
sage: M.cross_ratios()                                                      # needs sage.graphs
set()

dual()#

Return the dual of the matroid.

Let \(M\) be a matroid with ground set \(E\). If \(B\) is the set of bases of \(M\), then the set \(\{E - b : b \in B\}\) is the set of bases of another matroid, the dual of \(M\).

If the matroid is represented by \([I_1\ \ A]\), then the dual is represented by \([-A^T\ \ I_2]\) for appropriately sized identity matrices \(I_1, I_2\).

OUTPUT:

The dual matroid.

EXAMPLES:

sage: A = Matrix(GF(7), [[1, 1, 0, 1],
....:                    [1, 0, 1, 1],
....:                    [0, 1, 1, 1]])
sage: B = - A.transpose()
sage: Matroid(reduced_matrix=A).dual() == Matroid(
....:                             reduced_matrix=B,
....:                             groundset=[3, 4, 5, 6, 0, 1, 2])
True

fundamental_cocycle(B, e)#

Return the fundamental cycle, relative to B, containing element e.

This is the fundamental cocircuit together with an appropriate signing from the field, such that \(Av=0\), where \(A\) is a representation matrix of the dual, and \(v\) the vector corresponding to the output.

INPUT:

B – a basis of the matroid
e – an element of the basis

OUTPUT:

A dictionary mapping elements of M.fundamental_cocircuit(B, e) to elements of the ring.

See also

M.fundamental_cocircuit()

EXAMPLES:

sage: M = Matroid(Matrix(GF(7), [[1, 0, 1, 1], [0, 1, 1, 4]]))
sage: v = M.fundamental_cocycle([0, 1], 0)
sage: [v[0], v[2], v[3]]
[1, 1, 1]
sage: frozenset(v.keys()) == M.fundamental_cocircuit([0, 1], 0)
True

fundamental_cycle(B, e)#

Return the fundamental cycle, relative to B, containing element e.

This is the fundamental circuit together with an appropriate signing from the field, such that \(Av=0\), where \(A\) is the representation matrix, and \(v\) the vector corresponding to the output.

INPUT:

B – a basis of the matroid
e – an element outside the basis

OUTPUT:

A dictionary mapping elements of M.fundamental_circuit(B, e) to elements of the ring.

See also

M.fundamental_circuit()

EXAMPLES:

sage: M = Matroid(Matrix(GF(7), [[1, 0, 1, 1], [0, 1, 1, 4]]))
sage: v = M.fundamental_cycle([0, 1], 3)
sage: [v[0], v[1], v[3]]
[6, 3, 1]
sage: frozenset(v.keys()) == M.fundamental_circuit([0, 1], 3)
True

has_field_minor(N)#

Check if self has a minor field isomorphic to N.

INPUT:

N – A matroid.

OUTPUT:

Boolean.

See also

M.minor(), M.is_field_isomorphic()

Todo

This important method can (and should) be optimized considerably. See [Hli2006] p.1219 for hints to that end.

EXAMPLES:

sage: M = matroids.Whirl(3)
sage: matroids.catalog.Fano().has_field_minor(M)
False
sage: matroids.catalog.NonFano().has_field_minor(M)
True

has_line_minor(k, hyperlines=None, certificate=False)#

Test if the matroid has a \(U_{2, k}\)-minor.

The matroid \(U_{2, k}\) is a matroid on \(k\) elements in which every subset of at most 2 elements is independent, and every subset of more than two elements is dependent.

The optional argument hyperlines restricts the search space: this method returns True if \(si(M/F)\) is isomorphic to \(U_{2, l}\) with \(l \geq k\) for some \(F\) in hyperlines, and False otherwise.

INPUT:

k – the length of the line minor
hyperlines – (default: None) a set of flats of codimension 2. Defaults to the set of all flats of codimension 2.
certificate (default: False); If True returns True, F, where F is a flat and self.minor(contractions=F) has a \(U_{2,k}\) restriction or False, None.

OUTPUT:

Boolean or tuple.

EXAMPLES:

sage: M = matroids.catalog.N1()
sage: M.has_line_minor(4)
True
sage: M.has_line_minor(5)
False
sage: M.has_line_minor(k=4, hyperlines=[['a', 'b', 'c']])
False
sage: M.has_line_minor(k=4, hyperlines=[['a', 'b', 'c'],
....:                                   ['a', 'b', 'd' ]])
True
sage: M.has_line_minor(4, certificate=True)
(True, frozenset({'a', 'b', 'd'}))
sage: M.has_line_minor(5, certificate=True)
(False, None)
sage: M.has_line_minor(k=4, hyperlines=[['a', 'b', 'c'],
....:                                   ['a', 'b', 'd' ]], certificate=True)
(True, frozenset({'a', 'b', 'd'}))

is_field_equivalent(other)#

Test for matroid representation equality.

Two linear matroids \(M\) and \(N\) with representation matrices \(A\) and \(B\) are field equivalent if they have the same groundset, and the identity map between the groundsets is an isomorphism between the representations \(A\) and \(B\). That is, one can be turned into the other using only row operations and column scaling.

INPUT:

other – A matroid.

OUTPUT:

Boolean.

EXAMPLES:

A BinaryMatroid and LinearMatroid use different representations of the matroid internally, so `` == `` yields False, even if the matroids are equal:

sage: from sage.matroids.advanced import *
sage: M = matroids.catalog.Fano()
sage: M1 = LinearMatroid(Matrix(M), groundset=M.groundset_list())
sage: M2 = Matroid(groundset='abcdefg',
....:              reduced_matrix=[[0, 1, 1, 1],
....:                              [1, 0, 1, 1],
....:                              [1, 1, 0, 1]], field=GF(2))
sage: M.equals(M1)
True
sage: M.equals(M2)
True
sage: M.is_field_equivalent(M1)
True
sage: M.is_field_equivalent(M2)
True
sage: M == M1
False
sage: M == M2
True

LinearMatroid instances M and N satisfy M == N if the representations are equivalent up to row operations and column scaling:

sage: M1 = Matroid(groundset='abcd',
....:          matrix=Matrix(GF(7), [[1, 0, 1, 1], [0, 1, 1, 2]]))
sage: M2 = Matroid(groundset='abcd',
....:          matrix=Matrix(GF(7), [[1, 0, 1, 1], [0, 1, 1, 3]]))
sage: M3 = Matroid(groundset='abcd',
....:          matrix=Matrix(GF(7), [[2, 6, 1, 0], [6, 1, 0, 1]]))
sage: M1.equals(M2)
True
sage: M1.equals(M3)
True
sage: M1 == M2
False
sage: M1 == M3
True
sage: M1.is_field_equivalent(M2)
False
sage: M1.is_field_equivalent(M3)
True
sage: M1.is_field_equivalent(M1)
True

is_field_isomorphic(other)#

Test isomorphism between matroid representations.

Two represented matroids are field isomorphic if there is a bijection between their groundsets that induces a field equivalence between their representation matrices: the matrices are equal up to row operations and column scaling. This implies that the matroids are isomorphic, but the converse is false: two isomorphic matroids can be represented by matrices that are not field equivalent.

INPUT:

other – A matroid.

OUTPUT:

Boolean.

EXAMPLES:

sage: M1 = matroids.Wheel(3)
sage: M2 = Matroid(graphs.CompleteGraph(4), regular=True)                   # needs sage.graphs
sage: M1.is_field_isomorphic(M2)                                            # needs sage.graphs
True
sage: M3 = Matroid(bases=M1.bases())
sage: M1.is_field_isomorphic(M3)
Traceback (most recent call last):
...
AttributeError: 'sage.matroids.basis_matroid.BasisMatroid' object
has no attribute 'base_ring'...
sage: from sage.matroids.advanced import *
sage: M4 = BinaryMatroid(Matrix(M1))
sage: M5 = LinearMatroid(reduced_matrix=Matrix(GF(2), [[-1, 0, 1],
....:                                    [1, -1, 0], [0, 1, -1]]))
sage: M4.is_field_isomorphic(M5)
True

sage: M1 = Matroid(groundset=[0, 1, 2, 3], matrix=Matrix(GF(7),
....:                               [[1, 0, 1, 1], [0, 1, 1, 2]]))
sage: M2 = Matroid(groundset=[0, 1, 2, 3], matrix=Matrix(GF(7),
....:                               [[1, 0, 1, 1], [0, 1, 2, 1]]))
sage: M1.is_field_isomorphic(M2)
True
sage: M1.is_field_equivalent(M2)
False

is_field_isomorphism(other, morphism)#

Test if a provided morphism induces a bijection between represented matroids.

Two represented matroids are field isomorphic if the bijection morphism between them induces a field equivalence between their representation matrices: the matrices are equal up to row operations and column scaling. This implies that the matroids are isomorphic, but the converse is false: two isomorphic matroids can be represented by matrices that are not field equivalent.

INPUT:

other – A matroid.
morphism – A map from the groundset of self to the groundset of other. See documentation of the M.is_isomorphism() method for more on what is accepted as input.

OUTPUT:

Boolean.

EXAMPLES:

sage: M = matroids.catalog.Fano()
sage: N = matroids.catalog.NonFano()
sage: N.is_field_isomorphism(M, {e:e for e in M.groundset()})
False

sage: from sage.matroids.advanced import *
sage: M = matroids.catalog.Fano().delete(['g'])
sage: N = LinearMatroid(reduced_matrix=Matrix(GF(2),
....:                       [[-1, 0, 1], [1, -1, 0], [0, 1, -1]]))
sage: morphism = {'a':0, 'b':1, 'c': 2, 'd':4, 'e':5, 'f':3}
sage: M.is_field_isomorphism(N, morphism)
True

sage: M1 = Matroid(groundset=[0, 1, 2, 3], matrix=Matrix(GF(7),
....:                               [[1, 0, 1, 1], [0, 1, 1, 2]]))
sage: M2 = Matroid(groundset=[0, 1, 2, 3], matrix=Matrix(GF(7),
....:                               [[1, 0, 1, 1], [0, 1, 2, 1]]))
sage: mf1 = {0:0, 1:1, 2:2, 3:3}
sage: mf2 = {0:0, 1:1, 2:3, 3:2}
sage: M1.is_field_isomorphism(M2, mf1)
False
sage: M1.is_field_isomorphism(M2, mf2)
True

is_valid()#

Test if the data represent an actual matroid.

Since this matroid is linear, we test the representation matrix.

OUTPUT:

True if the matrix is over a field.
True if the matrix is over a ring and all cross ratios are invertible.
False otherwise.

Note

This function does NOT test if the cross ratios are contained in the appropriate set of fundamentals. To that end, use

M.cross_ratios().issubset(F)

where F is the set of fundamentals.

See also

M.cross_ratios()

EXAMPLES:

sage: M = Matroid(ring=QQ, reduced_matrix=Matrix(ZZ,
....:                          [[1, 0, 1], [1, 1, 0], [0, 1, 1]]))
sage: M.is_valid()
True
sage: from sage.matroids.advanced import *  # LinearMatroid
sage: M = LinearMatroid(ring=ZZ, reduced_matrix=Matrix(ZZ,
....:                          [[1, 0, 1], [1, 1, 0], [0, 1, 1]]))
sage: M.is_valid()
False

linear_coextension(element, cochain=None, row=None)#

Return a linear coextension of this matroid.

A linear coextension of the represented matroid \(M\) by element \(e\) is a matroid represented by

\[\begin{split}\begin{bmatrix} A & 0\\ -c & 1 \end{bmatrix},\end{split}\]

where \(A\) is a representation matrix of \(M\), \(c\) is a new row, and the last column is labeled by \(e\).

This is the dual method of M.linear_extension().

INPUT:

element – the name of the new element.
row – (default: None) a row to be appended to self.representation(). Can be any iterable.
cochain – (default: None) a dictionary that maps elements of the ground set to elements of the base ring.

OUTPUT:

A linear matroid \(N = M([A 0; -c 1])\), where \(A\) is a matrix such that the current matroid is \(M[A]\), and \(c\) is either given by row (relative to self.representation()) or has nonzero entries given by cochain.

Note

The minus sign is to ensure this method commutes with dualizing. See the last example.

EXAMPLES:

sage: M = Matroid(ring=GF(2), matrix=[[1, 1, 0, 1, 0, 0],
....:                                 [1, 0, 1, 0, 1, 0],
....:                                 [0, 1, 1, 0, 0, 1],
....:                                 [0, 0, 0, 1, 1, 1]])
sage: M.linear_coextension(6, {0:1, 5: 1}).representation()
[1 1 0 1 0 0 0]
[1 0 1 0 1 0 0]
[0 1 1 0 0 1 0]
[0 0 0 1 1 1 0]
[1 0 0 0 0 1 1]
sage: M.linear_coextension(6, row=[0,1,1,1,0,1]).representation()
[1 1 0 1 0 0 0]
[1 0 1 0 1 0 0]
[0 1 1 0 0 1 0]
[0 0 0 1 1 1 0]
[0 1 1 1 0 1 1]

Coextending commutes with dualizing:

sage: M = matroids.catalog.NonFano()
sage: chain = {'a': 1, 'b': -1, 'f': 1}
sage: M1 = M.linear_coextension('x', chain)
sage: M2 = M.dual().linear_extension('x', chain)
sage: M1 == M2.dual()
True

linear_coextension_cochains(F=None, cosimple=False, fundamentals=None)#

Create a list of cochains that determine the single-element coextensions of this linear matroid representation.

A cochain is a dictionary, mapping elements from the groundset to elements of the base ring. If \(A\) represents the current matroid, then the coextension is given by \(N = M([A 0; -c 1])\), with the entries of \(c\) given by the cochain. Note that the matroid does not change when row operations are carried out on \(A\).

INPUT:

F – (default: self.groundset()) a subset of the groundset.
cosimple – (default: False) a boolean variable.
fundamentals – (default: None) a set elements of the base ring.

OUTPUT:

A list of cochains, so each single-element coextension of this linear matroid representation is given by one of these cochains.

If one or more of the above inputs is given, the list is restricted to chains

so that the support of each cochain lies in F, if given
so that the cochain does not generate a series extension or coloop, if cosimple = True
so that in the coextension generated by this cochain, the cross ratios are restricted to fundamentals, if given.

EXAMPLES:

sage: M = Matroid(reduced_matrix=Matrix(GF(2),
....:                          [[1, 1, 0], [1, 0, 1], [0, 1, 1]]))
sage: len(M.linear_coextension_cochains())
8
sage: len(M.linear_coextension_cochains(F=[0, 1]))
4
sage: len(M.linear_coextension_cochains(F=[0, 1], cosimple=True))
0
sage: M.linear_coextension_cochains(F=[3, 4, 5], cosimple=True)
[{3: 1, 4: 1, 5: 1}]
sage: N = Matroid(ring=QQ,
....:         reduced_matrix=[[-1, -1, 0], [1, 0, -1], [0, 1, 1]])
sage: N.linear_coextension_cochains(F=[0, 1], cosimple=True,
....:                           fundamentals=set([1, -1, 1/2, 2]))
[{0: 2, 1: 1}, {0: -1, 1: 1}, {0: 1/2, 1: 1}]

linear_coextensions(element=None, F=None, cosimple=False, fundamentals=None)#

Create a list of linear matroids represented by corank-preserving single-element coextensions of this linear matroid representation.

INPUT:

element – (default: None) the name of the new element of the groundset.
F – (default: None) a subset of the ground set.
cosimple – (default: False) a boolean variable.
fundamentals – (default: None) a set elements of the base ring.

OUTPUT:

A list of linear matroids represented by corank-preserving single-element coextensions of this linear matroid representation. In particular, the coextension by a loop is not generated.

If one or more of the above inputs is given, the list is restricted to coextensions

so that the new element lies in the cospan of F, if given.
so that the new element is no coloop and is not in series with another element, if cosimple = True.
so that in the representation of the coextension, the cross ratios are restricted to fundamentals, if given. Note that it is assumed that the cross ratios of the input matroid already satisfy this condition.

EXAMPLES:

sage: M = Matroid(ring=GF(2),
....:         reduced_matrix=[[-1, 0, 1], [1, -1, 0], [0, 1, -1]])
sage: len(M.linear_coextensions())
8
sage: S = M.linear_coextensions(cosimple=True)
sage: S
[Binary matroid of rank 4 on 7 elements, type (3, 7)]
sage: F7 = matroids.catalog.Fano()
sage: S[0].is_field_isomorphic(F7.dual())
True
sage: M = Matroid(ring=QQ,
....:            reduced_matrix=[[1, 0, 1], [1, 1, 0], [0, 1, 1]])
sage: S = M.linear_coextensions(cosimple=True,
....:                           fundamentals=[1, -1, 1/2, 2])
sage: len(S)
7
sage: NF7 = matroids.catalog.NonFano()
sage: any(N.is_isomorphic(NF7.dual()) for N in S)
True
sage: len(M.linear_coextensions(cosimple=True,
....:                           fundamentals=[1, -1, 1/2, 2],
....:                           F=[3, 4]))
1

linear_extension(element, chain=None, col=None)#

Return a linear extension of this matroid.

A linear extension of the represented matroid \(M\) by element \(e\) is a matroid represented by \([A\ \ b]\), where \(A\) is a representation matrix of \(M\) and \(b\) is a new column labeled by \(e\).

INPUT:

element – the name of the new element.
col – (default: None) a column to be appended to self.representation(). Can be any iterable.
chain – (default: None) a dictionary that maps elements of the ground set to elements of the base ring.

OUTPUT:

A linear matroid \(N = M([A\ \ b])\), where \(A\) is a matrix such that the current matroid is \(M[A]\), and \(b\) is either given by col or is a weighted combination of columns of \(A\), the weights being given by chain.

See also

M.extension().

EXAMPLES:

sage: M = Matroid(ring=GF(2), matrix=[[1, 1, 0, 1, 0, 0],
....:                                 [1, 0, 1, 0, 1, 0],
....:                                 [0, 1, 1, 0, 0, 1],
....:                                 [0, 0, 0, 1, 1, 1]])
sage: M.linear_extension(6, {0:1, 5: 1}).representation()
[1 1 0 1 0 0 1]
[1 0 1 0 1 0 1]
[0 1 1 0 0 1 1]
[0 0 0 1 1 1 1]
sage: M.linear_extension(6, col=[0, 1, 1, 1]).representation()
[1 1 0 1 0 0 0]
[1 0 1 0 1 0 1]
[0 1 1 0 0 1 1]
[0 0 0 1 1 1 1]

linear_extension_chains(F=None, simple=False, fundamentals=None)#

Create a list of chains that determine the single-element extensions of this linear matroid representation.

A chain is a dictionary, mapping elements from the groundset to elements of the base ring, indicating a linear combination of columns to form the new column. Think of chains as vectors, only independent of representation.

INPUT:

F – (default: self.groundset()) a subset of the groundset.
simple – (default: False) a boolean variable.
fundamentals – (default: None) a set elements of the base ring.

OUTPUT:

A list of chains, so each single-element extension of this linear matroid representation is given by one of these chains.

If one or more of the above inputs is given, the list is restricted to chains

so that the support of each chain lies in F, if given
so that the chain does not generate a parallel extension or loop, if simple = True
so that in the extension generated by this chain, the cross ratios are restricted to fundamentals, if given.

EXAMPLES:

sage: M = Matroid(reduced_matrix=Matrix(GF(2),
....:                          [[1, 1, 0], [1, 0, 1], [0, 1, 1]]))
sage: len(M.linear_extension_chains())
8
sage: len(M.linear_extension_chains(F=[0, 1]))
4
sage: len(M.linear_extension_chains(F=[0, 1], simple=True))
0
sage: M.linear_extension_chains(F=[0, 1, 2], simple=True)
[{0: 1, 1: 1, 2: 1}]
sage: N = Matroid(ring=QQ,
....:         reduced_matrix=[[-1, -1, 0], [1, 0, -1], [0, 1, 1]])
sage: L = N.linear_extension_chains(F=[0, 1], simple=True,
....:                           fundamentals=set([1, -1, 1/2, 2]))
sage: result = [{0: 1, 1: 1}, {0: -1/2, 1: 1}, {0: -2, 1: 1}]
sage: all(D in L for D in result)
True

linear_extensions(element=None, F=None, simple=False, fundamentals=None)#

Create a list of linear matroids represented by rank-preserving single-element extensions of this linear matroid representation.

INPUT:

element – (default: None) the name of the new element of the groundset.
F – (default: None) a subset of the ground set.
simple – (default: False) a boolean variable.
fundamentals – (default: None) a set elements of the base ring.

OUTPUT:

A list of linear matroids represented by rank-preserving single-element extensions of this linear matroid representation. In particular, the extension by a coloop is not generated.

If one or more of the above inputs is given, the list is restricted to matroids

so that the new element is spanned by F, if given
so that the new element is not a loop or in a parallel pair, if simple=True
so that in the representation of the extension, the cross ratios are restricted to fundamentals, if given. Note that it is assumed that the cross ratios of the input matroid already satisfy this condition.

EXAMPLES:

sage: M = Matroid(ring=GF(2),
....:             reduced_matrix=[[-1, 0, 1], [1, -1, 0], [0, 1, -1]])
sage: len(M.linear_extensions())
8
sage: S = M.linear_extensions(simple=True); S
[Binary matroid of rank 3 on 7 elements, type (3, 0)]
sage: S[0].is_field_isomorphic(matroids.catalog.Fano())
True
sage: M = Matroid(ring=QQ,
....:             reduced_matrix=[[1, 0, 1], [1, 1, 0], [0, 1, 1]])
sage: S = M.linear_extensions(simple=True,
....:                         fundamentals=[1, -1, 1/2, 2])
sage: len(S)
7
sage: any(N.is_isomorphic(matroids.catalog.NonFano())
....:     for N in S)
True
sage: len(M.linear_extensions(simple=True,
....:                         fundamentals=[1, -1, 1/2, 2], F=[0, 1]))
1

orlik_terao_algebra(R=None, ordering=None, **kwargs)#

Return the Orlik-Terao algebra of self.

INPUT:

R – (default: the base ring of self) the base ring
ordering – (optional) an ordering of the ground set

See also

OrlikTeraoAlgebra

EXAMPLES:

sage: M = matroids.Wheel(3)
sage: OS = M.orlik_terao_algebra(); OS
Orlik-Terao algebra of Wheel(3):
 Regular matroid of rank 3 on 6 elements with 16 bases
 over Integer Ring
sage: OS.base_ring()
Integer Ring
sage: M.orlik_terao_algebra(QQ).base_ring()
Rational Field

sage: G = SymmetricGroup(3);                                                # needs sage.groups
sage: OTG = M.orlik_terao_algebra(QQ, invariant=G)                          # needs sage.groups

sage: # needs sage.groups
sage: G = SymmetricGroup(4)
sage: action = lambda g, x: g(x + 1) - 1
sage: OTG1 = M.orlik_terao_algebra(QQ, invariant=(G,action))
sage: OTG2 = M.orlik_terao_algebra(QQ, invariant=(action,G))
sage: OTG1 is OTG2
True

representation(B=None, reduced=False, labels=None, order=None, lift_map=None)#

Return a matrix representing the matroid.

Let \(M\) be a matroid on \(n\) elements with rank \(r\). Let \(E\) be an ordering of the groundset, as output by M.groundset_list(). A representation of the matroid is an \(r \times n\) matrix with the following property. Consider column \(i\) to be labeled by \(E[i]\), and denote by \(A[F]\) the submatrix formed by the columns labeled by the subset \(F \subseteq E\). Then for all \(F \subseteq E\), the columns of \(A[F]\) are linearly independent if and only if \(F\) is an independent set in the matroid.

A reduced representation is a matrix \(D\) such that \([I\ \ D]\) is a representation of the matroid, where \(I\) is an \(r \times r\) identity matrix. In this case, the rows of \(D\) are considered to be labeled by the first \(r\) elements of the list E, and the columns by the remaining \(n - r\) elements.

INPUT:

B – (default: None) a subset of elements. When provided, the representation is such that a basis \(B'\) that maximally intersects \(B\) is an identity matrix.
reduced – (default: False) when True, return a reduced matrix \(D\) (so \([I\ \ D]\) is a representation of the matroid). Otherwise return a full representation matrix.
labels – (default: None) when True, return additionally a list of column labels (if reduced=False) or a list of row labels and a list of column labels (if reduced=True). The default setting, None, will not return the labels for a full matrix, but will return the labels for a reduced matrix.
order – (default: None) an ordering of the groundset elements. If provided, the columns (and, in case of a reduced representation, rows) will be presented in the given order.
lift_map – (default: None) a dictionary containing the cross ratios of the representing matrix in its domain. If provided, the representation will be transformed by mapping its cross ratios according to lift_map.

OUTPUT:

A – a full or reduced representation matrix of self; or
(A, E) – a full representation matrix A and a list E of column labels; or
(A, R, C) – a reduced representation matrix and a list R of row labels and a list C of column labels.

If B == None and reduced == False and order == None then this method will always output the same matrix (except when M._forget() is called): either the matrix used as input to create the matroid, or a matrix in which the lexicographically least basis corresponds to an identity. If only order is not None, the columns of this matrix will be permuted accordingly.

If a lift_map is provided, then the resulting matrix will be lifted using the method lift_cross_ratios() See the docstring of this method for further details.

Note

A shortcut for M.representation() is Matrix(M).

EXAMPLES:

sage: M = matroids.catalog.Fano()
sage: M.representation()
[1 0 0 0 1 1 1]
[0 1 0 1 0 1 1]
[0 0 1 1 1 0 1]
sage: Matrix(M) == M.representation()
True
sage: M.representation(labels=True)
(
[1 0 0 0 1 1 1]
[0 1 0 1 0 1 1]
[0 0 1 1 1 0 1], ['a', 'b', 'c', 'd', 'e', 'f', 'g']
)
sage: M.representation(B='efg')
[1 1 0 1 1 0 0]
[1 0 1 1 0 1 0]
[1 1 1 0 0 0 1]
sage: M.representation(B='efg', order='efgabcd')
[1 0 0 1 1 0 1]
[0 1 0 1 0 1 1]
[0 0 1 1 1 1 0]
sage: M.representation(B='abc', reduced=True)
(
[0 1 1 1]
[1 0 1 1]
[1 1 0 1], ['a', 'b', 'c'], ['d', 'e', 'f', 'g']
)
sage: M.representation(B='efg', reduced=True, labels=False,
....:                  order='gfeabcd')
[1 1 1 0]
[1 0 1 1]
[1 1 0 1]

sage: from sage.matroids.advanced import lift_cross_ratios, lift_map, LinearMatroid
sage: R = GF(7)
sage: A = Matrix(R, [[1, 0, 6, 1, 2],[6, 1, 0, 0, 1],[0, 6, 3, 6, 0]])
sage: M = LinearMatroid(reduced_matrix=A)
sage: M.representation(lift_map=lift_map('sru'))                            # needs sage.rings.finite_rings
[     1      0      0      1      0      1      1      1]
[     0      1      0 -z + 1      1      0      0      1]
[     0      0      1      0      1      -1 z - 1      0]

representation_vectors()#

Return a dictionary that associates a column vector with each element of the matroid.

See also

M.representation()

EXAMPLES:

sage: M = matroids.catalog.Fano()
sage: E = M.groundset_list()
sage: [M.representation_vectors()[e] for e in E]
[(1, 0, 0), (0, 1, 0), (0, 0, 1), (0, 1, 1), (1, 0, 1), (1, 1, 0),
 (1, 1, 1)]

class sage.matroids.linear_matroid.QuaternaryMatroid#

Bases: LinearMatroid

Quaternary matroids.

A quaternary matroid is a linear matroid represented over the finite field with four elements. See LinearMatroid for a definition.

The simplest way to create a QuaternaryMatroid is by giving only a matrix \(A\). Then, the groundset defaults to range(A.ncols()). Any iterable object \(E\) can be given as a groundset. If \(E\) is a list, then E[i] will label the \(i\)-th column of \(A\). Another possibility is to specify a ‘reduced’ matrix \(B\), to create the matroid induced by \(A = [I\ \ B]\).

INPUT:

matrix – (default: None) a matrix whose column vectors represent the matroid.
reduced_matrix – (default: None) a matrix \(B\) such that \([I\ \ B]\) represents the matroid, where \(I\) is an identity matrix with the same number of rows as \(B\). Only one of matrix and reduced_matrix should be provided.
groundset – (default: None) an iterable containing the element labels. When provided, must have the correct number of elements: the number of columns of matrix or the number of rows plus the number of columns of reduced_matrix.
ring – (default: None) must be a copy of \(\GF{4}\).
keep_initial_representation – (default: True) boolean. Decides whether or not an internal copy of the input matrix should be preserved. This can help to see the structure of the matroid (e.g. in the case of graphic matroids), and makes it easier to look at extensions. However, the input matrix may have redundant rows, and sometimes it is desirable to store only a row-reduced copy.
basis – (default: None) When provided, this is an ordered subset of groundset, such that the submatrix of matrix indexed by basis is an identity matrix. In this case, no row reduction takes place in the initialization phase.

OUTPUT:

A QuaternaryMatroid instance based on the data above.

Note

The recommended way to generate a quaternary matroid is through the Matroid() function. This is usually the preferred way, since it automatically chooses between QuaternaryMatroid and other classes. For direct access to the QuaternaryMatroid constructor, run:

sage: from sage.matroids.advanced import *

EXAMPLES:

sage: # needs sage.rings.finite_rings
sage: GF4 = GF(4, 'x')
sage: x = GF4.gens()[0]
sage: A = Matrix(GF4, 2, 4, [[1, 0, 1, 1], [0, 1, 1, x]])
sage: M = Matroid(A)
sage: M
Quaternary matroid of rank 2 on 4 elements
sage: sorted(M.groundset())
[0, 1, 2, 3]
sage: Matrix(M)
[1 0 1 1]
[0 1 1 x]
sage: M = Matroid(matrix=A, groundset='abcd')
sage: sorted(M.groundset())
['a', 'b', 'c', 'd']
sage: GF4p = GF(4, 'y')
sage: y = GF4p.gens()[0]
sage: B = Matrix(GF4p, 2, 2, [[1, 1], [1, y]])
sage: N = Matroid(reduced_matrix=B, groundset='abcd')
sage: M == N
False

base_ring()#

Return the base ring of the matrix representing the matroid, in this case \(\GF{4}\).

EXAMPLES:

sage: M = Matroid(ring=GF(4, 'y'), reduced_matrix=[[1, 0, 1],               # needs sage.rings.finite_rings
....:                                              [0, 1, 1]])
sage: M.base_ring()                                                         # needs sage.rings.finite_rings
Finite Field in y of size 2^2

bicycle_dimension()#

Return the bicycle dimension of the quaternary matroid.

The bicycle dimension of a linear subspace \(V\) is \(\dim(V\cap V^\perp)\). We use the inner product \(< v, w >=v_1 w_1^* + ... + v_n w_n^*\), where \(w_i^*\) is obtained from \(w_i\) by applying the unique nontrivial field automorphism of \(\GF{4}\).

The bicycle dimension of a matroid equals the bicycle dimension of its rowspace, and is a matroid invariant. See [Pen2012].

OUTPUT:

Integer.

EXAMPLES:

sage: M = matroids.catalog.Q10()                                     # needs sage.rings.finite_rings
sage: M.bicycle_dimension()                                                 # needs sage.rings.finite_rings
0

characteristic()#

Return the characteristic of the base ring of the matrix representing the matroid, in this case \(2\).

EXAMPLES:

sage: M = Matroid(ring=GF(4, 'y'), reduced_matrix=[[1, 0, 1],               # needs sage.rings.finite_rings
....:                                              [0, 1, 1]])
sage: M.characteristic()                                                    # needs sage.rings.finite_rings
2

is_valid()#

Test if the data obey the matroid axioms.

Since this is a linear matroid over the field \(\GF{4}\), this is always the case.

OUTPUT:

True.

EXAMPLES:

sage: M = Matroid(Matrix(GF(4, 'x'), [[]]))                                 # needs sage.rings.finite_rings
sage: M.is_valid()                                                          # needs sage.rings.finite_rings
True

class sage.matroids.linear_matroid.RegularMatroid#

Bases: LinearMatroid

Regular matroids.

A regular matroid is a linear matroid represented over the integers by a totally unimodular matrix.

The simplest way to create a RegularMatroid is by giving only a matrix \(A\). Then, the groundset defaults to range(A.ncols()). Any iterable object \(E\) can be given as a groundset. If \(E\) is a list, then E[i] will label the \(i\)-th column of \(A\). Another possibility is to specify a ‘reduced’ matrix \(B\), to create the matroid induced by \(A = [ I B ]\).

INPUT:

matrix – (default: None) a matrix whose column vectors represent the matroid.
reduced_matrix – (default: None) a matrix \(B\) such that \([I\ \ B]\) represents the matroid, where \(I\) is an identity matrix with the same number of rows as \(B\). Only one of matrix and reduced_matrix should be provided.
groundset – (default: None) an iterable containing the element labels. When provided, must have the correct number of elements: the number of columns of matrix or the number of rows plus the number of columns of reduced_matrix.
ring – (default: None) ignored.
keep_initial_representation – (default: True) boolean. Decides whether or not an internal copy of the input matrix should be preserved. This can help to see the structure of the matroid (e.g. in the case of graphic matroids), and makes it easier to look at extensions. However, the input matrix may have redundant rows, and sometimes it is desirable to store only a row-reduced copy.
basis – (default: None) when provided, this is an ordered subset of groundset, such that the submatrix of matrix indexed by basis is an identity matrix. In this case, no row reduction takes place in the initialization phase.

OUTPUT:

A RegularMatroid instance based on the data above.

Note

The recommended way to generate a regular matroid is through the Matroid() function. This is usually the preferred way, since it automatically chooses between RegularMatroid and other classes. Moreover, it will test whether the input actually yields a regular matroid, unlike this class. For direct access to the RegularMatroid constructor, run:

sage: from sage.matroids.advanced import *

Warning

No checks are performed to ensure the input data form an actual regular matroid! If not, the behavior is unpredictable, and the internal representation can get corrupted. If in doubt, run self.is_valid() to ensure the data are as desired.

EXAMPLES:

sage: A = Matrix(ZZ, 2, 4, [[1, 0, 1, 1], [0, 1, 1, 1]])
sage: M = Matroid(A, regular=True); M                                           # needs sage.graphs
Regular matroid of rank 2 on 4 elements with 5 bases
sage: sorted(M.groundset())                                                     # needs sage.graphs
[0, 1, 2, 3]
sage: Matrix(M)                                                                 # needs sage.graphs
[1 0 1 1]
[0 1 1 1]
sage: M = Matroid(matrix=A, groundset='abcd', regular=True)                     # needs sage.graphs
sage: sorted(M.groundset())                                                     # needs sage.graphs
['a', 'b', 'c', 'd']

base_ring()#

Return the base ring of the matrix representing the matroid, in this case \(\ZZ\).

EXAMPLES:

sage: M = matroids.catalog.R10()
sage: M.base_ring()
Integer Ring

bases_count()#

Count the number of bases.

EXAMPLES:

sage: M = Matroid(graphs.CompleteGraph(5), regular=True)                    # needs sage.graphs
sage: M.bases_count()                                                       # needs sage.graphs
125

ALGORITHM:

Since the matroid is regular, we use Kirchhoff’s Matrix-Tree Theorem. See also Wikipedia article Kirchhoff%27s_theorem.

binary_matroid(randomized_tests=1, verify=True)#

Return a binary matroid representing self.

INPUT:

randomized_tests – Ignored.
verify – Ignored

OUTPUT:

A binary matroid.

ALGORITHM:

self is a regular matroid, so just cast self to a BinaryMatroid.

See also

M.binary_matroid()

EXAMPLES:

sage: N = matroids.catalog.R10()
sage: N.binary_matroid()
Binary matroid of rank 5 on 10 elements, type (1, None)

characteristic()#

Return the characteristic of the base ring of the matrix representing the matroid, in this case \(0\).

EXAMPLES:

sage: M = matroids.catalog.R10()
sage: M.characteristic()
0

has_line_minor(k, hyperlines=None, certificate=False)#

Test if the matroid has a \(U_{2, k}\)-minor.

The matroid \(U_{2, k}\) is a matroid on \(k\) elements in which every subset of at most 2 elements is independent, and every subset of more than two elements is dependent.

INPUT:

k – the length of the line minor
hyperlines – (default: None) a set of flats of codimension 2. Defaults to the set of all flats of codimension 2.
certificate (default: False); If True returns True, F, where F is a flat and self.minor(contractions=F) has a \(U_{2,k}\) restriction or False, None.

OUTPUT:

Boolean or tuple.

See also

Matroid.has_minor()

EXAMPLES:

sage: M = matroids.catalog.R10()
sage: M.has_line_minor(4)
False
sage: M.has_line_minor(4, certificate=True)
(False, None)
sage: M.has_line_minor(3)
True
sage: M.has_line_minor(3, certificate=True)
(True, frozenset({'a', 'b', 'c', 'g'}))
sage: M.has_line_minor(k=3, hyperlines=[['a', 'b', 'c'],
....:                                   ['a', 'b', 'd' ]])
True
sage: M.has_line_minor(k=3, hyperlines=[['a', 'b', 'c'],
....:                                   ['a', 'b', 'd' ]], certificate=True)
(True, frozenset({'a', 'b', 'c'}))

is_binary(randomized_tests=1)#

Decide if self is a binary matroid.

INPUT:

randomized_tests – Ignored.

OUTPUT:

A Boolean.

ALGORITHM:

self is a regular matroid, so just return True.

See also

M.is_binary()

EXAMPLES:

sage: N = matroids.catalog.R10()
sage: N.is_binary()
True

is_graphic()#

Test if the regular matroid is graphic.

OUTPUT:

Boolean.

EXAMPLES:

sage: M = matroids.catalog.R10()
sage: M.is_graphic()
False
sage: M = Matroid(graphs.CompleteGraph(5), regular=True)                    # needs sage.graphs
sage: M.is_graphic()                                                        # needs sage.graphs sage.rings.finite_rings
True
sage: M.dual().is_graphic()                                                 # needs sage.graphs
False

ALGORITHM:

is_ternary(randomized_tests=1)#

Decide if self is a ternary matroid.

INPUT:

randomized_tests – Ignored.

OUTPUT:

A Boolean.

ALGORITHM:

self is a regular matroid, so just return True.

See also

M.is_ternary()

EXAMPLES:

sage: N = matroids.catalog.R10()
sage: N.is_ternary()
True

is_valid()#

Test if the data obey the matroid axioms.

Since this is a regular matroid, this function tests if the representation matrix is totally unimodular, i.e. if all square submatrices have determinant in \(\{-1, 0, 1\}\).

OUTPUT:

Boolean.

EXAMPLES:

sage: # needs sage.graphs
sage: M = Matroid(Matrix(ZZ, [[1, 0, 0, 1, 1, 0, 1],
....:                         [0, 1, 0, 1, 0, 1, 1],
....:                         [0, 0, 1, 0, 1, 1, 1]]),
....:             regular=True, check=False)
sage: M.is_valid()
False
sage: M = Matroid(graphs.PetersenGraph())
sage: M.is_valid()
True

ternary_matroid(randomized_tests=1, verify=True)#

Return a ternary matroid representing self.

INPUT:

randomized_tests – Ignored.
verify – Ignored

OUTPUT:

A ternary matroid.

ALGORITHM:

self is a regular matroid, so just cast self to a TernaryMatroid.

See also

M.ternary_matroid()

EXAMPLES:

sage: N = matroids.catalog.R10()
sage: N.ternary_matroid()
Ternary matroid of rank 5 on 10 elements, type 4+

class sage.matroids.linear_matroid.TernaryMatroid#

Bases: LinearMatroid

Ternary matroids.

A ternary matroid is a linear matroid represented over the finite field with three elements. See LinearMatroid for a definition.

The simplest way to create a TernaryMatroid is by giving only a matrix \(A\). Then, the groundset defaults to range(A.ncols()). Any iterable object \(E\) can be given as a groundset. If \(E\) is a list, then E[i] will label the \(i\)-th column of \(A\). Another possibility is to specify a ‘reduced’ matrix \(B\), to create the matroid induced by \(A = [I\ \ B]\).

INPUT:

matrix – (default: None) a matrix whose column vectors represent the matroid.
reduced_matrix – (default: None) a matrix \(B\) such that \([I\ \ B]\) represents the matroid, where \(I\) is an identity matrix with the same number of rows as \(B\). Only one of matrix and reduced_matrix should be provided.
groundset – (default: None) an iterable containing the element labels. When provided, must have the correct number of elements: the number of columns of matrix or the number of rows plus the number of columns of reduced_matrix.
ring – (default: None) ignored.
keep_initial_representation – (default: True) boolean. Decides whether or not an internal copy of the input matrix should be preserved. This can help to see the structure of the matroid (e.g. in the case of graphic matroids), and makes it easier to look at extensions. However, the input matrix may have redundant rows, and sometimes it is desirable to store only a row-reduced copy.
basis – (default: None) when provided, this is an ordered subset of groundset, such that the submatrix of matrix indexed by basis is an identity matrix. In this case, no row reduction takes place in the initialization phase.

OUTPUT:

A TernaryMatroid instance based on the data above.

Note

The recommended way to generate a ternary matroid is through the Matroid() function. This is usually the preferred way, since it automatically chooses between TernaryMatroid and other classes. For direct access to the TernaryMatroid constructor, run:

sage: from sage.matroids.advanced import *

EXAMPLES:

sage: A = Matrix(GF(3), 2, 4, [[1, 0, 1, 1], [0, 1, 1, 1]])
sage: M = Matroid(A); M
Ternary matroid of rank 2 on 4 elements, type 0-
sage: sorted(M.groundset())
[0, 1, 2, 3]
sage: Matrix(M)
[1 0 1 1]
[0 1 1 1]
sage: M = Matroid(matrix=A, groundset='abcd')
sage: sorted(M.groundset())
['a', 'b', 'c', 'd']
sage: B = Matrix(GF(2), 2, 2, [[1, 1], [1, 1]])
sage: N = Matroid(ring=GF(3), reduced_matrix=B, groundset='abcd')               # needs sage.rings.finite_rings
sage: M == N                                                                    # needs sage.rings.finite_rings
True

base_ring()#

Return the base ring of the matrix representing the matroid, in this case \(\GF{3}\).

EXAMPLES:

sage: M = matroids.catalog.NonFano()
sage: M.base_ring()
Finite Field of size 3

bicycle_dimension()#

Return the bicycle dimension of the ternary matroid.

The bicycle dimension of a linear subspace \(V\) is \(\dim(V\cap V^\perp)\). The bicycle dimension of a matroid equals the bicycle dimension of its rowspace, and is a matroid invariant. See [Pen2012].

OUTPUT:

Integer.

EXAMPLES:

sage: M = matroids.catalog.NonFano()
sage: M.bicycle_dimension()
0

character()#

Return the character of the ternary matroid.

For a linear subspace \(V\) over \(GF(3)\) with orthogonal basis \(q_1, \ldots, q_k\) the character equals the product of \(|q_i|\) modulo 3, where the product ranges over the \(i\) such that \(|q_i|\) is not divisible by 3. The character does not depend on the choice of the orthogonal basis. The character of a ternary matroid equals the character of its cocycle-space, and is an invariant for ternary matroids. See [Pen2012].

OUTPUT:

Integer.

EXAMPLES:

sage: M = matroids.catalog.NonFano()
sage: M.character()
2

characteristic()#

Return the characteristic of the base ring of the matrix representing the matroid, in this case \(3\).

EXAMPLES:

sage: M = matroids.catalog.NonFano()
sage: M.characteristic()
3

is_ternary(randomized_tests=1)#

Decide if self is a binary matroid.

INPUT:

randomized_tests – Ignored.

OUTPUT:

A Boolean.

ALGORITHM:

self is a ternary matroid, so just return True.

See also

M.is_ternary()

EXAMPLES:

sage: N = matroids.catalog.NonFano()
sage: N.is_ternary()
True

is_valid()#

Test if the data obey the matroid axioms.

Since this is a linear matroid over the field \(\GF{3}\), this is always the case.

OUTPUT:

True.

EXAMPLES:

sage: M = Matroid(Matrix(GF(3), [[]]))
sage: M.is_valid()
True

ternary_matroid(randomized_tests=1, verify=True)#

Return a ternary matroid representing self.

INPUT:

randomized_tests – Ignored.
verify – Ignored

OUTPUT:

A binary matroid.

ALGORITHM:

self is a ternary matroid, so just return self.

See also

M.ternary_matroid()

EXAMPLES:

sage: N = matroids.catalog.NonFano()
sage: N.ternary_matroid() is N
True