# Polyhedral Complexes

## Introduction

Let $\mathbb{F}$ be an ordered field; the default is that $\mathbb{F}=\mathbb{Q}$ is the field of rational numbers and other fields are not yet supported everywhere in the implementation.

A nonempty finite collection $\mathcal{P}$ of polyhedra in $\mathbb{F}^n$, for $n$ fixed, is a polyhedral complex if

• the set $\mathcal{F}$ is closed with respect to taking faces and
• if $C,D\in\mathcal{F}$ then $C\cap D$ is a face of both $C$ and $D$.

## Construction

To construct a polyhedral complex, you must pass points of each polyhedron in the polyhedral complex, such that the polyhedron is the convex hull thereof, along with an IncidenceMatrix encoding which points generate which polyhedron.

polyhedral_complexFunction
polyhedral_complex(::T, polyhedra, vr, far_vertices, L) where T<:scalar_types

Arguments

• T: Type or parent Field of scalar to use, defaults to QQFieldElem.
• polyhedra::IncidenceMatrix: An incidence matrix; there is a 1 at position (i,j) if the ith polytope contains point j and 0 otherwise.
• vr::AbstractCollection[PointVector]: The points whose convex hulls make up the polyhedral complex. This matrix also contains the far vertices.
• far_vertices::Vector{Int}: Vector containing the indices of the rows corresponding to the far vertices in vr.
• L::AbstractCollection[RayVector]: Generators of the lineality space of the polyhedral complex.

A polyhedral complex formed from points, rays, and lineality combined into polyhedra indicated by an incidence matrix, where the columns represent the points and the rows represent the polyhedra.

Examples

julia> IM = IncidenceMatrix([[1,2,3],[1,3,4]])
2×4 IncidenceMatrix
[1, 2, 3]
[1, 3, 4]

julia> vr = [0 0; 1 0; 1 1; 0 1]
4×2 Matrix{Int64}:
0  0
1  0
1  1
0  1

julia> PC = polyhedral_complex(IM, vr)
Polyhedral complex in ambient dimension 2

Polyhedral complex with rays and lineality:

julia> VR = [0 0 0; 1 0 0; 0 1 0; -1 0 0];

julia> IM = IncidenceMatrix([[1,2,3],[1,3,4]]);

julia> far_vertices = [2,3,4];

julia> L = [0 0 1];

julia> PC = polyhedral_complex(IM, VR, far_vertices, L)
Polyhedral complex in ambient dimension 3

julia> lineality_dim(PC)
1
source

## Auxiliary functions

ambient_dimMethod
ambient_dim(PC::PolyhedralComplex)

Return the ambient dimension of PC.

Examples

julia> IM = IncidenceMatrix([[1,2,3],[1,3,4]])
2×4 IncidenceMatrix
[1, 2, 3]
[1, 3, 4]

julia> V = [0 0; 1 0; 1 1; 0 1]
4×2 Matrix{Int64}:
0  0
1  0
1  1
0  1

julia> PC = polyhedral_complex(IM, V)
Polyhedral complex in ambient dimension 2

julia> ambient_dim(PC)
2
source
codimMethod
codim(PC::PolyhedralComplex)

Compute the codimension of a polyhedral complex.

Examples

julia> VR = [0 0; 1 0; -1 0; 0 1];

julia> IM = IncidenceMatrix([[1,2],[1,3],[1,4]]);

julia> far_vertices = [2,3,4];

julia> PC = polyhedral_complex(IM, VR, far_vertices)
A polyhedral complex in ambient dimension 2

julia> codim(PC)
1
source
dimMethod
dim(PC::PolyhedralComplex)

Compute the dimension of the polyhedral complex.

Examples

julia> IM = IncidenceMatrix([[1,2,3],[1,3,4]]);

julia> VR = [0 0; 1 0; 1 1; 0 1];

julia> PC = polyhedral_complex(IM, VR)
Polyhedral complex in ambient dimension 2

julia> dim(PC)
2
source
f_vectorMethod
f_vector(PC::PolyhedralComplex)

Compute the vector $(f₀,f₁,f₂,...,f_{dim(PC))$ where $f_i$ is the number of faces of $PC$ of dimension $i$.

Examples

julia> VR = [0 0; 1 0; -1 0; 0 1];

julia> IM = IncidenceMatrix([[1,2,4],[1,3,4]]);

julia> far_vertices = [2,3,4];

julia> PC = polyhedral_complex(IM, VR, far_vertices);

julia> f_vector(PC)
3-element Vector{Int64}:
1
3
2
source
is_embeddedMethod
is_embedded(PC::PolyhedralComplex)

Return true if PC is embedded, i.e. if its vertices can be computed as a subset of some $\mathbb{R}^n$.

Examples

julia> VR = [0 0; 1 0; -1 0; 0 1];

julia> IM = IncidenceMatrix([[1,2],[1,3],[1,4]]);

julia> PC = polyhedral_complex(IM, VR)
Polyhedral complex in ambient dimension 2

julia> is_embedded(PC)
true
source
maximal_polyhedraMethod
maximal_polyhedra(PC::PolyhedralComplex)

Return the maximal polyhedra of PC

Examples

julia> IM = IncidenceMatrix([[1,2,3],[1,3,4]])
2×4 IncidenceMatrix
[1, 2, 3]
[1, 3, 4]

julia> VR = [0 0; 1 0; 1 1; 0 1]
4×2 Matrix{Int64}:
0  0
1  0
1  1
0  1

julia> PC = polyhedral_complex(IM, VR, )
Polyhedral complex in ambient dimension 2

julia> maximal_polyhedra(PC)
2-element SubObjectIterator{Polyhedron{QQFieldElem}}:
Polyhedron in ambient dimension 2
Polyhedron in ambient dimension 2
source
minimal_facesMethod
minimal_faces(as, PC::PolyhedralComplex)

Return the minimal faces of a polyhedral complex as a NamedTuple with two iterators. For a polyhedral complex without lineality, the base_points are the vertices. If PC has lineality L, then every minimal face is an affine translation p+L, where p is only unique modulo L. The return type is a dict, the key :base_points gives an iterator over such p, and the key :lineality_basis lets one access a basis for the lineality space L of PC.

Examples

julia> VR = [0 0 0; 1 0 0; 0 1 0; -1 0 0];

julia> IM = IncidenceMatrix([[1,2,3],[1,3,4]]);

julia> far_vertices = [2,3,4];

julia> L = [0 0 1];

julia> PC = polyhedral_complex(IM, VR, far_vertices, L)
Polyhedral complex in ambient dimension 3

julia> MFPC = minimal_faces(PC)
(base_points = PointVector{QQFieldElem}[[0, 0, 0]], lineality_basis = RayVector{QQFieldElem}[[0, 0, 1]])

julia> MFPC.base_points
1-element SubObjectIterator{PointVector{QQFieldElem}}:
[0, 0, 0]

julia> MFPC.lineality_basis
1-element SubObjectIterator{RayVector{QQFieldElem}}:
[0, 0, 1]
source
n_maximal_polyhedraMethod
n_maximal_polyhedra(PC::PolyhedralComplex)

Return the number of maximal polyhedra of PC

Examples

julia> IM = IncidenceMatrix([[1,2,3],[1,3,4]])
2×4 IncidenceMatrix
[1, 2, 3]
[1, 3, 4]

julia> VR = [0 0; 1 0; 1 1; 0 1]
4×2 Matrix{Int64}:
0  0
1  0
1  1
0  1

julia> PC = polyhedral_complex(IM, VR, )
Polyhedral complex in ambient dimension 2

julia> n_maximal_polyhedra(PC)
2
source
npolyhedraMethod
npolyhedra(PC::PolyhedralComplex)

Return the total number of polyhedra in the polyhedral complex PC.

Examples

julia> VR = [0 0; 1 0; -1 0; 0 1];

julia> IM = IncidenceMatrix([[1,2,4],[1,3,4]]);

julia> far_vertices = [2,3,4];

julia> PC = polyhedral_complex(IM, VR, far_vertices);

julia> npolyhedra(PC)
6
source
nraysMethod
nrays(PC::PolyhedralComplex)

Return the number of rays of PC.

Examples

julia> VR = [0 0; 1 0; -1 0; 0 1];

julia> IM = IncidenceMatrix([[1,2,4],[1,3,4]]);

julia> far_vertices = [2,3,4];

julia> PC = polyhedral_complex(IM, VR, far_vertices);

julia> nrays(PC)
3
source
nverticesMethod
nvertices(PC::PolyhedralComplex)

Return the number of vertices of PC.

Examples

julia> VR = [0 0; 1 0; -1 0; 0 1];

julia> IM = IncidenceMatrix([[1,2,4],[1,3,4]]);

julia> far_vertices = [2,3,4];

julia> PC = polyhedral_complex(IM, VR, far_vertices);

julia> nvertices(PC)
1
source
polyhedra_of_dimFunction
polyhedra_of_dim(PC::PolyhedralComplex, polyhedron_dim::Int)

Return the polyhedra of a given dimension in the polyhedral complex PC.

Examples

julia> IM = IncidenceMatrix([[1,2,3],[1,3,4]]);

julia> VR = [0 0; 1 0; 1 1; 0 1];

julia> PC = polyhedral_complex(IM, VR);

julia> P1s = polyhedra_of_dim(PC,1)
5-element SubObjectIterator{Polyhedron{QQFieldElem}}:
Polyhedron in ambient dimension 2
Polyhedron in ambient dimension 2
Polyhedron in ambient dimension 2
Polyhedron in ambient dimension 2
Polyhedron in ambient dimension 2

julia> for p in P1s
println(dim(p))
end
1
1
1
1
1
source
raysMethod
rays(PC::PolyhedralComplex)

Return the rays of PC

Examples

julia> IM = IncidenceMatrix([[1,2,3],[1,3,4]]);

julia> VR = [0 0; 1 0; 1 1; 0 1];

julia> PC = polyhedral_complex(IM, VR, )
Polyhedral complex in ambient dimension 2

julia> rays(PC)
1-element SubObjectIterator{RayVector{QQFieldElem}}:
[1, 0]

julia> matrix(QQ, rays(PC))
[1   0]
source
rays_modulo_linealityMethod
rays_modulo_lineality(as, PC::PolyhedralComplex)

Return the rays of the recession cone of PC up to lineality as a NamedTuple with two iterators. If PC has lineality L, then the iterator rays_modulo_lineality iterates over representatives of the rays of PC/L. The iterator lineality_basis gives a basis of the lineality space L.

Examples

julia> VR = [0 0 0; 1 0 0; 0 1 0; -1 0 0];

julia> IM = IncidenceMatrix([[1,2,3],[1,3,4]]);

julia> far_vertices = [2,3,4];

julia> L = [0 0 1];

julia> PC = polyhedral_complex(IM, VR, far_vertices, L)
Polyhedral complex in ambient dimension 3

julia> RML = rays_modulo_lineality(PC)
(rays_modulo_lineality = RayVector{QQFieldElem}[[1, 0, 0], [0, 1, 0], [-1, 0, 0]], lineality_basis = RayVector{QQFieldElem}[[0, 0, 1]])

julia> RML.rays_modulo_lineality
3-element SubObjectIterator{RayVector{QQFieldElem}}:
[1, 0, 0]
[0, 1, 0]
[-1, 0, 0]

julia> RML.lineality_basis
1-element SubObjectIterator{RayVector{QQFieldElem}}:
[0, 0, 1]
source
verticesMethod
vertices(T::TropicalVariety{M, EMB})
vertices(T::TropicalCurve{M, EMB})
vertices(T::TropicalHypersurface{M, EMB})
vertices(T::TropicalLinearSpace{M, EMB})

Return the vertices of T, which are points in euclidean space if T is embedded or elements in an ordered set otherwise.

Examples

The vertices of a plane tropical line, plane tropical honeycomb quadric, and plane tropical honeycomb cubic

julia> RR = TropicalSemiring(min);

julia> S,(x,y) = RR["x","y"];

julia> f1 = x+y+1;

julia> tropicalLine = TropicalHypersurface(f1);

julia> vertices(tropicalLine)
1-element SubObjectIterator{PointVector{QQFieldElem}}:
[1, 1]

julia> f2 = 1*x^2+x*y+1*y^2+x+y+1;

[1, 1]`