Constructions

Polyhedron{T}(A::AnyVecOrMat, b) where T<:scalar_types

The (convex) polyhedron defined by

\[P(A,b) = \{ x | Ax ≤ b \}.\]

see Def. 3.35 and Section 4.1. of Michael Joswig, Thorsten Theobald (2013)

Examples

The following lines define the square $[0,1]^2 \subset \mathbb{R}^2$:

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

julia> b = [1, 1, 0, 0];

julia> Polyhedron(A,b)
A polyhedron in ambient dimension 2

Polyhedron{T}(I::Union{Nothing, AbstractCollection[AffineHalfspace]}, E::Union{Nothing, AbstractCollection[AffineHyperplane]} = nothing) where T<:scalar_types

The (convex) polyhedron obtained intersecting the halfspaces I (inequalities) and the hyperplanes E (equations).

Examples

The following lines define the square $[0,1]^2 \subset \mathbb{R}^2$:

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

julia> b = [1, 1, 0, 0];

julia> Polyhedron((A,b))
A polyhedron in ambient dimension 2

As an example for a polyhedron constructed from both inequalities and equations, we construct the polytope $[0,1]\times\{0\}\subset\mathbb{R}^2$

julia> P = Polyhedron(([-1 0; 1 0], [0,1]), ([0 1], [0]))
A polyhedron in ambient dimension 2

julia> is_feasible(P)
true

julia> dim(P)
1

julia> vertices(P)
2-element SubObjectIterator{PointVector{fmpq}}:
 [1, 0]
 [0, 0]

convex_hull([::Type{T} = fmpq,] V [, R [, L]]; non_redundant::Bool = false)

Construct the convex hull of the vertices V, rays R, and lineality L. If R or L are omitted, then they are assumed to be zero.

Arguments

• V::AbstractCollection[PointVector]: Points whose convex hull is to be computed.
• R::AbstractCollection[RayVector]: Rays completing the set of points.
• L::AbstractCollection[RayVector]: Generators of the Lineality space.

If an argument is given as a matrix, its content has to be encoded row-wise.

R can be given as an empty matrix or as nothing if the user wants to compute the convex hull only from V and L.

If it is known that V and R only contain extremal points and that the description of the lineality space is complete, set non_redundant = true to avoid unnecessary redundancy checks.

See Def. 2.11 and Def. 3.1 of Michael Joswig, Thorsten Theobald (2013).

Examples

The following lines define the square $[0,1]^2 \subset \mathbb{R}^2$:

julia> Square = convex_hull([0 0; 0 1; 1 0; 1 1])
A polyhedron in ambient dimension 2

To construct the positive orthant, rays have to be passed:

julia> V = [0 0];

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

julia> PO = convex_hull(V, R)
A polyhedron in ambient dimension 2

The closed-upper half plane can be constructed by passing rays and a lineality space:

julia> V = [0 0];

julia> R = [0 1];

julia> L = [1 0];

julia> UH = convex_hull(V, R, L)
A polyhedron in ambient dimension 2

To obtain the x-axis in $\mathbb{R}^2$:

julia> V = [0 0];

julia> R = nothing;

julia> L = [1 0];

julia> XA = convex_hull(V, R, L)
A polyhedron in ambient dimension 2

archimedean_solid(s)

Construct an Archimedean solid with the name given by String s from the list below. The polytopes are realized with floating point numbers and thus not exact; Vertex-facet-incidences are correct in all cases.

Arguments

• s::String: The name of the desired Archimedean solid. Possible values:
  • "truncated_tetrahedron" : Truncated tetrahedron. Regular polytope with four triangular and four hexagonal facets.
  • "cuboctahedron" : Cuboctahedron. Regular polytope with eight triangular and six square facets.
  • "truncated_cube" : Truncated cube. Regular polytope with eight triangular and six octagonal facets.
  • "truncated_octahedron" : Truncated Octahedron. Regular polytope with six square and eight hexagonal facets.
  • "rhombicuboctahedron" : Rhombicuboctahedron. Regular polytope with eight triangular and 18 square facets.
  • "truncated_cuboctahedron" : Truncated Cuboctahedron. Regular polytope with 12 square, eight hexagonal and six octagonal facets.
  • "snub_cube" : Snub Cube. Regular polytope with 32 triangular and six square facets. The vertices are realized as floating point numbers. This is a chiral polytope.
  • "icosidodecahedron" : Icosidodecahedon. Regular polytope with 20 triangular and 12 pentagonal facets.
  • "truncated_dodecahedron" : Truncated Dodecahedron. Regular polytope with 20 triangular and 12 decagonal facets.
  • "truncated_icosahedron" : Truncated Icosahedron. Regular polytope with 12 pentagonal and 20 hexagonal facets.
  • "rhombicosidodecahedron" : Rhombicosidodecahedron. Regular polytope with 20 triangular, 30 square and 12 pentagonal facets.
  • "truncated_icosidodecahedron" : Truncated Icosidodecahedron. Regular polytope with 30 square, 20 hexagonal and 12 decagonal facets.
  • "snub_dodecahedron" : Snub Dodecahedron. Regular polytope with 80 triangular and 12 pentagonal facets. The vertices are realized as floating point numbers. This is a chiral polytope.

Examples

julia> T = archimedean_solid("cuboctahedron")
A polyhedron in ambient dimension 3

julia> sum([nvertices(F) for F in faces(T, 2)] .== 3)
8

julia> sum([nvertices(F) for F in faces(T, 2)] .== 4)
6

julia> nfacets(T)
14

birkhoff(n::Integer, even::Bool = false)

Construct the Birkhoff polytope of dimension $n^2$.

This is the polytope of $n \times n$ stochastic matrices (encoded as row vectors of length $n^2$), i.e., the matrices with non-negative real entries whose row and column entries sum up to one. Its vertices are the permutation matrices.

Use even = true to get the vertices only for the even permutation matrices.

Examples

julia> b = birkhoff(3)
A polyhedron in ambient dimension 9

julia> vertices(b)
6-element SubObjectIterator{PointVector{fmpq}}:
 [1, 0, 0, 0, 1, 0, 0, 0, 1]
 [0, 1, 0, 1, 0, 0, 0, 0, 1]
 [0, 0, 1, 1, 0, 0, 0, 1, 0]
 [1, 0, 0, 0, 0, 1, 0, 1, 0]
 [0, 1, 0, 0, 0, 1, 1, 0, 0]
 [0, 0, 1, 0, 1, 0, 1, 0, 0]

catalan_solid(s::String)

Construct a Catalan solid with the name s from the list below. The polytopes are realized with floating point coordinates and thus are not exact. However, vertex-facet-incidences are correct in all cases.

Arguments

• s::String: The name of the desired Archimedean solid. Possible values:
  • "triakis_tetrahedron" : Triakis Tetrahedron. Dual polytope to the Truncated Tetrahedron, made of 12 isosceles triangular facets.
  • "triakis_octahedron" : Triakis Octahedron. Dual polytope to the Truncated Cube, made of 24 isosceles triangular facets.
  • "rhombic_dodecahedron" : Rhombic dodecahedron. Dual polytope to the cuboctahedron, made of 12 rhombic facets.
  • "tetrakis_hexahedron" : Tetrakis hexahedron. Dual polytope to the truncated octahedron, made of 24 isosceles triangluar facets.
  • "disdyakis_dodecahedron" : Disdyakis dodecahedron. Dual polytope to the truncated cuboctahedron, made of 48 scalene triangular facets.
  • "pentagonal_icositetrahedron" : Pentagonal Icositetrahedron. Dual polytope to the snub cube, made of 24 irregular pentagonal facets. The vertices are realized as floating point numbers.
  • "pentagonal_hexecontahedron" : Pentagonal Hexecontahedron. Dual polytope to the snub dodecahedron, made of 60 irregular pentagonal facets. The vertices are realized as floating point numbers.
  • "rhombic_triacontahedron" : Rhombic triacontahedron. Dual polytope to the icosidodecahedron, made of 30 rhombic facets.
  • "triakis_icosahedron" : Triakis icosahedron. Dual polytope to the icosidodecahedron, made of 30 rhombic facets.
  • "deltoidal_icositetrahedron" : Deltoidal Icositetrahedron. Dual polytope to the rhombicubaoctahedron, made of 24 kite facets.
  • "pentakis_dodecahedron" : Pentakis dodecahedron. Dual polytope to the truncated icosahedron, made of 60 isosceles triangular facets.
  • "deltoidal_hexecontahedron" : Deltoidal hexecontahedron. Dual polytope to the rhombicosidodecahedron, made of 60 kite facets.
  • "disdyakis_triacontahedron" : Disdyakis triacontahedron. Dual polytope to the truncated icosidodecahedron, made of 120 scalene triangular facets.

Examples

julia> T = catalan_solid("triakis_tetrahedron");

julia> count(F -> nvertices(F) == 3, faces(T, 2))
12

julia> nfacets(T)
12

cross([::Type{T} = fmpq,] d::Int [,n::Rational])

Construct a $d$-dimensional cross polytope around origin with vertices located at $\pm e_i$ for each unit vector $e_i$ of $R^d$, scaled by $n$.

Examples

Here we print the facets of a non-scaled and a scaled 3-dimensional cross polytope:

julia> C = cross(3)
A polyhedron in ambient dimension 3

julia> facets(C)
8-element SubObjectIterator{AffineHalfspace{fmpq}} over the Halfspaces of R^3 described by:
x₁ + x₂ + x₃ ≦ 1
-x₁ + x₂ + x₃ ≦ 1
x₁ - x₂ + x₃ ≦ 1
-x₁ - x₂ + x₃ ≦ 1
x₁ + x₂ - x₃ ≦ 1
-x₁ + x₂ - x₃ ≦ 1
x₁ - x₂ - x₃ ≦ 1
-x₁ - x₂ - x₃ ≦ 1

julia> D = cross(3, 2)
A polyhedron in ambient dimension 3

julia> facets(D)
8-element SubObjectIterator{AffineHalfspace{fmpq}} over the Halfspaces of R^3 described by:
x₁ + x₂ + x₃ ≦ 2
-x₁ + x₂ + x₃ ≦ 2
x₁ - x₂ + x₃ ≦ 2
-x₁ - x₂ + x₃ ≦ 2
x₁ + x₂ - x₃ ≦ 2
-x₁ + x₂ - x₃ ≦ 2
x₁ - x₂ - x₃ ≦ 2
-x₁ - x₂ - x₃ ≦ 2

cube([::Type{T} = fmpq,] d::Int , [l::Rational = -1, u::Rational = 1])

Construct the $[l,u]$-cube in dimension $d$.

Examples

In this example the 5-dimensional unit cube is constructed to ask for one of its properties:

julia> C = cube(5,0,1);

julia> normalized_volume(C)
120

cyclic_polytope(d::Int, n::Int)

Construct the cyclic polytope that is the convex hull of $n$ points on the moment curve in dimension $d$.

Examples

julia> cp = cyclic_polytope(3, 20)
A polyhedron in ambient dimension 3

julia> nvertices(cp)
20

delpezzo(d::Int)

Produce the d-dimensional del-Pezzo polytope, which is the convex hull of the cross polytope together with the all-ones and minus all-ones vector.

julia> DP = delpezzo(4)
A polyhedron in ambient dimension 4

julia> f_vector(DP)
4-element Vector{fmpz}:
 10
 40
 60
 30

fano_simplex(d::Int)

Construct a lattice simplex such that the origin is the unique interior lattice point. The normal toric variety associated with its face fan is smooth.

julia> S = fano_simplex(3)
A polyhedron in ambient dimension 3

julia> X = NormalToricVariety(face_fan(S))
A normal toric variety

julia> is_smooth(X)
true

fractional_cut_polytope(G::Graphs.Graph{Graphs.Undirected})

Construct the fractional cut polytope of the graph $G$.

Examples

julia> G = Graphs.complete_graph(4);

julia> fractional_cut_polytope(G)
A polyhedron in ambient dimension 6

fractional_matching_polytope(G::Graphs.Graph{Graphs.Undirected})

Construct the fractional matching polytope of the graph $G$.

Examples

julia> G = Graphs.complete_graph(4);

julia> fractional_matching_polytope(G)
A polyhedron in ambient dimension 6

gelfand_tsetlin(lambda::AbstractVector)

Construct the Gelfand Tsetlin polytope indexed by a weakly decreasing vector lambda.

julia> P = gelfand_tsetlin([5,3,2])
A polyhedron in ambient dimension 6

julia> is_fulldimensional(P)
false

julia> p = project_full(P)
A polyhedron in ambient dimension 3

julia> is_fulldimensional(p)
true

julia> volume(p)
3

simplex([::Type{T} = fmpq,] d::Int [,n::Rational])

Construct the simplex which is the convex hull of the standard basis vectors along with the origin in $\mathbb{R}^d$, scaled by $n$.

Examples

Here we take a look at the facets of the 7-simplex and a scaled 7-simplex:

julia> s = simplex(7)
A polyhedron in ambient dimension 7

julia> facets(s)
8-element SubObjectIterator{AffineHalfspace{fmpq}} over the Halfspaces of R^7 described by:
-x₁ ≦ 0
-x₂ ≦ 0
-x₃ ≦ 0
-x₄ ≦ 0
-x₅ ≦ 0
-x₆ ≦ 0
-x₇ ≦ 0
x₁ + x₂ + x₃ + x₄ + x₅ + x₆ + x₇ ≦ 1

julia> t = simplex(7, 5)
A polyhedron in ambient dimension 7

julia> facets(t)
8-element SubObjectIterator{AffineHalfspace{fmpq}} over the Halfspaces of R^7 described by:
-x₁ ≦ 0
-x₂ ≦ 0
-x₃ ≦ 0
-x₄ ≦ 0
-x₅ ≦ 0
-x₆ ≦ 0
-x₇ ≦ 0
x₁ + x₂ + x₃ + x₄ + x₅ + x₆ + x₇ ≦ 5

+(P::Polyhedron, Q::Polyhedron)

Return the Minkowski sum $P + Q = \{ x+y\ |\ x∈P, y∈Q\}$ of P and Q (see also minkowski_sum).

Examples

The Minkowski sum of a square and the 2-dimensional cross-polytope is an octagon:

julia> P = cube(2);

julia> Q = cross(2);

julia> M = minkowski_sum(P, Q)
A polyhedron in ambient dimension 2

julia> nvertices(M)
8

*(k::Int, Q::Polyhedron)

Return the scaled polyhedron $kQ = \{ kx\ |\ x∈Q\}$.

Note that k*Q = Q*k.

Examples

Scaling an $n$-dimensional bounded polyhedron by the factor $k$ results in the volume being scaled by $k^n$. This example confirms the statement for the 6-dimensional cube and $k = 2$.

julia> C = cube(6);

julia> SC = 2*C
A polyhedron in ambient dimension 6

julia> volume(SC)//volume(C)
64

*(P::Polyhedron, Q::Polyhedron)

Return the Cartesian product of P and Q (see also product).

Examples

The Cartesian product of a triangle and a line segment is a triangular prism.

julia> T=simplex(2)
A polyhedron in ambient dimension 2

julia> S=cube(1)
A polyhedron in ambient dimension 1

julia> length(vertices(T*S))
6

bipyramid(P::Polyhedron, z::Number = 1, z_prime::Number = -z)

Make a bipyramid over a pointed polyhedron P.

The bipyramid is the convex hull of the input polyhedron P and two apexes (v, z), (v, z_prime) on both sides of the affine span of P. For bounded polyhedra, the projections of the apexes v to the affine span of P is the vertex barycenter of P.

Examples

julia> c = cube(2)
A polyhedron in ambient dimension 2

julia> vertices(bipyramid(c,2))
6-element SubObjectIterator{PointVector{fmpq}}:
 [-1, -1, 0]
 [1, -1, 0]
 [-1, 1, 0]
 [1, 1, 0]
 [0, 0, 2]
 [0, 0, -2]

intersect(P::Polyhedron, Q::Polyhedron)

Return the intersection $P \cap Q$ of P and Q.

Examples

The positive orthant of the plane is the intersection of the two halfspaces with $x≥0$ and $y≥0$ respectively.

julia> UH1 = convex_hull([0 0],[1 0],[0 1]);

julia> UH2 = convex_hull([0 0],[0 1],[1 0]);

julia> PO = intersect(UH1, UH2)
A polyhedron in ambient dimension 2

julia> rays(PO)
2-element SubObjectIterator{RayVector{fmpq}}:
 [1, 0]
 [0, 1]

pyramid(P::Polyhedron, z::Number = 1)

Make a pyramid over the given polyhedron P.

The pyramid is the convex hull of the input polyhedron P and a point v outside the affine span of P. For bounded polyhedra, the projection of v to the affine span of P coincides with the vertex barycenter of P. The scalar z is the distance between the vertex barycenter and v.

Examples

julia> c = cube(2)
A polyhedron in ambient dimension 2

julia> vertices(pyramid(c,5))
5-element SubObjectIterator{PointVector{fmpq}}:
 [-1, -1, 0]
 [1, -1, 0]
 [-1, 1, 0]
 [1, 1, 0]
 [0, 0, 5]

convex_hull(P::Polyhedron, Q::Polyhedron)

Return the convex_hull of P and Q.

Examples

The convex hull of the following two line segments in $R^3$ is a tetrahedron.

julia> L₁ = convex_hull([-1 0 0; 1 0 0])
A polyhedron in ambient dimension 3

julia> L₂ = convex_hull([0 -1 0; 0 1 0])
A polyhedron in ambient dimension 3

julia> T=convex_hull(L₁,L₂);

julia> f_vector(T)
2-element Vector{fmpz}:
 4
 4

orbit_polytope(V::AbstractCollection[PointVector], G::PermGroup)

Construct the convex hull of the orbit of one or several points (given row-wise in V) under the action of G.

Examples

This will construct the $3$-dimensional permutahedron:

julia> V = [1 2 3];

julia> G = symmetric_group(3);

julia> P = orbit_polytope(V, G)
A polyhedron in ambient dimension 3

julia> vertices(P)
6-element SubObjectIterator{PointVector{fmpq}}:
 [1, 2, 3]
 [1, 3, 2]
 [2, 1, 3]
 [2, 3, 1]
 [3, 1, 2]
 [3, 2, 1]

newton_polytope(poly::Polynomial)

Compute the Newton polytope of the multivariate polynomial poly.

Examples

julia> S, (x, y) = PolynomialRing(ZZ, ["x", "y"])
(Multivariate Polynomial Ring in x, y over Integer Ring, fmpz_mpoly[x, y])

julia> f = x^3*y + 3x*y^2 + 1
x^3*y + 3*x*y^2 + 1

julia> NP = newton_polytope(f)
A polyhedron in ambient dimension 2

julia> vertices(NP)
3-element SubObjectIterator{PointVector{fmpq}}:
 [3, 1]
 [1, 2]
 [0, 0]