# Working with polyhedra

We start with a polyhedron in 2-space which is the convexHull of a given set of points.
 i1 : V = matrix {{0,2,-2,0},{-1,1,1,1}} o1 = | 0 2 -2 0 | | -1 1 1 1 | 2 4 o1 : Matrix ZZ <--- ZZ i2 : P = convexHull V o2 = {ambient dimension => 2 } dimension of lineality space => 0 dimension of polyhedron => 2 number of facets => 3 number of rays => 0 number of vertices => 3 o2 : Polyhedron

This gives an overview of the characteristics of the polyhedron. If we want to know more details, we can ask for them.

 i3 : vertices P o3 = | 0 -2 2 | | -1 1 1 | 2 3 o3 : Matrix QQ <--- QQ

Here we see that the point (0,1) is not a vertex and P is actually a triangle.

 i4 : (HS,v) = halfspaces P o4 = (| -1 -1 |, | 1 |) | 1 -1 | | 1 | | 0 1 | | 1 | o4 : Sequence

This gives the defining affine half-spaces, i.e. P is given by all p such that HS*p =< v and that lie in the defining affine hyperplanes. To get the hyperplanes we use:

 i5 : hyperplanes P o5 = (0, 0) o5 : Sequence

There are none, so the polyhedron is of full dimension. It is also compact, since P has no rays and the lineality space is of dimension zero.

 i6 : rays P o6 = 0 2 o6 : Matrix ZZ <--- 0 i7 : linSpace P o7 = 0 2 o7 : Matrix ZZ <--- 0

Furthermore, we can construct the convex hull of a set of points and a set of rays.

 i8 : R = matrix {{1},{0},{0}} o8 = | 1 | | 0 | | 0 | 3 1 o8 : Matrix ZZ <--- ZZ i9 : V1 = V || matrix {{1,1,1,1}} o9 = | 0 2 -2 0 | | -1 1 1 1 | | 1 1 1 1 | 3 4 o9 : Matrix ZZ <--- ZZ i10 : P1 = convexHull(V1,R) o10 = {ambient dimension => 3 } dimension of lineality space => 0 dimension of polyhedron => 2 number of facets => 3 number of rays => 1 number of vertices => 2 o10 : Polyhedron i11 : vertices P1 o11 = | 0 -2 | | -1 1 | | 1 1 | 3 2 o11 : Matrix QQ <--- QQ

This polyhedron is not compact anymore and also not of full dimension.

 i12 : rays P1 o12 = | 1 | | 0 | | 0 | 3 1 o12 : Matrix ZZ <--- ZZ i13 : hyperplanes P1 o13 = (| 0 0 1 |, | 1 |) o13 : Sequence

On the other hand we can construct a polyhedron as the intersection of affine half-spaces and affine hyperplanes.

 i14 : HS = transpose (V || matrix {{-1,2,0,1}}) o14 = | 0 -1 -1 | | 2 1 2 | | -2 1 0 | | 0 1 1 | 4 3 o14 : Matrix ZZ <--- ZZ i15 : v = matrix {{1},{1},{1},{1}} o15 = | 1 | | 1 | | 1 | | 1 | 4 1 o15 : Matrix ZZ <--- ZZ i16 : HP = matrix {{1,1,1}} o16 = | 1 1 1 | 1 3 o16 : Matrix ZZ <--- ZZ i17 : w = matrix {{3}} o17 = | 3 | 1 1 o17 : Matrix ZZ <--- ZZ i18 : P2 = intersection(HS,v,HP,w) o18 = {ambient dimension => 3 } dimension of lineality space => 0 dimension of polyhedron => 2 number of facets => 3 number of rays => 0 number of vertices => 3 o18 : Polyhedron

This is a triangle in 3-space with the following vertices.

 i19 : vertices P2 o19 = | 4 4 2 | | 9 5 5 | | -10 -6 -4 | 3 3 o19 : Matrix QQ <--- QQ

If we don't intersect with the hyperplane we get a full dimensional polyhedron.

 i20 : P3 = intersection(HS,v) o20 = {ambient dimension => 3 } dimension of lineality space => 1 dimension of polyhedron => 3 number of facets => 3 number of rays => 0 number of vertices => 3 o20 : Polyhedron i21 : vertices P3 o21 = | 10/9 -2/3 -2/9 | | -7/9 -1/3 5/9 | | -2/9 -2/3 4/9 | 3 3 o21 : Matrix QQ <--- QQ i22 : linSpace P3 o22 = | -1 | | -2 | | 2 | 3 1 o22 : Matrix ZZ <--- ZZ

Note that the vertices are given modulo the lineality space. Besides constructing polyhedra by hand, there are also some basic polyhedra implemented such as the hypercube, in this case with edge-length four.

 i23 : P4 = hypercube(3,2) o23 = {ambient dimension => 3 } dimension of lineality space => 0 dimension of polyhedron => 3 number of facets => 6 number of rays => 0 number of vertices => 8 o23 : Polyhedron i24 : vertices P4 o24 = | -2 2 -2 2 -2 2 -2 2 | | -2 -2 2 2 -2 -2 2 2 | | -2 -2 -2 -2 2 2 2 2 | 3 8 o24 : Matrix QQ <--- QQ

Another on is the crossPolytope, in this case with diameter six.

 i25 : P5 = crossPolytope(3,3) o25 = {ambient dimension => 3 } dimension of lineality space => 0 dimension of polyhedron => 3 number of facets => 8 number of rays => 0 number of vertices => 6 o25 : Polyhedron i26 : vertices P5 o26 = | -3 3 0 0 0 0 | | 0 0 -3 3 0 0 | | 0 0 0 0 -3 3 | 3 6 o26 : Matrix QQ <--- QQ

Furthermore the standard simplex (stdSimplex).

 i27 : P6 = stdSimplex 2 o27 = {ambient dimension => 3 } dimension of lineality space => 0 dimension of polyhedron => 2 number of facets => 3 number of rays => 0 number of vertices => 3 o27 : Polyhedron i28 : vertices P6 o28 = | 1 0 0 | | 0 1 0 | | 0 0 1 | 3 3 o28 : Matrix QQ <--- QQ

Now that we can construct polyhedra, we can turn to the functions that can be applied to polyhedra. First of all, we can apply the convexHull function also to a pair of polyhedra:

 i29 : P7 = convexHull(P4,P5) o29 = {ambient dimension => 3 } dimension of lineality space => 0 dimension of polyhedron => 3 number of facets => 24 number of rays => 0 number of vertices => 14 o29 : Polyhedron i30 : vertices P7 o30 = | -3 3 0 0 0 -2 2 -2 2 -2 2 -2 2 0 | | 0 0 -3 3 0 -2 -2 2 2 -2 -2 2 2 0 | | 0 0 0 0 -3 -2 -2 -2 -2 2 2 2 2 3 | 3 14 o30 : Matrix QQ <--- QQ

Or we can intersect them by using intersection:

 i31 : P8 = intersection(P4,P5) o31 = {ambient dimension => 3 } dimension of lineality space => 0 dimension of polyhedron => 3 number of facets => 14 number of rays => 0 number of vertices => 24 o31 : Polyhedron i32 : vertices P8 o32 = | -1 1 -2 2 -2 2 -1 1 -1 1 0 0 -2 2 0 0 -2 2 0 0 -1 1 0 0 | | -2 -2 -1 -1 1 1 2 2 0 0 -1 1 0 0 -2 2 0 0 -2 2 0 0 -1 1 | | 0 0 0 0 0 0 0 0 -2 -2 -2 -2 -1 -1 -1 -1 1 1 1 1 2 2 2 2 | 3 24 o32 : Matrix QQ <--- QQ

Furthermore, both functions can be applied to a list containing any number of polyhedra and matrices defining vertices/rays or affine half-spaces/hyperplanes. All of these must be in the same ambient space. For example:

 i33 : P9 = convexHull {(V1,R),P2,P6} o33 = {ambient dimension => 3 } dimension of lineality space => 0 dimension of polyhedron => 3 number of facets => 8 number of rays => 1 number of vertices => 5 o33 : Polyhedron i34 : vertices P9 o34 = | 4 4 2 0 -2 | | 9 5 5 -1 1 | | -10 -6 -4 1 1 | 3 5 o34 : Matrix QQ <--- QQ

Further functions are for example the Minkowski sum (minkowskiSum) of two polyhedra.

 i35 : Q = convexHull (-V) o35 = {ambient dimension => 2 } dimension of lineality space => 0 dimension of polyhedron => 2 number of facets => 3 number of rays => 0 number of vertices => 3 o35 : Polyhedron i36 : P10 = P + Q o36 = {ambient dimension => 2 } dimension of lineality space => 0 dimension of polyhedron => 2 number of facets => 6 number of rays => 0 number of vertices => 6 o36 : Polyhedron i37 : vertices P10 o37 = | -4 4 -2 2 -2 2 | | 0 0 -2 -2 2 2 | 2 6 o37 : Matrix QQ <--- QQ

In the other direction, we can also determine all Minkowski summands (see minkSummandCone) of a polyhedron.

 i38 : (C,L,M) = minkSummandCone P10 o38 = ({ambient dimension => 6 }, HashTable{0 => dimension of lineality space => 0 dimension of the cone => 4 number of facets => 6 number of rays => 5 1 => 2 => 3 => 4 => ----------------------------------------------------------------------- {ambient dimension => 2 }}, | 1 0 |) dimension of lineality space => 0 | 0 1 | dimension of polyhedron => 1 | 1 0 | number of facets => 2 | 1 0 | number of rays => 0 | 0 1 | number of vertices => 2 {ambient dimension => 2 } dimension of lineality space => 0 dimension of polyhedron => 2 number of facets => 3 number of rays => 0 number of vertices => 3 {ambient dimension => 2 } dimension of lineality space => 0 dimension of polyhedron => 1 number of facets => 2 number of rays => 0 number of vertices => 2 {ambient dimension => 2 } dimension of lineality space => 0 dimension of polyhedron => 1 number of facets => 2 number of rays => 0 number of vertices => 2 {ambient dimension => 2 } dimension of lineality space => 0 dimension of polyhedron => 2 number of facets => 3 number of rays => 0 number of vertices => 3 o38 : Sequence i39 : apply(values L, vertices) o39 = {| 0 4 |, | 0 4 2 |, | 0 2 |, | 0 2 |, | 0 4 2 |} | 0 0 | | 0 0 -2 | | 0 2 | | 0 -2 | | 0 0 2 | o39 : List

Here the polyhedra in the hash table L are all possible Minkowski summands up to scalar multiplication and the columns of M give the minimal decompositions. So the hexagon P10 is not only the sum of two triangles but also the sum of three lines. Furthermore, we can take the direct product of two polyhedra.

 i40 : P11 = P * Q o40 = {ambient dimension => 4 } dimension of lineality space => 0 dimension of polyhedron => 4 number of facets => 6 number of rays => 0 number of vertices => 9 o40 : Polyhedron i41 : vertices P11 o41 = | 0 -2 2 0 -2 2 0 -2 2 | | -1 1 1 -1 1 1 -1 1 1 | | -2 -2 -2 2 2 2 0 0 0 | | -1 -1 -1 -1 -1 -1 1 1 1 | 4 9 o41 : Matrix QQ <--- QQ

The result is in QQ^4.

 i42 : ambDim P11 o42 = 4

 i43 : fVector P11 o43 = {9, 18, 15, 6, 1} o43 : List

The function fVector gives the number of faces of each dimension, so it has 9 vertices, 18 edges and so on. We can access the faces of a certain codimension via:

 i44 : L = faces(1,P11) o44 = {{ambient dimension => 4 }, {ambient dimension => 4 dimension of lineality space => 0 dimension of lineality space => dimension of polyhedron => 3 dimension of polyhedron => 3 number of facets => 5 number of facets => 5 number of rays => 0 number of rays => 0 number of vertices => 6 number of vertices => 6 ----------------------------------------------------------------------- }, {ambient dimension => 4 }, {ambient dimension => 4 0 dimension of lineality space => 0 dimension of lineality space dimension of polyhedron => 3 dimension of polyhedron => 3 number of facets => 5 number of facets => 5 number of rays => 0 number of rays => 0 number of vertices => 6 number of vertices => 6 ----------------------------------------------------------------------- }, {ambient dimension => 4 }, => 0 dimension of lineality space => 0 dimension of polyhedron => 3 number of facets => 5 number of rays => 0 number of vertices => 6 ----------------------------------------------------------------------- {ambient dimension => 4 }} dimension of lineality space => 0 dimension of polyhedron => 3 number of facets => 5 number of rays => 0 number of vertices => 6 o44 : List i45 : apply(L,vertices) o45 = {| 0 -2 0 -2 0 -2 |, | 0 2 0 2 0 2 |, | -2 2 -2 2 -2 2 |, | | -1 1 -1 1 -1 1 | | -1 1 -1 1 -1 1 | | 1 1 1 1 1 1 | | | -2 -2 2 2 0 0 | | -2 -2 2 2 0 0 | | -2 -2 2 2 0 0 | | | -1 -1 -1 -1 1 1 | | -1 -1 -1 -1 1 1 | | -1 -1 -1 -1 1 1 | | ----------------------------------------------------------------------- 0 -2 2 0 -2 2 |, | 0 -2 2 0 -2 2 |, | 0 -2 2 0 -2 2 |} -1 1 1 -1 1 1 | | -1 1 1 -1 1 1 | | -1 1 1 -1 1 1 | -2 -2 -2 2 2 2 | | -2 -2 -2 0 0 0 | | 2 2 2 0 0 0 | -1 -1 -1 -1 -1 -1 | | -1 -1 -1 1 1 1 | | -1 -1 -1 1 1 1 | o45 : List

We can compute all lattice points of the polyhedron with latticePoints.

 i46 : L = latticePoints P11 o46 = {| 0 |, | 0 |, | 0 |, | 0 |, | 0 |, | 0 |, | 0 |, | 0 |, | 0 | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | -2 | | -1 | | 0 | | 1 | | 2 | | -1 | | 0 | | 1 | | 0 | -1 | | -1 | | -1 | | -1 | | -1 | | 0 | | 0 | | 0 | | 1 ----------------------------------------------------------------------- |, | -1 |, | -1 |, | -1 |, | -1 |, | -1 |, | -1 |, | -1 |, | -1 |, | -1 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | -2 | | -1 | | 0 | | 1 | | 2 | | -1 | | 0 | | 1 | | 0 | | -1 | | -1 | | -1 | | -1 | | -1 | | 0 | | 0 | | 0 | | 1 ----------------------------------------------------------------------- |, | 0 |, | 0 |, | 0 |, | 0 |, | 0 |, | 0 |, 0, | 0 |, | 0 |, | | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | | | -2 | | -1 | | 0 | | 1 | | 2 | | -1 | | 1 | | 0 | | | | -1 | | -1 | | -1 | | -1 | | -1 | | 0 | | 0 | | 1 | | ----------------------------------------------------------------------- 1 |, | 1 |, | 1 |, | 1 |, | 1 |, | 1 |, | 1 |, | 1 |, | 1 |, | -2 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 1 -2 | | -1 | | 0 | | 1 | | 2 | | -1 | | 0 | | 1 | | 0 | | -2 -1 | | -1 | | -1 | | -1 | | -1 | | 0 | | 0 | | 0 | | 1 | | -1 ----------------------------------------------------------------------- |, | -2 |, | -2 |, | -2 |, | -2 |, | -1 |, | -1 |, | -1 |, | -1 |, | -1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | -1 | | 0 | | 1 | | 2 | | -2 | | -1 | | 0 | | 1 | | 2 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 ----------------------------------------------------------------------- |, | 0 |, | 0 |, | 0 |, | 0 |, | 0 |, | 1 |, | 1 |, | 1 |, | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | -2 | | -1 | | 0 | | 1 | | 2 | | -2 | | -1 | | 0 | | 1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 ----------------------------------------------------------------------- |, | 1 |, | 2 |, | 2 |, | 2 |, | 2 |, | 2 |, | -2 |, | -1 |, | 0 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 2 | | -2 | | -1 | | 0 | | 1 | | 2 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | -1 | | 0 | | 0 | | 0 ----------------------------------------------------------------------- |, | 1 |, | 2 |, | -2 |, | -1 |, | 0 |, | 1 |, | 2 |, | -2 |, | -1 |, | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | | -1 | | -1 | | 0 | | 0 | | 0 | | 0 | | 0 | | 1 | | 1 | | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | ----------------------------------------------------------------------- | 0 |, | 1 |, | 2 |, | -2 |, | -1 |, | 0 |, | 1 |, | 2 |} | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 0 | | 1 | | 1 | | 1 | | 1 | | 1 | o46 : List i47 : #L o47 = 81

Evenmore the tail/recession cone of a polyhedron with tailCone.

 i48 : C = tailCone P1 o48 = {ambient dimension => 3 } dimension of lineality space => 0 dimension of the cone => 1 number of facets => 1 number of rays => 1 o48 : Cone i49 : rays C o49 = | 1 | | 0 | | 0 | 3 1 o49 : Matrix ZZ <--- ZZ

Finally, there is also a function to compute the polar of a polyhedron, i.e. all points in the dual space that are greater than -1 on all points of the polyhedron:

 i50 : P12 = polar P11 o50 = {ambient dimension => 4 } dimension of lineality space => 0 dimension of polyhedron => 4 number of facets => 9 number of rays => 0 number of vertices => 6 o50 : Polyhedron i51 : vertices P12 o51 = | 0 -1 1 0 0 0 | | -1 1 1 0 0 0 | | 0 0 0 -1 1 0 | | 0 0 0 -1 -1 1 | 4 6 o51 : Matrix QQ <--- QQ