Convex rational polyhedral cones#
This module was designed as a part of framework for toric varieties
(variety
,
fano_variety
). While the emphasis is on
strictly convex cones, non-strictly convex cones are supported as well. Work
with distinct lattices (in the sense of discrete subgroups spanning vector
spaces) is supported. The default lattice is ToricLattice
\(N\) of the appropriate
dimension. The only case when you must specify lattice explicitly is creation
of a 0-dimensional cone, where dimension of the ambient space cannot be
guessed.
AUTHORS:
Andrey Novoseltsev (2010-05-13): initial version.
Andrey Novoseltsev (2010-06-17): substantial improvement during review by Volker Braun.
Volker Braun (2010-06-21): various spanned/quotient/dual lattice computations added.
Volker Braun (2010-12-28): Hilbert basis for cones.
Andrey Novoseltsev (2012-02-23): switch to PointCollection container.
EXAMPLES:
Use Cone()
to construct cones:
sage: octant = Cone([(1,0,0), (0,1,0), (0,0,1)])
sage: halfspace = Cone([(1,0,0), (0,1,0), (-1,-1,0), (0,0,1)])
sage: positive_xy = Cone([(1,0,0), (0,1,0)])
sage: four_rays = Cone([(1,1,1), (1,-1,1), (-1,-1,1), (-1,1,1)])
For all of the cones above we have provided primitive generating rays, but in
fact this is not necessary - a cone can be constructed from any collection of
rays (from the same space, of course). If there are non-primitive (or even
non-integral) rays, they will be replaced with primitive ones. If there are
extra rays, they will be discarded. Of course, this means that Cone()
has to do some work before actually constructing the cone and sometimes it is
not desirable, if you know for sure that your input is already “good”. In this
case you can use options check=False
to force Cone()
to use
exactly the directions that you have specified and normalize=False
to
force it to use exactly the rays that you have specified. However, it is
better not to use these possibilities without necessity, since cones are
assumed to be represented by a minimal set of primitive generating rays.
See Cone()
for further documentation on construction.
Once you have a cone, you can perform numerous operations on it. The most important ones are, probably, ray accessing methods:
sage: rays = halfspace.rays()
sage: rays
N( 0, 0, 1),
N( 0, 1, 0),
N( 0, -1, 0),
N( 1, 0, 0),
N(-1, 0, 0)
in 3-d lattice N
sage: rays.set()
frozenset({N(-1, 0, 0), N(0, -1, 0), N(0, 0, 1), N(0, 1, 0), N(1, 0, 0)})
sage: rays.matrix()
[ 0 0 1]
[ 0 1 0]
[ 0 -1 0]
[ 1 0 0]
[-1 0 0]
sage: rays.column_matrix()
[ 0 0 0 1 -1]
[ 0 1 -1 0 0]
[ 1 0 0 0 0]
sage: rays(3)
N(1, 0, 0)
in 3-d lattice N
sage: rays[3]
N(1, 0, 0)
sage: halfspace.ray(3)
N(1, 0, 0)
The method rays()
returns a
PointCollection
with the
\(i\)-th element being the primitive integral generator of the \(i\)-th
ray. It is possible to convert this collection to a matrix with either
rows or columns corresponding to these generators. You may also change
the default
output_format()
of all point collections to be such a matrix.
If you want to do something with each ray of a cone, you can write
sage: for ray in positive_xy: print(ray)
N(1, 0, 0)
N(0, 1, 0)
There are two dimensions associated to each cone - the dimension of the subspace spanned by the cone and the dimension of the space where it lives:
sage: positive_xy.dim()
2
sage: positive_xy.lattice_dim()
3
You also may be interested in this dimension:
sage: dim(positive_xy.linear_subspace())
0
sage: dim(halfspace.linear_subspace())
2
Or, perhaps, all you care about is whether it is zero or not:
sage: positive_xy.is_strictly_convex()
True
sage: halfspace.is_strictly_convex()
False
You can also perform these checks:
sage: positive_xy.is_simplicial()
True
sage: four_rays.is_simplicial()
False
sage: positive_xy.is_smooth()
True
You can work with subcones that form faces of other cones:
sage: # needs sage.graphs
sage: face = four_rays.faces(dim=2)[0]
sage: face
2-d face of 3-d cone in 3-d lattice N
sage: face.rays()
N(-1, -1, 1),
N(-1, 1, 1)
in 3-d lattice N
sage: face.ambient_ray_indices()
(2, 3)
sage: four_rays.rays(face.ambient_ray_indices())
N(-1, -1, 1),
N(-1, 1, 1)
in 3-d lattice N
If you need to know inclusion relations between faces, you can use
sage: # needs sage.graphs
sage: L = four_rays.face_lattice()
sage: [len(s) for s in L.level_sets()]
[1, 4, 4, 1]
sage: face = L.level_sets()[2][0]
sage: face.rays()
N(1, 1, 1),
N(1, -1, 1)
in 3-d lattice N
sage: L.hasse_diagram().neighbors_in(face)
[1-d face of 3-d cone in 3-d lattice N,
1-d face of 3-d cone in 3-d lattice N]
Warning
The order of faces in level sets of
the face lattice may differ from the order of faces returned by
faces()
. While the first order is
random, the latter one ensures that one-dimensional faces are listed in
the same order as generating rays.
When all the functionality provided by cones is not enough, you may want to check if you can do necessary things using polyhedra corresponding to cones:
sage: four_rays.polyhedron()
A 3-dimensional polyhedron in ZZ^3 defined as
the convex hull of 1 vertex and 4 rays
And of course you are always welcome to suggest new features that should be added to cones!
REFERENCES:
- sage.geometry.cone.Cone(rays, lattice=None, check=True, normalize=True)#
Construct a (not necessarily strictly) convex rational polyhedral cone.
INPUT:
rays
– a list of rays. Each ray should be given as a list or a vector convertible to the rational extension of the givenlattice
. May also be specified by aPolyhedron_base
object;lattice
–ToricLattice
, \(\ZZ^n\), or any other object that behaves like these. If not specified, an attempt will be made to determine an appropriate toric lattice automatically;check
– by default the input data will be checked for correctness (e.g. that all rays have the same number of components) and generating rays will be constructed fromrays
. If you know that the input is a minimal set of generators of a valid cone, you may significantly decrease construction time usingcheck=False
option;normalize
– you can further speed up construction usingnormalize=False
option. In this caserays
must be a list of immutable primitive rays inlattice
. In general, you should not use this option, it is designed for code optimization and does not give as drastic improvement in speed as the previous one.
OUTPUT:
convex rational polyhedral cone determined by
rays
.
EXAMPLES:
Let’s define a cone corresponding to the first quadrant of the plane (note, you can even mix objects of different types to represent rays, as long as you let this function to perform all the checks and necessary conversions!):
sage: quadrant = Cone([(1,0), [0,1]]) sage: quadrant 2-d cone in 2-d lattice N sage: quadrant.rays() N(1, 0), N(0, 1) in 2-d lattice N
If you give more rays than necessary, the extra ones will be discarded:
sage: Cone([(1,0), (0,1), (1,1), (0,1)]).rays() N(0, 1), N(1, 0) in 2-d lattice N
However, this work is not done with
check=False
option, so use it carefully!sage: Cone([(1,0), (0,1), (1,1), (0,1)], check=False).rays() N(1, 0), N(0, 1), N(1, 1), N(0, 1) in 2-d lattice N
Even worse things can happen with
normalize=False
option:sage: Cone([(1,0), (0,1)], check=False, normalize=False) Traceback (most recent call last): ... AttributeError: 'tuple' object has no attribute 'parent'
You can construct different “not” cones: not full-dimensional, not strictly convex, not containing any rays:
sage: one_dimensional_cone = Cone([(1,0)]) sage: one_dimensional_cone.dim() 1 sage: half_plane = Cone([(1,0), (0,1), (-1,0)]) sage: half_plane.rays() N( 0, 1), N( 1, 0), N(-1, 0) in 2-d lattice N sage: half_plane.is_strictly_convex() False sage: origin = Cone([(0,0)]) sage: origin.rays() Empty collection in 2-d lattice N sage: origin.dim() 0 sage: origin.lattice_dim() 2
You may construct the cone above without giving any rays, but in this case you must provide
lattice
explicitly:sage: origin = Cone([]) Traceback (most recent call last): ... ValueError: lattice must be given explicitly if there are no rays! sage: origin = Cone([], lattice=ToricLattice(2)) sage: origin.dim() 0 sage: origin.lattice_dim() 2 sage: origin.lattice() 2-d lattice N
However, the trivial cone in
n
dimensions has a predefined constructor for you to use:sage: origin = cones.trivial(2) sage: origin.rays() Empty collection in 2-d lattice N
Of course, you can also provide
lattice
in other cases:sage: L = ToricLattice(3, "L") sage: c1 = Cone([(1,0,0),(1,1,1)], lattice=L) sage: c1.rays() L(1, 0, 0), L(1, 1, 1) in 3-d lattice L
Or you can construct cones from rays of a particular lattice:
sage: ray1 = L(1,0,0) sage: ray2 = L(1,1,1) sage: c2 = Cone([ray1, ray2]) sage: c2.rays() L(1, 0, 0), L(1, 1, 1) in 3-d lattice L sage: c1 == c2 True
When the cone in question is not strictly convex, the standard form for the “generating rays” of the linear subspace is “basis vectors and their negatives”, as in the following example:
sage: plane = Cone([(1,0), (0,1), (-1,-1)]) sage: plane.rays() N( 0, 1), N( 0, -1), N( 1, 0), N(-1, 0) in 2-d lattice N
The cone can also be specified by a
Polyhedron_base
:sage: p = plane.polyhedron() sage: Cone(p) 2-d cone in 2-d lattice N sage: Cone(p) == plane True
- class sage.geometry.cone.ConvexRationalPolyhedralCone(rays=None, lattice=None, ambient=None, ambient_ray_indices=None, PPL=None)#
Bases:
IntegralRayCollection
,Container
,ConvexSet_closed
,ConvexRationalPolyhedralCone
Create a convex rational polyhedral cone.
Warning
This class does not perform any checks of correctness of input nor does it convert input into the standard representation. Use
Cone()
to construct cones.Cones are immutable, but they cache most of the returned values.
INPUT:
The input can be either:
rays
– list of immutable primitive vectors inlattice
;lattice
–ToricLattice
, \(\ZZ^n\), or any other object that behaves like these. IfNone
, it will be determined asparent()
of the first ray. Of course, this cannot be done if there are no rays, so in this case you must give an appropriatelattice
directly.
or (these parameters must be given as keywords):
ambient
– ambient structure of this cone, a biggercone
or afan
, this cone must be a face ofambient
;ambient_ray_indices
– increasing list or tuple of integers, indices of rays ofambient
generating this cone.
In both cases, the following keyword parameter may be specified in addition:
PPL
– eitherNone
(default) or aC_Polyhedron
representing the cone. This serves only to cache the polyhedral data if you know it already. The constructor does not make a copy so thePPL
object should not be modified afterwards.
OUTPUT:
convex rational polyhedral cone.
Note
Every cone has its ambient structure. If it was not specified, it is this cone itself.
- Hilbert_basis()#
Return the Hilbert basis of the cone.
Given a strictly convex cone \(C\subset \RR^d\), the Hilbert basis of \(C\) is the set of all irreducible elements in the semigroup \(C\cap \ZZ^d\). It is the unique minimal generating set over \(\ZZ\) for the integral points \(C\cap \ZZ^d\).
If the cone \(C\) is not strictly convex, this method finds the (unique) minimal set of lattice points that need to be added to the defining rays of the cone to generate the whole semigroup \(C\cap \ZZ^d\). But because the rays of the cone are not unique nor necessarily minimal in this case, neither is the returned generating set (consisting of the rays plus additional generators).
See also
semigroup_generators()
if you are not interested in a minimal set of generators.OUTPUT:
a
PointCollection
. The rays ofself
are the firstself.nrays()
entries.
EXAMPLES:
The following command ensures that the output ordering in the examples below is independent of TOPCOM, you don’t have to use it:
sage: PointConfiguration.set_engine('internal')
We start with a simple case of a non-smooth 2-dimensional cone:
sage: Cone([(1,0), (1,2)]).Hilbert_basis() N(1, 0), N(1, 2), N(1, 1) in 2-d lattice N
Two more complicated example from GAP/toric:
sage: Cone([[1,0], [3,4]]).dual().Hilbert_basis() M(0, 1), M(4, -3), M(1, 0), M(2, -1), M(3, -2) in 2-d lattice M sage: cone = Cone([[1,2,3,4], [0,1,0,7], [3,1,0,2], [0,0,1,0]]).dual() sage: cone.Hilbert_basis() # long time M(10, -7, 0, 1), M(-5, 21, 0, -3), M( 0, -2, 0, 1), M(15, -63, 25, 9), M( 2, -3, 0, 1), M( 1, -4, 1, 1), M( 4, -4, 0, 1), M(-1, 3, 0, 0), M( 1, -5, 2, 1), M( 3, -5, 1, 1), M( 6, -5, 0, 1), M( 3, -13, 5, 2), M( 2, -6, 2, 1), M( 5, -6, 1, 1), M( 8, -6, 0, 1), M( 0, 1, 0, 0), M(-2, 8, 0, -1), M(10, -42, 17, 6), M( 7, -28, 11, 4), M( 5, -21, 9, 3), M( 6, -21, 8, 3), M( 5, -14, 5, 2), M( 2, -7, 3, 1), M( 4, -7, 2, 1), M( 7, -7, 1, 1), M( 0, 0, 1, 0), M( 1, 0, 0, 0), M(-1, 7, 0, -1), M(-3, 14, 0, -2) in 4-d lattice M
Not a strictly convex cone:
sage: wedge = Cone([(1,0,0), (1,2,0), (0,0,1), (0,0,-1)]) sage: sorted(wedge.semigroup_generators()) [N(0, 0, -1), N(0, 0, 1), N(1, 0, 0), N(1, 1, 0), N(1, 2, 0)] sage: wedge.Hilbert_basis() N(1, 2, 0), N(1, 0, 0), N(0, 0, 1), N(0, 0, -1), N(1, 1, 0) in 3-d lattice N
Not full-dimensional cones are ok, too (see github issue #11312):
sage: Cone([(1,1,0), (-1,1,0)]).Hilbert_basis() N( 1, 1, 0), N(-1, 1, 0), N( 0, 1, 0) in 3-d lattice N
ALGORITHM:
The primal Normaliz algorithm, see [Normaliz].
- Hilbert_coefficients(point, solver, verbose=None, integrality_tolerance=0)#
Return the expansion coefficients of
point
with respect toHilbert_basis()
.INPUT:
point
– alattice()
point in the cone, or something that can be converted to a point. For example, a list or tuple of integers.solver
– (default:None
) Specify a Mixed Integer Linear Programming (MILP) solver to be used. If set toNone
, the default one is used. For more information on MILP solvers and which default solver is used, see the methodsolve
of the classMixedIntegerLinearProgram
.verbose
– integer (default:0
). Sets the level of verbosity of the LP solver. Set to 0 by default, which means quiet.integrality_tolerance
– parameter for use with MILP solvers over an inexact base ring; seeMixedIntegerLinearProgram.get_values()
.
OUTPUT:
A \(\ZZ\)-vector of length
len(self.Hilbert_basis())
with nonnegative components.Note
Since the Hilbert basis elements are not necessarily linearly independent, the expansion coefficients are not unique. However, this method will always return the same expansion coefficients when invoked with the same argument.
EXAMPLES:
sage: cone = Cone([(1,0), (0,1)]) sage: cone.rays() N(1, 0), N(0, 1) in 2-d lattice N sage: cone.Hilbert_coefficients([3,2]) (3, 2)
A more complicated example:
sage: N = ToricLattice(2) sage: cone = Cone([N(1,0), N(1,2)]) sage: cone.Hilbert_basis() N(1, 0), N(1, 2), N(1, 1) in 2-d lattice N sage: cone.Hilbert_coefficients(N(1,1)) (0, 0, 1)
The cone need not be strictly convex:
sage: N = ToricLattice(3) sage: cone = Cone([N(1,0,0), N(1,2,0), N(0,0,1), N(0,0,-1)]) sage: cone.Hilbert_basis() N(1, 2, 0), N(1, 0, 0), N(0, 0, 1), N(0, 0, -1), N(1, 1, 0) in 3-d lattice N sage: cone.Hilbert_coefficients(N(1,1,3)) (0, 0, 3, 0, 1)
- Z_operators_gens()#
Compute minimal generators of the Z-operators on this cone.
The Z-operators on a cone generalize the Z-matrices over the nonnegative orthant. They are simply negations of the
cross_positive_operators_gens()
.OUTPUT:
A list of \(n\)-by-\(n\) matrices where \(n\) is the ambient dimension of this cone. Each matrix \(L\) in the list has the property that \(s(L(x)) \le 0\) whenever \((x,s)\) is an element of this cone’s
discrete_complementarity_set()
.The returned matrices generate the cone of Z-operators on this cone; that is,
Any nonnegative linear combination of the returned matrices is a Z-operator on this cone.
Every Z-operator on this cone is some nonnegative linear combination of the returned matrices.
REFERENCES:
- adjacent()#
Return faces adjacent to
self
in the ambient face lattice.Two distinct faces \(F_1\) and \(F_2\) of the same face lattice are adjacent if all of the following conditions hold:
\(F_1\) and \(F_2\) have the same dimension \(d\);
\(F_1\) and \(F_2\) share a facet of dimension \(d-1\);
\(F_1\) and \(F_2\) are facets of some face of dimension \(d+1\), unless \(d\) is the dimension of the ambient structure.
OUTPUT:
tuple
ofcones
.
EXAMPLES:
sage: # needs sage.graphs sage: octant = Cone([(1,0,0), (0,1,0), (0,0,1)]) sage: octant.adjacent() () sage: one_face = octant.faces(1)[0] sage: len(one_face.adjacent()) 2 sage: one_face.adjacent()[1] 1-d face of 3-d cone in 3-d lattice N
Things are a little bit subtle with fans, as we illustrate below.
First, we create a fan from two cones in the plane:
sage: fan = Fan(cones=[(0,1), (1,2)], ....: rays=[(1,0), (0,1), (-1,0)]) sage: cone = fan.generating_cone(0) sage: len(cone.adjacent()) # needs sage.graphs 1
The second generating cone is adjacent to this one. Now we create the same fan, but embedded into the 3-dimensional space:
sage: fan = Fan(cones=[(0,1), (1,2)], ....: rays=[(1,0,0), (0,1,0), (-1,0,0)]) sage: cone = fan.generating_cone(0) sage: len(cone.adjacent()) # needs sage.graphs 1
The result is as before, since we still have:
sage: fan.dim() 2
Now we add another cone to make the fan 3-dimensional:
sage: fan = Fan(cones=[(0,1), (1,2), (3,)], ....: rays=[(1,0,0), (0,1,0), (-1,0,0), (0,0,1)]) sage: cone = fan.generating_cone(0) sage: len(cone.adjacent()) # needs sage.graphs 0
Since now
cone
has smaller dimension thanfan
, it and its adjacent cones must be facets of a bigger one, but sincecone
in this example is generating, it is not contained in any other.
- ambient()#
Return the ambient structure of
self
.OUTPUT:
cone or fan containing
self
as a face.
EXAMPLES:
sage: cone = Cone([(1,2,3), (4,6,5), (9,8,7)]) sage: cone.ambient() 3-d cone in 3-d lattice N sage: cone.ambient() is cone True sage: # needs sage.graphs sage: face = cone.faces(1)[0] sage: face 1-d face of 3-d cone in 3-d lattice N sage: face.ambient() 3-d cone in 3-d lattice N sage: face.ambient() is cone True
- ambient_ray_indices()#
Return indices of rays of the ambient structure generating
self
.OUTPUT:
increasing
tuple
of integers.
EXAMPLES:
sage: quadrant = Cone([(1,0), (0,1)]) sage: quadrant.ambient_ray_indices() (0, 1) sage: quadrant.facets()[1].ambient_ray_indices() # needs sage.graphs (1,)
- an_affine_basis()#
Return points in
self
that form a basis for the affine hull.EXAMPLES:
sage: quadrant = Cone([(1,0), (0,1)]) sage: quadrant.an_affine_basis() [(0, 0), (1, 0), (0, 1)] sage: ray = Cone([(1, 1)]) sage: ray.an_affine_basis() [(0, 0), (1, 1)] sage: line = Cone([(1,0), (-1,0)]) sage: line.an_affine_basis() [(1, 0), (0, 0)]
- cartesian_product(other, lattice=None)#
Return the Cartesian product of
self
withother
.INPUT:
other
– acone
;lattice
– (optional) the ambient lattice for the Cartesian product cone. By default, the direct sum of the ambient lattices ofself
andother
is constructed.
OUTPUT:
a
cone
.
EXAMPLES:
sage: c = Cone([(1,)]) sage: c.cartesian_product(c) 2-d cone in 2-d lattice N+N sage: _.rays() N+N(1, 0), N+N(0, 1) in 2-d lattice N+N
- contains(*args)#
Check if a given point is contained in
self
.INPUT:
anything. An attempt will be made to convert all arguments into a single element of the ambient space of
self
. If it fails,False
will be returned.
OUTPUT:
True
if the given point is contained inself
,False
otherwise.
EXAMPLES:
sage: c = Cone([(1,0), (0,1)]) sage: c.contains(c.lattice()(1,0)) True sage: c.contains((1,0)) True sage: c.contains((1,1)) True sage: c.contains(1,1) True sage: c.contains((-1,0)) False sage: c.contains(c.dual_lattice()(1,0)) # random output (warning) False sage: c.contains(c.dual_lattice()(1,0)) False sage: c.contains(1) False sage: c.contains(1/2, sqrt(3)) # needs sage.symbolic True sage: c.contains(-1/2, sqrt(3)) # needs sage.symbolic False
- cross_positive_operators_gens()#
Compute minimal generators of the cross-positive operators on this cone.
Any positive operator \(P\) on this cone will have \(s(P(x)) \ge 0\) whenever \(x\) is an element of this cone and \(s\) is an element of its dual. By contrast, the cross-positive operators need only satisfy that property on the
discrete_complementarity_set()
; that is, when \(x\) and \(s\) are “cross” (orthogonal).The cross-positive operators (on some fixed cone) themselves form a closed convex cone. This method computes and returns the generators of that cone as a list of matrices.
Cross-positive operators are also called exponentially-positive, since they become positive operators when exponentiated. Other equivalent names are resolvent-positive, essentially-positive, and quasimonotone.
OUTPUT:
A list of \(n\)-by-\(n\) matrices where \(n\) is the ambient dimension of this cone. Each matrix \(L\) in the list has the property that \(s(L(x)) \ge 0\) whenever \((x,s)\) is an element of this cone’s
discrete_complementarity_set()
.The returned matrices generate the cone of cross-positive operators on this cone; that is,
Any nonnegative linear combination of the returned matrices is cross-positive on this cone.
Every cross-positive operator on this cone is some nonnegative linear combination of the returned matrices.
REFERENCES:
EXAMPLES:
Cross-positive operators on the nonnegative orthant are negations of Z-matrices; that is, matrices whose off-diagonal elements are nonnegative:
sage: K = cones.nonnegative_orthant(2) sage: K.cross_positive_operators_gens() [ [0 1] [0 0] [1 0] [-1 0] [0 0] [ 0 0] [0 0], [1 0], [0 0], [ 0 0], [0 1], [ 0 -1] ] sage: K = Cone([(1,0,0,0), (0,1,0,0), (0,0,1,0), (0,0,0,1)]) sage: all(c[i][j] >= 0 for c in K.cross_positive_operators_gens() ....: for i in range(c.nrows()) ....: for j in range(c.ncols()) ....: if i != j) True
The trivial cone in a trivial space has no cross-positive operators:
sage: K = cones.trivial(0) sage: K.cross_positive_operators_gens() []
Every operator is a cross-positive operator on the ambient vector space:
sage: K = Cone([(1,), (-1,)]) sage: K.is_full_space() True sage: K.cross_positive_operators_gens() [[1], [-1]] sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) sage: K.is_full_space() True sage: K.cross_positive_operators_gens() [ [1 0] [-1 0] [0 1] [ 0 -1] [0 0] [ 0 0] [0 0] [ 0 0] [0 0], [ 0 0], [0 0], [ 0 0], [1 0], [-1 0], [0 1], [ 0 -1] ]
A non-obvious application is to find the cross-positive operators on the right half-plane [Or2018b]:
sage: K = Cone([(1,0), (0,1), (0,-1)]) sage: K.cross_positive_operators_gens() [ [1 0] [-1 0] [0 0] [ 0 0] [0 0] [ 0 0] [0 0], [ 0 0], [1 0], [-1 0], [0 1], [ 0 -1] ]
Cross-positive operators on a subspace are Lyapunov-like and vice-versa:
sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) sage: K.is_full_space() True sage: lls = span(vector(l.list()) ....: for l in K.lyapunov_like_basis()) sage: cs = span(vector(c.list()) ....: for c in K.cross_positive_operators_gens()) sage: cs == lls True
- discrete_complementarity_set()#
Compute a discrete complementarity set of this cone.
A discrete complementarity set of a cone is the set of all orthogonal pairs \((x,s)\) where \(x\) is in some fixed generating set of the cone, and \(s\) is in some fixed generating set of its dual. The generators chosen for this cone and its dual are simply their
rays()
.OUTPUT:
A tuple of pairs \((x,s)\) such that,
REFERENCES:
EXAMPLES:
Pairs of standard basis elements form a discrete complementarity set for the nonnegative orthant:
sage: K = cones.nonnegative_orthant(2) sage: K.discrete_complementarity_set() ((N(1, 0), M(0, 1)), (N(0, 1), M(1, 0)))
If a cone consists of a single ray, then the second components of a discrete complementarity set for that cone should generate the orthogonal complement of the ray:
sage: K = Cone([(1,0)]) sage: K.discrete_complementarity_set() ((N(1, 0), M(0, 1)), (N(1, 0), M(0, -1))) sage: K = Cone([(1,0,0)]) sage: K.discrete_complementarity_set() ((N(1, 0, 0), M(0, 1, 0)), (N(1, 0, 0), M(0, -1, 0)), (N(1, 0, 0), M(0, 0, 1)), (N(1, 0, 0), M(0, 0, -1)))
When a cone is the entire space, its dual is the trivial cone, so the only discrete complementarity set for it is empty:
sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) sage: K.is_full_space() True sage: K.discrete_complementarity_set() ()
Likewise for trivial cones, whose duals are the entire space:
sage: cones.trivial(0).discrete_complementarity_set() ()
- dual()#
Return the dual cone of
self
.OUTPUT:
cone
.
EXAMPLES:
sage: cone = Cone([(1,0), (-1,3)]) sage: cone.dual().rays() M(0, 1), M(3, 1) in 2-d lattice M
Now let’s look at a more complicated case:
sage: cone = Cone([(-2,-1,2), (4,1,0), (-4,-1,-5), (4,1,5)]) sage: cone.is_strictly_convex() False sage: cone.dim() 3 sage: cone.dual().rays() M(7, -18, -2), M(1, -4, 0) in 3-d lattice M sage: cone.dual().dual() is cone True
We correctly handle the degenerate cases:
sage: N = ToricLattice(2) sage: Cone([], lattice=N).dual().rays() # empty cone M( 1, 0), M(-1, 0), M( 0, 1), M( 0, -1) in 2-d lattice M sage: Cone([(1,0)], lattice=N).dual().rays() # ray in 2d M(1, 0), M(0, 1), M(0, -1) in 2-d lattice M sage: Cone([(1,0),(-1,0)], lattice=N).dual().rays() # line in 2d M(0, 1), M(0, -1) in 2-d lattice M sage: Cone([(1,0),(0,1)], lattice=N).dual().rays() # strictly convex cone M(0, 1), M(1, 0) in 2-d lattice M sage: Cone([(1,0),(-1,0),(0,1)], lattice=N).dual().rays() # half space M(0, 1) in 2-d lattice M sage: Cone([(1,0),(0,1),(-1,-1)], lattice=N).dual().rays() # whole space Empty collection in 2-d lattice M
- embed(cone)#
Return the cone equivalent to the given one, but sitting in
self
as a face.You may need to use this method before calling methods of
cone
that depend on the ambient structure, such asambient_ray_indices()
orfacet_of()
. The cone returned by this method will haveself
as ambient. Ifcone
does not represent a valid cone ofself
,ValueError
exception is raised.Note
This method is very quick if
self
is already the ambient structure ofcone
, so you can use without extra checks and performance hit even ifcone
is likely to sit inself
but in principle may not.INPUT:
cone
– acone
.
OUTPUT:
a
cone
, equivalent tocone
but sitting insideself
.
EXAMPLES:
Let’s take a 3-d cone on 4 rays:
sage: c = Cone([(1,0,1), (0,1,1), (-1,0,1), (0,-1,1)])
Then any ray generates a 1-d face of this cone, but if you construct such a face directly, it will not “sit” inside the cone:
sage: ray = Cone([(0,-1,1)]) sage: ray 1-d cone in 3-d lattice N sage: ray.ambient_ray_indices() (0,) sage: ray.adjacent() # needs sage.graphs () sage: ray.ambient() 1-d cone in 3-d lattice N
If we want to operate with this ray as a face of the cone, we need to embed it first:
sage: # needs sage.graphs sage: e_ray = c.embed(ray) sage: e_ray 1-d face of 3-d cone in 3-d lattice N sage: e_ray.rays() N(0, -1, 1) in 3-d lattice N sage: e_ray is ray False sage: e_ray.is_equivalent(ray) True sage: e_ray.ambient_ray_indices() (3,) sage: e_ray.adjacent() (1-d face of 3-d cone in 3-d lattice N, 1-d face of 3-d cone in 3-d lattice N) sage: e_ray.ambient() 3-d cone in 3-d lattice N
Not every cone can be embedded into a fixed ambient cone:
sage: c.embed(Cone([(0,0,1)])) Traceback (most recent call last): ... ValueError: 1-d cone in 3-d lattice N is not a face of 3-d cone in 3-d lattice N! sage: c.embed(Cone([(1,0,1), (-1,0,1)])) # needs sage.graphs Traceback (most recent call last): ... ValueError: 2-d cone in 3-d lattice N is not a face of 3-d cone in 3-d lattice N!
- face_lattice()#
Return the face lattice of
self
.This lattice will have the origin as the bottom (we do not include the empty set as a face) and this cone itself as the top.
OUTPUT:
finite poset
ofcones
.
EXAMPLES:
Let’s take a look at the face lattice of the first quadrant:
sage: quadrant = Cone([(1,0), (0,1)]) sage: L = quadrant.face_lattice() # needs sage.combinat sage.graphs sage: L # needs sage.combinat sage.graphs Finite lattice containing 4 elements with distinguished linear extension
To see all faces arranged by dimension, you can do this:
sage: for level in L.level_sets(): print(level) # needs sage.combinat sage.graphs [0-d face of 2-d cone in 2-d lattice N] [1-d face of 2-d cone in 2-d lattice N, 1-d face of 2-d cone in 2-d lattice N] [2-d cone in 2-d lattice N]
For a particular face you can look at its actual rays…
sage: face = L.level_sets()[1][0] # needs sage.combinat sage.graphs sage: face.rays() # needs sage.combinat sage.graphs N(1, 0) in 2-d lattice N
… or you can see the index of the ray of the original cone that corresponds to the above one:
sage: face.ambient_ray_indices() # needs sage.combinat sage.graphs (0,) sage: quadrant.ray(0) N(1, 0)
An alternative to extracting faces from the face lattice is to use
faces()
method:sage: face is quadrant.faces(dim=1)[0] # needs sage.combinat sage.graphs True
The advantage of working with the face lattice directly is that you can (relatively easily) get faces that are related to the given one:
sage: face = L.level_sets()[1][0] # needs sage.combinat sage.graphs sage: D = L.hasse_diagram() # needs sage.combinat sage.graphs sage: sorted(D.neighbors(face)) # needs sage.combinat sage.graphs [0-d face of 2-d cone in 2-d lattice N, 2-d cone in 2-d lattice N]
However, you can achieve some of this functionality using
facets()
,facet_of()
, andadjacent()
methods:sage: # needs sage.graphs sage: face = quadrant.faces(1)[0] sage: face 1-d face of 2-d cone in 2-d lattice N sage: face.rays() N(1, 0) in 2-d lattice N sage: face.facets() (0-d face of 2-d cone in 2-d lattice N,) sage: face.facet_of() (2-d cone in 2-d lattice N,) sage: face.adjacent() (1-d face of 2-d cone in 2-d lattice N,) sage: face.adjacent()[0].rays() N(0, 1) in 2-d lattice N
Note that if
cone
is a face ofsupercone
, then the face lattice ofcone
consists of (appropriate) faces ofsupercone
:sage: # needs sage.combinat sage.graphs sage: supercone = Cone([(1,2,3,4), (5,6,7,8), ....: (1,2,4,8), (1,3,9,7)]) sage: supercone.face_lattice() Finite lattice containing 16 elements with distinguished linear extension sage: supercone.face_lattice().top() 4-d cone in 4-d lattice N sage: cone = supercone.facets()[0] sage: cone 3-d face of 4-d cone in 4-d lattice N sage: cone.face_lattice() Finite poset containing 8 elements with distinguished linear extension sage: cone.face_lattice().bottom() 0-d face of 4-d cone in 4-d lattice N sage: cone.face_lattice().top() 3-d face of 4-d cone in 4-d lattice N sage: cone.face_lattice().top() == cone True
- faces(dim=None, codim=None)#
Return faces of
self
of specified (co)dimension.INPUT:
dim
– integer, dimension of the requested faces;codim
– integer, codimension of the requested faces.
Note
You can specify at most one parameter. If you don’t give any, then all faces will be returned.
OUTPUT:
if either
dim
orcodim
is given, the output will be atuple
ofcones
;if neither
dim
norcodim
is given, the output will be thetuple
of tuples as above, giving faces of all existing dimensions. If you care about inclusion relations between faces, consider usingface_lattice()
oradjacent()
,facet_of()
, andfacets()
.
EXAMPLES:
Let’s take a look at the faces of the first quadrant:
sage: quadrant = Cone([(1,0), (0,1)]) sage: quadrant.faces() # needs sage.graphs ((0-d face of 2-d cone in 2-d lattice N,), (1-d face of 2-d cone in 2-d lattice N, 1-d face of 2-d cone in 2-d lattice N), (2-d cone in 2-d lattice N,)) sage: quadrant.faces(dim=1) # needs sage.graphs (1-d face of 2-d cone in 2-d lattice N, 1-d face of 2-d cone in 2-d lattice N) sage: face = quadrant.faces(dim=1)[0] # needs sage.graphs
Now you can look at the actual rays of this face…
sage: face.rays() # needs sage.graphs N(1, 0) in 2-d lattice N
… or you can see indices of the rays of the original cone that correspond to the above ray:
sage: face.ambient_ray_indices() # needs sage.graphs (0,) sage: quadrant.ray(0) N(1, 0)
Note that it is OK to ask for faces of too small or high dimension:
sage: quadrant.faces(-1) # needs sage.graphs () sage: quadrant.faces(3) # needs sage.graphs ()
In the case of non-strictly convex cones even faces of small non-negative dimension may be missing:
sage: # needs sage.graphs sage: halfplane = Cone([(1,0), (0,1), (-1,0)]) sage: halfplane.faces(0) () sage: halfplane.faces() ((1-d face of 2-d cone in 2-d lattice N,), (2-d cone in 2-d lattice N,)) sage: plane = Cone([(1,0), (0,1), (-1,-1)]) sage: plane.faces(1) () sage: plane.faces() ((2-d cone in 2-d lattice N,),)
- facet_normals()#
Return inward normals to facets of
self
.Note
For a not full-dimensional cone facet normals will specify hyperplanes whose intersections with the space spanned by
self
give facets ofself
.For a not strictly convex cone facet normals will be orthogonal to the linear subspace of
self
, i.e. they always will be elements of the dual cone ofself
.The order of normals is random, but consistent with
facets()
.
OUTPUT:
If the ambient
lattice()
ofself
is atoric lattice
, the facet normals will be elements of the dual lattice. If it is a general lattice (likeZZ^n
) that does not have adual()
method, the facet normals will be returned as integral vectors.EXAMPLES:
sage: cone = Cone([(1,0), (-1,3)]) sage: cone.facet_normals() M(0, 1), M(3, 1) in 2-d lattice M
Now let’s look at a more complicated case:
sage: cone = Cone([(-2,-1,2), (4,1,0), (-4,-1,-5), (4,1,5)]) sage: cone.is_strictly_convex() False sage: cone.dim() 3 sage: cone.linear_subspace().dimension() 1 sage: lsg = (QQ^3)(cone.linear_subspace().gen(0)); lsg (1, 1/4, 5/4) sage: cone.facet_normals() M(7, -18, -2), M(1, -4, 0) in 3-d lattice M sage: [lsg*normal for normal in cone.facet_normals()] [0, 0]
A lattice that does not have a
dual()
method:sage: Cone([(1,1),(0,1)], lattice=ZZ^2).facet_normals() (-1, 1), ( 1, 0) in Ambient free module of rank 2 over the principal ideal domain Integer Ring
We correctly handle the degenerate cases:
sage: N = ToricLattice(2) sage: Cone([], lattice=N).facet_normals() # empty cone Empty collection in 2-d lattice M sage: Cone([(1,0)], lattice=N).facet_normals() # ray in 2d M(1, 0) in 2-d lattice M sage: Cone([(1,0),(-1,0)], lattice=N).facet_normals() # line in 2d Empty collection in 2-d lattice M sage: Cone([(1,0),(0,1)], lattice=N).facet_normals() # strictly convex cone M(0, 1), M(1, 0) in 2-d lattice M sage: Cone([(1,0),(-1,0),(0,1)], lattice=N).facet_normals() # half space M(0, 1) in 2-d lattice M sage: Cone([(1,0),(0,1),(-1,-1)], lattice=N).facet_normals() # whole space Empty collection in 2-d lattice M
- facet_of()#
Return cones of the ambient face lattice having
self
as a facet.OUTPUT:
tuple
ofcones
.
EXAMPLES:
sage: # needs sage.graphs sage: octant = Cone([(1,0,0), (0,1,0), (0,0,1)]) sage: octant.facet_of() () sage: one_face = octant.faces(1)[0] sage: len(one_face.facet_of()) 2 sage: one_face.facet_of()[1] 2-d face of 3-d cone in 3-d lattice N
While fan is the top element of its own cone lattice, which is a variant of a face lattice, we do not refer to cones as its facets:
sage: fan = Fan([octant]) # needs sage.graphs sage: fan.generating_cone(0).facet_of() # needs sage.graphs ()
Subcones of generating cones work as before:
sage: one_cone = fan(1)[0] # needs sage.graphs sage: len(one_cone.facet_of()) # needs sage.graphs 2
- facets()#
Return facets (faces of codimension 1) of
self
.OUTPUT:
tuple
ofcones
.
EXAMPLES:
sage: quadrant = Cone([(1,0), (0,1)]) sage: quadrant.facets() # needs sage.graphs (1-d face of 2-d cone in 2-d lattice N, 1-d face of 2-d cone in 2-d lattice N)
- incidence_matrix()#
Return the incidence matrix.
Note
The columns correspond to facets/facet normals in the order of
facet_normals()
, the rows correspond to the rays in the order ofrays()
.EXAMPLES:
sage: octant = Cone([(1,0,0), (0,1,0), (0,0,1)]) sage: octant.incidence_matrix() [0 1 1] [1 0 1] [1 1 0] sage: halfspace = Cone([(1,0,0), (0,1,0), (-1,-1,0), (0,0,1)]) sage: halfspace.incidence_matrix() [0] [1] [1] [1] [1]
- interior()#
Return the interior of
self
.OUTPUT:
either
self
, an empty polyhedron, or an instance ofRelativeInterior
.
EXAMPLES:
sage: c = Cone([(1,0,0), (0,1,0)]); c 2-d cone in 3-d lattice N sage: c.interior() The empty polyhedron in ZZ^3 sage: origin = cones.trivial(2); origin 0-d cone in 2-d lattice N sage: origin.interior() The empty polyhedron in ZZ^2 sage: K = cones.nonnegative_orthant(2); K 2-d cone in 2-d lattice N sage: K.interior() Relative interior of 2-d cone in 2-d lattice N sage: K2 = Cone([(1,0),(-1,0),(0,1),(0,-1)]); K2 2-d cone in 2-d lattice N sage: K2.interior() is K2 True
- interior_contains(*args)#
Check if a given point is contained in the interior of
self
.For a cone of strictly lower-dimension than the ambient space, the interior is always empty. You probably want to use
relative_interior_contains()
in this case.INPUT:
anything. An attempt will be made to convert all arguments into a single element of the ambient space of
self
. If it fails,False
will be returned.
OUTPUT:
True
if the given point is contained in the interior ofself
,False
otherwise.
EXAMPLES:
sage: c = Cone([(1,0), (0,1)]) sage: c.contains((1,1)) True sage: c.interior_contains((1,1)) True sage: c.contains((1,0)) True sage: c.interior_contains((1,0)) False
- intersection(other)#
Compute the intersection of two cones.
INPUT:
other
-cone
.
OUTPUT:
cone
.
Raises
ValueError
if the ambient space dimensions are not compatible.EXAMPLES:
sage: cone1 = Cone([(1,0), (-1, 3)]) sage: cone2 = Cone([(-1,0), (2, 5)]) sage: cone1.intersection(cone2).rays() N(-1, 3), N( 2, 5) in 2-d lattice N
The intersection can also be expressed using the operator
&
:sage: (cone1 & cone2).rays() N(-1, 3), N( 2, 5) in 2-d lattice N
It is OK to intersect cones living in sublattices of the same ambient lattice:
sage: N = cone1.lattice() sage: Ns = N.submodule([(1,1)]) sage: cone3 = Cone([(1,1)], lattice=Ns) sage: I = cone1.intersection(cone3) sage: I.rays() N(1, 1) in Sublattice <N(1, 1)> sage: I.lattice() Sublattice <N(1, 1)>
But you cannot intersect cones from incompatible lattices without explicit conversion:
sage: cone1.intersection(cone1.dual()) Traceback (most recent call last): ... ValueError: 2-d lattice N and 2-d lattice M have different ambient lattices! sage: cone1.intersection(Cone(cone1.dual().rays(), N)).rays() N(3, 1), N(0, 1) in 2-d lattice N
An intersection with a polyhedron returns a polyhedron:
sage: cone = Cone([(1,0), (-1,0), (0,1)]) sage: p = polytopes.hypercube(2) sage: cone & p A 2-dimensional polyhedron in ZZ^2 defined as the convex hull of 4 vertices sage: sorted(_.vertices_list()) [[-1, 0], [-1, 1], [1, 0], [1, 1]]
- is_compact()#
Checks if the cone has no rays.
OUTPUT:
True
if the cone has no rays,False
otherwise.
EXAMPLES:
sage: c0 = cones.trivial(3) sage: c0.is_trivial() True sage: c0.nrays() 0
- is_empty()#
Return whether
self
is the empty set.Because a cone always contains the origin, this method returns
False
.EXAMPLES:
sage: trivial_cone = cones.trivial(3) sage: trivial_cone.is_empty() False
- is_equivalent(other)#
Check if
self
is “mathematically” the same asother
.INPUT:
other
- cone.
OUTPUT:
True
ifself
andother
define the same cones as sets of points in the same lattice,False
otherwise.
There are three different equivalences between cones \(C_1\) and \(C_2\) in the same lattice:
They have the same generating rays in the same order. This is tested by
C1 == C2
.They describe the same sets of points. This is tested by
C1.is_equivalent(C2)
.They are in the same orbit of \(GL(n,\ZZ)\) (and, therefore, correspond to isomorphic affine toric varieties). This is tested by
C1.is_isomorphic(C2)
.
EXAMPLES:
sage: cone1 = Cone([(1,0), (-1, 3)]) sage: cone2 = Cone([(-1,3), (1, 0)]) sage: cone1.rays() N( 1, 0), N(-1, 3) in 2-d lattice N sage: cone2.rays() N(-1, 3), N( 1, 0) in 2-d lattice N sage: cone1 == cone2 False sage: cone1.is_equivalent(cone2) True
- is_face_of(cone)#
Check if
self
forms a face of anothercone
.INPUT:
cone
– cone.
OUTPUT:
True
ifself
is a face ofcone
,False
otherwise.
EXAMPLES:
sage: quadrant = Cone([(1,0), (0,1)]) sage: cone1 = Cone([(1,0)]) sage: cone2 = Cone([(1,2)]) sage: quadrant.is_face_of(quadrant) True sage: cone1.is_face_of(quadrant) True sage: cone2.is_face_of(quadrant) False
Being a face means more than just saturating a facet inequality:
sage: octant = Cone([(1,0,0), (0,1,0), (0,0,1)]) sage: cone = Cone([(2,1,0),(1,2,0)]) sage: cone.is_face_of(octant) False
- is_full_dimensional()#
Check if this cone is solid.
A cone is said to be solid if it has nonempty interior. That is, if its extreme rays span the entire ambient space.
An alias is
is_full_dimensional()
.OUTPUT:
True
if this cone is solid, andFalse
otherwise.See also
EXAMPLES:
The nonnegative orthant is always solid:
sage: quadrant = cones.nonnegative_orthant(2) sage: quadrant.is_solid() True sage: octant = Cone([(1,0,0), (0,1,0), (0,0,1)]) sage: octant.is_solid() True
However, if we embed the two-dimensional nonnegative quadrant into three-dimensional space, then the resulting cone no longer has interior, so it is not solid:
sage: quadrant = Cone([(1,0,0), (0,1,0)]) sage: quadrant.is_solid() False
- is_full_space()#
Check if this cone is equal to its ambient vector space.
An alias is
is_universe()
.OUTPUT:
True
if this cone equals its entire ambient vector space andFalse
otherwise.EXAMPLES:
A single ray in two dimensions is not equal to the entire space:
sage: K = Cone([(1,0)]) sage: K.is_full_space() False
Neither is the nonnegative orthant:
sage: K = cones.nonnegative_orthant(2) sage: K.is_full_space() False
The right half-space contains a vector subspace, but it is still not equal to the entire space:
sage: K = Cone([(1,0), (-1,0), (0,1)]) sage: K.is_full_space() False
However, if we allow conic combinations of both axes, then the resulting cone is the entire two-dimensional space:
sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) sage: K.is_full_space() True
- is_isomorphic(other)#
Check if
self
is in the same \(GL(n, \ZZ)\)-orbit asother
.INPUT:
other
- cone.
OUTPUT:
True
ifself
andother
are in the same \(GL(n, \ZZ)\)-orbit,False
otherwise.
There are three different equivalences between cones \(C_1\) and \(C_2\) in the same lattice:
They have the same generating rays in the same order. This is tested by
C1 == C2
.They describe the same sets of points. This is tested by
C1.is_equivalent(C2)
.They are in the same orbit of \(GL(n,\ZZ)\) (and, therefore, correspond to isomorphic affine toric varieties). This is tested by
C1.is_isomorphic(C2)
.
EXAMPLES:
sage: cone1 = Cone([(1,0), (0, 3)]) sage: m = matrix(ZZ, [(1, -5), (-1, 4)]) # a GL(2,ZZ)-matrix sage: cone2 = Cone( m*r for r in cone1.rays() ) sage: cone1.is_isomorphic(cone2) True sage: cone1 = Cone([(1,0), (0, 3)]) sage: cone2 = Cone([(-1,3), (1, 0)]) sage: cone1.is_isomorphic(cone2) False
- is_proper()#
Check if this cone is proper.
A cone is said to be proper if it is closed, convex, solid, and contains no lines. This cone is assumed to be closed and convex; therefore it is proper if it is solid and contains no lines.
OUTPUT:
True
if this cone is proper, andFalse
otherwise.See also
EXAMPLES:
The nonnegative orthant is always proper:
sage: quadrant = cones.nonnegative_orthant(2) sage: quadrant.is_proper() True sage: octant = Cone([(1,0,0), (0,1,0), (0,0,1)]) sage: octant.is_proper() True
However, if we embed the two-dimensional nonnegative quadrant into three-dimensional space, then the resulting cone no longer has interior, so it is not solid, and thus not proper:
sage: quadrant = Cone([(1,0,0), (0,1,0)]) sage: quadrant.is_proper() False
Likewise, a half-space contains at least one line, so it is not proper:
sage: halfspace = Cone([(1,0), (0,1), (-1,0)]) sage: halfspace.is_proper() False
- is_relatively_open()#
Return whether
self
is relatively open.OUTPUT:
Boolean.
EXAMPLES:
sage: K = cones.nonnegative_orthant(3) sage: K.is_relatively_open() False sage: K1 = Cone([(1,0), (-1,0)]); K1 1-d cone in 2-d lattice N sage: K1.is_relatively_open() True
- is_simplicial()#
Check if
self
is simplicial.A cone is called simplicial if primitive vectors along its generating rays form a part of a rational basis of the ambient space.
OUTPUT:
True
ifself
is simplicial,False
otherwise.
EXAMPLES:
sage: cone1 = Cone([(1,0), (0, 3)]) sage: cone2 = Cone([(1,0), (0, 3), (-1,-1)]) sage: cone1.is_simplicial() True sage: cone2.is_simplicial() False
- is_smooth()#
Check if
self
is smooth.A cone is called smooth if primitive vectors along its generating rays form a part of an integral basis of the ambient space. Equivalently, they generate the whole lattice on the linear subspace spanned by the rays.
OUTPUT:
True
ifself
is smooth,False
otherwise.
EXAMPLES:
sage: cone1 = Cone([(1,0), (0, 1)]) sage: cone2 = Cone([(1,0), (-1, 3)]) sage: cone1.is_smooth() True sage: cone2.is_smooth() False
The following cones are the same up to a \(SL(2,\ZZ)\) coordinate transformation:
sage: Cone([(1,0,0), (2,1,-1)]).is_smooth() True sage: Cone([(1,0,0), (2,1,1)]).is_smooth() True sage: Cone([(1,0,0), (2,1,2)]).is_smooth() True
- is_solid()#
Check if this cone is solid.
A cone is said to be solid if it has nonempty interior. That is, if its extreme rays span the entire ambient space.
An alias is
is_full_dimensional()
.OUTPUT:
True
if this cone is solid, andFalse
otherwise.See also
EXAMPLES:
The nonnegative orthant is always solid:
sage: quadrant = cones.nonnegative_orthant(2) sage: quadrant.is_solid() True sage: octant = Cone([(1,0,0), (0,1,0), (0,0,1)]) sage: octant.is_solid() True
However, if we embed the two-dimensional nonnegative quadrant into three-dimensional space, then the resulting cone no longer has interior, so it is not solid:
sage: quadrant = Cone([(1,0,0), (0,1,0)]) sage: quadrant.is_solid() False
- is_strictly_convex()#
Check if
self
is strictly convex.A cone is called strictly convex if it does not contain any lines.
OUTPUT:
True
ifself
is strictly convex,False
otherwise.
EXAMPLES:
sage: cone1 = Cone([(1,0), (0, 1)]) sage: cone2 = Cone([(1,0), (-1, 0)]) sage: cone1.is_strictly_convex() True sage: cone2.is_strictly_convex() False
- is_trivial()#
Checks if the cone has no rays.
OUTPUT:
True
if the cone has no rays,False
otherwise.
EXAMPLES:
sage: c0 = cones.trivial(3) sage: c0.is_trivial() True sage: c0.nrays() 0
- is_universe()#
Check if this cone is equal to its ambient vector space.
An alias is
is_universe()
.OUTPUT:
True
if this cone equals its entire ambient vector space andFalse
otherwise.EXAMPLES:
A single ray in two dimensions is not equal to the entire space:
sage: K = Cone([(1,0)]) sage: K.is_full_space() False
Neither is the nonnegative orthant:
sage: K = cones.nonnegative_orthant(2) sage: K.is_full_space() False
The right half-space contains a vector subspace, but it is still not equal to the entire space:
sage: K = Cone([(1,0), (-1,0), (0,1)]) sage: K.is_full_space() False
However, if we allow conic combinations of both axes, then the resulting cone is the entire two-dimensional space:
sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) sage: K.is_full_space() True
- lineality()#
Return the lineality of this cone.
The lineality of a cone is the dimension of the largest linear subspace contained in that cone.
OUTPUT:
A nonnegative integer; the dimension of the largest subspace contained within this cone.
REFERENCES:
EXAMPLES:
The lineality of the nonnegative orthant is zero, since it clearly contains no lines:
sage: K = cones.nonnegative_orthant(3) sage: K.lineality() 0
However, if we add another ray so that the entire \(x\)-axis belongs to the cone, then the resulting cone will have lineality one:
sage: K = Cone([(1,0,0), (-1,0,0), (0,1,0), (0,0,1)]) sage: K.lineality() 1
If our cone is all of \(\mathbb{R}^{2}\), then its lineality is equal to the dimension of the ambient space (i.e. two):
sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) sage: K.is_full_space() True sage: K.lineality() 2 sage: K.lattice_dim() 2
Per the definition, the lineality of the trivial cone in a trivial space is zero:
sage: K = cones.trivial(0) sage: K.lineality() 0
- linear_subspace()#
Return the largest linear subspace contained inside of
self
.OUTPUT:
subspace of the ambient space of
self
.
EXAMPLES:
sage: halfplane = Cone([(1,0), (0,1), (-1,0)]) sage: halfplane.linear_subspace() Vector space of degree 2 and dimension 1 over Rational Field Basis matrix: [1 0]
- lines()#
Return lines generating the linear subspace of
self
.OUTPUT:
tuple
of primitive vectors in the lattice ofself
giving directions of lines that span the linear subspace ofself
. These lines are arbitrary, but fixed. If you do not care about the order, see alsoline_set()
.
EXAMPLES:
sage: halfplane = Cone([(1,0), (0,1), (-1,0)]) sage: halfplane.lines() N(1, 0) in 2-d lattice N sage: fullplane = Cone([(1,0), (0,1), (-1,-1)]) sage: fullplane.lines() N(0, 1), N(1, 0) in 2-d lattice N
- lyapunov_like_basis()#
Compute a basis of Lyapunov-like transformations on this cone.
A linear transformation \(L\) is said to be Lyapunov-like on this cone if \(L(x)\) and \(s\) are orthogonal for every pair \((x,s)\) in its
discrete_complementarity_set()
. The set of all such transformations forms a vector space, namely the Lie algebra of the automorphism group of this cone.OUTPUT:
A list of matrices forming a basis for the space of all Lyapunov-like transformations on this cone.
REFERENCES:
EXAMPLES:
Every transformation is Lyapunov-like on the trivial cone:
sage: K = cones.trivial(2) sage: M = MatrixSpace(K.lattice().base_field(), K.lattice_dim()) sage: list(M.basis()) == K.lyapunov_like_basis() True
And by duality, every transformation is Lyapunov-like on the ambient space:
sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) sage: K.is_full_space() True sage: M = MatrixSpace(K.lattice().base_field(), K.lattice_dim()) sage: list(M.basis()) == K.lyapunov_like_basis() True
However, in a trivial space, there are no non-trivial linear maps, so there can be no Lyapunov-like basis:
sage: K = cones.trivial(0) sage: K.lyapunov_like_basis() []
The Lyapunov-like transformations on the nonnegative orthant are diagonal matrices:
sage: K = cones.nonnegative_orthant(1) sage: K.lyapunov_like_basis() [[1]] sage: K = cones.nonnegative_orthant(2) sage: K.lyapunov_like_basis() [ [1 0] [0 0] [0 0], [0 1] ] sage: K = cones.nonnegative_orthant(3) sage: K.lyapunov_like_basis() [ [1 0 0] [0 0 0] [0 0 0] [0 0 0] [0 1 0] [0 0 0] [0 0 0], [0 0 0], [0 0 1] ]
Only the identity matrix is Lyapunov-like on the pyramids defined by the one- and infinity-norms [RNPA2011]:
sage: l31 = Cone([(1,0,1), (0,-1,1), (-1,0,1), (0,1,1)]) sage: l31.lyapunov_like_basis() [ [1 0 0] [0 1 0] [0 0 1] ] sage: l3infty = Cone([(0,1,1), (1,0,1), (0,-1,1), (-1,0,1)]) sage: l3infty.lyapunov_like_basis() [ [1 0 0] [0 1 0] [0 0 1] ]
- lyapunov_rank()#
Compute the Lyapunov rank of this cone.
The Lyapunov rank of a cone is the dimension of the space of its Lyapunov-like transformations — that is, the length of a
lyapunov_like_basis()
. Equivalently, the Lyapunov rank is the dimension of the Lie algebra of the automorphism group of the cone.OUTPUT:
A nonnegative integer representing the Lyapunov rank of this cone.
If the ambient space is trivial, then the Lyapunov rank will be zero. On the other hand, if the dimension of the ambient vector space is \(n > 0\), then the resulting Lyapunov rank will be between \(1\) and \(n^2\) inclusive. If this cone
is_proper()
, then that upper bound reduces from \(n^2\) to \(n\). A Lyapunov rank of \(n-1\) is not possible (by Lemma 6 [Or2017]) in either case.ALGORITHM:
Algorithm 3 [Or2017] is used. Every closed convex cone is isomorphic to a Cartesian product of a proper cone, a subspace, and a trivial cone. The Lyapunov ranks of the subspace and trivial cone are easy to compute. Essentially, we “peel off” those easy parts of the cone and compute their Lyapunov ranks separately. We then compute the rank of the proper cone by counting a
lyapunov_like_basis()
for it. Summing the individual ranks gives the Lyapunov rank of the original cone.REFERENCES:
EXAMPLES:
The Lyapunov rank of the nonnegative orthant is the same as the dimension of the ambient space [RNPA2011]:
sage: positives = cones.nonnegative_orthant(1) sage: positives.lyapunov_rank() 1 sage: quadrant = cones.nonnegative_orthant(2) sage: quadrant.lyapunov_rank() 2 sage: octant = cones.nonnegative_orthant(3) sage: octant.lyapunov_rank() 3
A vector space of dimension \(n\) has Lyapunov rank \(n^{2}\) [Or2017]:
sage: Q5 = VectorSpace(QQ, 5) sage: gs = Q5.basis() + [-r for r in Q5.basis()] sage: K = Cone(gs) sage: K.lyapunov_rank() 25
A pyramid in three dimensions has Lyapunov rank one [RNPA2011]:
sage: l31 = Cone([(1,0,1), (0,-1,1), (-1,0,1), (0,1,1)]) sage: l31.lyapunov_rank() 1 sage: l3infty = Cone([(0,1,1), (1,0,1), (0,-1,1), (-1,0,1)]) sage: l3infty.lyapunov_rank() 1
A ray in \(n\) dimensions has Lyapunov rank \(n^{2} - n + 1\) [Or2017]:
sage: K = Cone([(1,0,0,0,0)]) sage: K.lyapunov_rank() 21 sage: K.lattice_dim()**2 - K.lattice_dim() + 1 21
A subspace of dimension \(m\) in an \(n\)-dimensional ambient space has Lyapunov rank \(n^{2} - m(n - m)\) [Or2017]:
sage: e1 = vector(QQ, [1,0,0,0,0]) sage: e2 = vector(QQ, [0,1,0,0,0]) sage: z = (0,0,0,0,0) sage: K = Cone([e1, -e1, e2, -e2, z, z, z]) sage: K.lyapunov_rank() 19 sage: K.lattice_dim()**2 - K.dim()*K.codim() 19
Lyapunov rank is additive on a product of proper cones [RNPA2011]:
sage: l31 = Cone([(1,0,1), (0,-1,1), (-1,0,1), (0,1,1)]) sage: octant = Cone([(1,0,0), (0,1,0), (0,0,1)]) sage: K = l31.cartesian_product(octant) sage: K.lyapunov_rank() 4 sage: l31.lyapunov_rank() + octant.lyapunov_rank() 4
Two linearly-isomorphic cones have the same Lyapunov rank [RNPA2011]. A cone linearly-isomorphic to the nonnegative octant will have Lyapunov rank
3
:sage: K = Cone([(1,2,3), (-1,1,0), (1,0,6)]) sage: K.lyapunov_rank() 3
Lyapunov rank is invariant under
dual()
[RNPA2011]:sage: K = Cone([(2,2,4), (-1,9,0), (2,0,6)]) sage: K.lyapunov_rank() == K.dual().lyapunov_rank() True
- orthogonal_sublattice(*args, **kwds)#
The sublattice (in the dual lattice) orthogonal to the sublattice spanned by the cone.
Let \(M=\)
self.dual_lattice()
be the lattice dual to the ambient lattice of the given cone \(\sigma\). Then, in the notation of [Ful1993], this method returns the sublattice\[M(\sigma) \stackrel{\text{def}}{=} \sigma^\perp \cap M \subset M\]INPUT:
either nothing or something that can be turned into an element of this lattice.
OUTPUT:
if no arguments were given, a
toric sublattice
, otherwise the corresponding element of it.
EXAMPLES:
sage: c = Cone([(1,1,1), (1,-1,1), (-1,-1,1), (-1,1,1)]) sage: c.orthogonal_sublattice() Sublattice <> sage: c12 = Cone([(1,1,1), (1,-1,1)]) sage: c12.sublattice() Sublattice <N(1, 1, 1), N(0, -1, 0)> sage: c12.orthogonal_sublattice() Sublattice <M(1, 0, -1)>
- plot(**options)#
Plot
self
.INPUT:
any options for toric plots (see
toric_plotter.options
), none are mandatory.
OUTPUT:
a plot.
EXAMPLES:
sage: quadrant = Cone([(1,0), (0,1)]) sage: quadrant.plot() # needs sage.plot Graphics object consisting of 9 graphics primitives
- polyhedron(**kwds)#
Return the polyhedron associated to
self
.Mathematically this polyhedron is the same as
self
.OUTPUT:
EXAMPLES:
sage: quadrant = Cone([(1,0), (0,1)]) sage: quadrant.polyhedron() A 2-dimensional polyhedron in ZZ^2 defined as the convex hull of 1 vertex and 2 rays sage: line = Cone([(1,0), (-1,0)]) sage: line.polyhedron() A 1-dimensional polyhedron in ZZ^2 defined as the convex hull of 1 vertex and 1 line
Here is an example of a trivial cone (see github issue #10237):
sage: origin = Cone([], lattice=ZZ^2) sage: origin.polyhedron() A 0-dimensional polyhedron in ZZ^2 defined as the convex hull of 1 vertex
- positive_operators_gens(K2=None)#
Compute minimal generators of the positive operators on this cone.
A linear operator on a cone is positive if the image of the cone under the operator is a subset of the cone. This concept can be extended to two cones: the image of the first cone under a positive operator is a subset of the second cone, which may live in a different space.
The positive operators (on one or two fixed cones) themselves form a closed convex cone. This method computes and returns the generators of that cone as a list of matrices.
INPUT:
K2
– (default:self
) the codomain cone; the image of this cone under the returned generators is a subset ofK2
.
OUTPUT:
A list of \(m\)-by-\(n\) matrices where \(m\) is the ambient dimension of
K2
and \(n\) is the ambient dimension of this cone. Each matrix \(P\) in the list has the property that \(P(x)\) is an element ofK2
whenever \(x\) is an element of this cone.The returned matrices generate the cone of positive operators from this cone to
K2
; that is,Any nonnegative linear combination of the returned matrices sends elements of this cone to
K2
.Every positive operator on this cone (with respect to
K2
) is some nonnegative linear combination of the returned matrices.
ALGORITHM:
Computing positive operators directly is difficult, but computing their dual is straightforward using the generators of Berman and Gaiha. We construct the dual of the positive operators, and then return the dual of that, which is guaranteed to be the desired positive operators because everything is closed, convex, and polyhedral.
REFERENCES:
EXAMPLES:
Positive operators on the nonnegative orthant are nonnegative matrices:
sage: K = Cone([(1,)]) sage: K.positive_operators_gens() [[1]] sage: K = Cone([(1,0), (0,1)]) sage: K.positive_operators_gens() [ [1 0] [0 1] [0 0] [0 0] [0 0], [0 0], [1 0], [0 1] ]
The trivial cone in a trivial space has no positive operators:
sage: K = cones.trivial(0) sage: K.positive_operators_gens() []
Every operator is positive on the trivial cone:
sage: K = cones.trivial(1) sage: K.positive_operators_gens() [[1], [-1]] sage: K = cones.trivial(2) sage: K.is_trivial() True sage: K.positive_operators_gens() [ [1 0] [-1 0] [0 1] [ 0 -1] [0 0] [ 0 0] [0 0] [ 0 0] [0 0], [ 0 0], [0 0], [ 0 0], [1 0], [-1 0], [0 1], [ 0 -1] ]
Every operator is positive on the ambient vector space:
sage: K = Cone([(1,), (-1,)]) sage: K.is_full_space() True sage: K.positive_operators_gens() [[1], [-1]] sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) sage: K.is_full_space() True sage: K.positive_operators_gens() [ [1 0] [-1 0] [0 1] [ 0 -1] [0 0] [ 0 0] [0 0] [ 0 0] [0 0], [ 0 0], [0 0], [ 0 0], [1 0], [-1 0], [0 1], [ 0 -1] ]
A non-obvious application is to find the positive operators on the right half-plane [Or2018b]:
sage: K = Cone([(1,0), (0,1), (0,-1)]) sage: K.positive_operators_gens() [ [1 0] [0 0] [ 0 0] [0 0] [ 0 0] [0 0], [1 0], [-1 0], [0 1], [ 0 -1] ]
- random_element(ring=Integer Ring)#
Return a random element of this cone.
All elements of a convex cone can be represented as a nonnegative linear combination of its generators. A random element is thus constructed by assigning random nonnegative weights to the generators of this cone. By default, these weights are integral and the resulting random element will live in the same lattice as the cone.
The random nonnegative weights are chosen from
ring
which defaults toZZ
. Whenring
is notZZ
, the random element returned will be a vector. Only the ringsZZ
andQQ
are currently supported.INPUT:
ring
– (default:ZZ
) the ring from which the random generator weights are chosen; eitherZZ
orQQ
.
OUTPUT:
Either a lattice element or vector contained in both this cone and its ambient vector space. If
ring
isZZ
, a lattice element is returned; otherwise a vector is returned. Ifring
is neitherZZ
norQQ
, then aNotImplementedError
is raised.EXAMPLES:
The trivial element
()
is always returned in a trivial space:sage: K = cones.trivial(0) sage: K.random_element() N() sage: K.random_element(ring=QQ) ()
A random element of the trivial cone in a nontrivial space is zero:
sage: K = cones.trivial(3) sage: K.random_element() N(0, 0, 0) sage: K.random_element(ring=QQ) (0, 0, 0)
A random element of the nonnegative orthant should have all components nonnegative:
sage: K = cones.nonnegative_orthant(3) sage: all(x >= 0 for x in K.random_element()) True sage: all(x >= 0 for x in K.random_element(ring=QQ)) True
If
ring
is notZZ
orQQ
, an error is raised:sage: K = Cone([(1,0), (0,1)]) sage: K.random_element(ring=RR) Traceback (most recent call last): ... NotImplementedError: ring must be either ZZ or QQ.
- relative_interior()#
Return the relative interior of
self
.OUTPUT:
either
self
or an instance ofRelativeInterior
.
EXAMPLES:
sage: c = Cone([(1,0,0), (0,1,0)]); c 2-d cone in 3-d lattice N sage: c.relative_interior() Relative interior of 2-d cone in 3-d lattice N sage: origin = cones.trivial(2); origin 0-d cone in 2-d lattice N sage: origin.relative_interior() is origin True sage: K1 = Cone([(1,0), (-1,0)]); K1 1-d cone in 2-d lattice N sage: K1.relative_interior() is K1 True sage: K2 = Cone([(1,0),(-1,0),(0,1),(0,-1)]); K2 2-d cone in 2-d lattice N sage: K2.relative_interior() is K2 True
- relative_interior_contains(*args)#
Check if a given point is contained in the relative interior of
self
.For a full-dimensional cone the relative interior is simply the interior, so this method will do the same check as
interior_contains()
. For a strictly lower-dimensional cone, the relative interior is the cone without its facets.INPUT:
anything. An attempt will be made to convert all arguments into a single element of the ambient space of
self
. If it fails,False
will be returned.
OUTPUT:
True
if the given point is contained in the relative interior ofself
,False
otherwise.
EXAMPLES:
sage: c = Cone([(1,0,0), (0,1,0)]) sage: c.contains((1,1,0)) True sage: c.relative_interior_contains((1,1,0)) True sage: c.interior_contains((1,1,0)) False sage: c.contains((1,0,0)) True sage: c.relative_interior_contains((1,0,0)) False sage: c.interior_contains((1,0,0)) False
- relative_orthogonal_quotient(supercone)#
The quotient of the dual spanned lattice by the dual of the supercone’s spanned lattice.
In the notation of [Ful1993], if
supercone
= \(\rho > \sigma\) =self
is a cone that contains \(\sigma\) as a face, then \(M(\rho)\) =supercone.orthogonal_sublattice()
is a saturated sublattice of \(M(\sigma)\) =self.orthogonal_sublattice()
. This method returns the quotient lattice. The lifts of the quotient generators are \(\dim(\rho)-\dim(\sigma)\) linearly independent M-lattice lattice points that, together with \(M(\rho)\), generate \(M(\sigma)\).OUTPUT:
If we call the output
Mrho
, thenMrho.cover() == self.orthogonal_sublattice()
, andMrho.relations() == supercone.orthogonal_sublattice()
.
Note
\(M(\sigma) / M(\rho)\) has no torsion since the sublattice \(M(\rho)\) is saturated.
In the codimension one case, (a lift of) the generator of \(M(\sigma) / M(\rho)\) is chosen to be positive on \(\sigma\).
EXAMPLES:
sage: # needs sage.graphs sage: rho = Cone([(1,1,1,3), (1,-1,1,3), (-1,-1,1,3), (-1,1,1,3)]) sage: rho.orthogonal_sublattice() Sublattice <M(0, 0, 3, -1)> sage: sigma = rho.facets()[1] sage: sigma.orthogonal_sublattice() Sublattice <M(0, 1, 1, 0), M(0, 0, 3, -1)> sage: sigma.is_face_of(rho) True sage: Q = sigma.relative_orthogonal_quotient(rho); Q 1-d lattice, quotient of Sublattice <M(0, 1, 1, 0), M(0, 0, 3, -1)> by Sublattice <M(0, 0, 3, -1)> sage: Q.gens() (M[0, 1, 1, 0],)
Different codimension:
sage: # needs sage.graphs sage: rho = Cone([[1,-1,1,3],[-1,-1,1,3]]) sage: sigma = rho.facets()[0] sage: sigma.orthogonal_sublattice() Sublattice <M(1, 0, 2, -1), M(0, 1, 1, 0), M(0, 0, 3, -1)> sage: rho.orthogonal_sublattice() Sublattice <M(0, 1, 1, 0), M(0, 0, 3, -1)> sage: sigma.relative_orthogonal_quotient(rho).gens() (M[-1, 0, -2, 1],)
Sign choice in the codimension one case:
sage: sigma1 = Cone([(1, 2, 3), (1, -1, 1), (-1, 1, 1), (-1, -1, 1)]) # 3d sage: sigma2 = Cone([(1, 1, -1), (1, 2, 3), (1, -1, 1), (1, -1, -1)]) # 3d sage: rho = sigma1.intersection(sigma2) sage: rho.relative_orthogonal_quotient(sigma1).gens() (M[-5, -2, 3],) sage: rho.relative_orthogonal_quotient(sigma2).gens() (M[5, 2, -3],)
- relative_quotient(subcone)#
The quotient of the spanned lattice by the lattice spanned by a subcone.
In the notation of [Ful1993], let \(N\) be the ambient lattice and \(N_\sigma\) the sublattice spanned by the given cone \(\sigma\). If \(\rho < \sigma\) is a subcone, then \(N_\rho\) =
rho.sublattice()
is a saturated sublattice of \(N_\sigma\) =self.sublattice()
. This method returns the quotient lattice. The lifts of the quotient generators are \(\dim(\sigma)-\dim(\rho)\) linearly independent primitive lattice points that, together with \(N_\rho\), generate \(N_\sigma\).OUTPUT:
Note
The quotient \(N_\sigma / N_\rho\) of spanned sublattices has no torsion since the sublattice \(N_\rho\) is saturated.
In the codimension one case, the generator of \(N_\sigma / N_\rho\) is chosen to be in the same direction as the image \(\sigma / N_\rho\)
EXAMPLES:
sage: sigma = Cone([(1,1,1,3),(1,-1,1,3),(-1,-1,1,3),(-1,1,1,3)]) sage: rho = Cone([(-1, -1, 1, 3), (-1, 1, 1, 3)]) sage: sigma.sublattice() Sublattice <N(1, 1, 1, 3), N(0, -1, 0, 0), N(-1, -1, 0, 0)> sage: rho.sublattice() Sublattice <N(-1, -1, 1, 3), N(0, 1, 0, 0)> sage: sigma.relative_quotient(rho) 1-d lattice, quotient of Sublattice <N(1, 1, 1, 3), N(0, -1, 0, 0), N(-1, -1, 0, 0)> by Sublattice <N(1, 0, -1, -3), N(0, 1, 0, 0)> sage: sigma.relative_quotient(rho).gens() (N[1, 0, 0, 0],)
More complicated example:
sage: rho = Cone([(1, 2, 3), (1, -1, 1)]) sage: sigma = Cone([(1, 2, 3), (1, -1, 1), (-1, 1, 1), (-1, -1, 1)]) sage: N_sigma = sigma.sublattice() sage: N_sigma Sublattice <N(1, 2, 3), N(1, -1, 1), N(-1, -1, -2)> sage: N_rho = rho.sublattice() sage: N_rho Sublattice <N(1, -1, 1), N(1, 2, 3)> sage: sigma.relative_quotient(rho).gens() (N[-1, -1, -2],) sage: N = rho.lattice() sage: N_sigma == N.span(N_rho.gens() + tuple(q.lift() ....: for q in sigma.relative_quotient(rho).gens())) True
Sign choice in the codimension one case:
sage: sigma1 = Cone([(1, 2, 3), (1, -1, 1), (-1, 1, 1), (-1, -1, 1)]) # 3d sage: sigma2 = Cone([(1, 1, -1), (1, 2, 3), (1, -1, 1), (1, -1, -1)]) # 3d sage: rho = sigma1.intersection(sigma2) sage: rho.sublattice() Sublattice <N(1, -1, 1), N(1, 2, 3)> sage: sigma1.relative_quotient(rho) 1-d lattice, quotient of Sublattice <N(1, 2, 3), N(1, -1, 1), N(-1, -1, -2)> by Sublattice <N(1, 2, 3), N(0, 3, 2)> sage: sigma1.relative_quotient(rho).gens() (N[-1, -1, -2],) sage: sigma2.relative_quotient(rho).gens() (N[0, 2, 1],)
- semigroup_generators()#
Return generators for the semigroup of lattice points of
self
.OUTPUT:
a
PointCollection
of lattice points generating the semigroup of lattice points contained inself
.
Note
No attempt is made to return a minimal set of generators, see
Hilbert_basis()
for that.EXAMPLES:
The following command ensures that the output ordering in the examples below is independent of TOPCOM, you don’t have to use it:
sage: PointConfiguration.set_engine('internal')
We start with a simple case of a non-smooth 2-dimensional cone:
sage: Cone([(1,0), (1,2)]).semigroup_generators() N(1, 1), N(1, 0), N(1, 2) in 2-d lattice N
A non-simplicial cone works, too:
sage: cone = Cone([(3,0,-1), (1,-1,0), (0,1,0), (0,0,1)]) sage: sorted(cone.semigroup_generators()) [N(0, 0, 1), N(0, 1, 0), N(1, -1, 0), N(1, 0, 0), N(3, 0, -1)]
GAP’s toric package thinks this is challenging:
sage: cone = Cone([[1,2,3,4], [0,1,0,7], [3,1,0,2], [0,0,1,0]]).dual() sage: len(cone.semigroup_generators()) 2806
The cone need not be strictly convex:
sage: halfplane = Cone([(1,0), (2,1), (-1,0)]) sage: sorted(halfplane.semigroup_generators()) [N(-1, 0), N(0, 1), N(1, 0)] sage: line = Cone([(1,1,1), (-1,-1,-1)]) sage: sorted(line.semigroup_generators()) [N(-1, -1, -1), N(1, 1, 1)] sage: wedge = Cone([(1,0,0), (1,2,0), (0,0,1), (0,0,-1)]) sage: sorted(wedge.semigroup_generators()) [N(0, 0, -1), N(0, 0, 1), N(1, 0, 0), N(1, 1, 0), N(1, 2, 0)]
Nor does it have to be full-dimensional (see github issue #11312):
sage: Cone([(1,1,0), (-1,1,0)]).semigroup_generators() N( 0, 1, 0), N( 1, 1, 0), N(-1, 1, 0) in 3-d lattice N
Neither full-dimensional nor simplicial:
sage: A = matrix([(1, 3, 0), (-1, 0, 1), (1, 1, -2), (15, -2, 0)]) sage: A.elementary_divisors() [1, 1, 1, 0] sage: cone3d = Cone([(3,0,-1), (1,-1,0), (0,1,0), (0,0,1)]) sage: rays = (A*vector(v) for v in cone3d.rays()) sage: gens = Cone(rays).semigroup_generators(); sorted(gens) [N(-2, -1, 0, 17), N(0, 1, -2, 0), N(1, -1, 1, 15), N(3, -4, 5, 45), N(3, 0, 1, -2)] sage: set(map(tuple,gens)) == set(tuple(A*r) for r in cone3d.semigroup_generators()) True
ALGORITHM:
If the cone is not simplicial, it is first triangulated. Each simplicial subcone has the integral points of the spaned parallelotope as generators. This is the first step of the primal Normaliz algorithm, see [Normaliz]. For each simplicial cone (of dimension \(d\)), the integral points of the open parallelotope
\[par \langle x_1, \dots, x_d \rangle = \ZZ^n \cap \left\{ q_1 x_1 + \cdots +q_d x_d :~ 0 \leq q_i < 1 \right\}\]are then computed [BK2001].
Finally, the union of the generators of all simplicial subcones is returned.
- solid_restriction()#
Return a solid representation of this cone in terms of a basis of its
sublattice()
.We define the solid restriction of a cone to be a representation of that cone in a basis of its own sublattice. Since a cone’s sublattice is just large enough to hold the cone (by definition), the resulting solid restriction
is_solid()
. For convenience, the solid restriction lives in a new lattice (of the appropriate dimension) and not actually in the sublattice object returned bysublattice()
.OUTPUT:
A solid cone in a new lattice having the same dimension as this cone’s
sublattice()
.EXAMPLES:
The nonnegative quadrant in the plane is left after we take its solid restriction in space:
sage: K = Cone([(1,0,0), (0,1,0)]) sage: K.solid_restriction().rays() N(0, 1), N(1, 0) in 2-d lattice N
The solid restriction of a single ray has the same representation regardless of the ambient space:
sage: K = Cone([(1,0)]) sage: K.solid_restriction().rays() N(1) in 1-d lattice N sage: K = Cone([(1,1,1)]) sage: K.solid_restriction().rays() N(1) in 1-d lattice N
The solid restriction of the trivial cone lives in a trivial space:
sage: K = cones.trivial(0) sage: K.solid_restriction() 0-d cone in 0-d lattice N sage: K = cones.trivial(4) sage: K.solid_restriction() 0-d cone in 0-d lattice N
The solid restriction of a solid cone is itself:
sage: K = Cone([(1,1),(1,2)]) sage: K.solid_restriction() is K True
- strict_quotient()#
Return the quotient of
self
by the linear subspace.We define the strict quotient of a cone to be the image of this cone in the quotient of the ambient space by the linear subspace of the cone, i.e. it is the “complementary part” to the linear subspace.
OUTPUT:
cone.
EXAMPLES:
sage: halfplane = Cone([(1,0), (0,1), (-1,0)]) sage: ssc = halfplane.strict_quotient() sage: ssc 1-d cone in 1-d lattice N sage: ssc.rays() N(1) in 1-d lattice N sage: line = Cone([(1,0), (-1,0)]) sage: ssc = line.strict_quotient() sage: ssc 0-d cone in 1-d lattice N sage: ssc.rays() Empty collection in 1-d lattice N
The quotient of the trivial cone is trivial:
sage: K = cones.trivial(0) sage: K.strict_quotient() 0-d cone in 0-d lattice N sage: K = Cone([(0,0,0,0)]) sage: K.strict_quotient() 0-d cone in 4-d lattice N
- sublattice(*args, **kwds)#
The sublattice spanned by the cone.
Let \(\sigma\) be the given cone and \(N=\)
self.lattice()
the ambient lattice. Then, in the notation of [Ful1993], this method returns the sublattice\[N_\sigma \stackrel{\text{def}}{=} \mathop{span}( N\cap \sigma )\]INPUT:
either nothing or something that can be turned into an element of this lattice.
OUTPUT:
if no arguments were given, a
toric sublattice
, otherwise the corresponding element of it.
Note
The sublattice spanned by the cone is the saturation of the sublattice generated by the rays of the cone.
If you only need a \(\QQ\)-basis, you may want to try the
basis()
method on the result ofrays()
.The returned lattice points are usually not rays of the cone. In fact, for a non-smooth cone the rays do not generate the sublattice \(N_\sigma\), but only a finite index sublattice.
EXAMPLES:
sage: cone = Cone([(1, 1, 1), (1, -1, 1), (-1, -1, 1), (-1, 1, 1)]) sage: cone.rays().basis() N( 1, 1, 1), N( 1, -1, 1), N(-1, -1, 1) in 3-d lattice N sage: cone.rays().basis().matrix().det() -4 sage: cone.sublattice() Sublattice <N(1, 1, 1), N(0, -1, 0), N(-1, -1, 0)> sage: matrix( cone.sublattice().gens() ).det() -1
Another example:
sage: c = Cone([(1,2,3), (4,-5,1)]) sage: c 2-d cone in 3-d lattice N sage: c.rays() N(1, 2, 3), N(4, -5, 1) in 3-d lattice N sage: c.sublattice() Sublattice <N(4, -5, 1), N(1, 2, 3)> sage: c.sublattice(5, -3, 4) N(5, -3, 4) sage: c.sublattice(1, 0, 0) Traceback (most recent call last): ... TypeError: element [1, 0, 0] is not in free module
- sublattice_complement(*args, **kwds)#
A complement of the sublattice spanned by the cone.
In other words,
sublattice()
andsublattice_complement()
together form a \(\ZZ\)-basis for the ambientlattice()
.In the notation of [Ful1993], let \(\sigma\) be the given cone and \(N=\)
self.lattice()
the ambient lattice. Then this method returns\[N(\sigma) \stackrel{\text{def}}{=} N / N_\sigma\]lifted (non-canonically) to a sublattice of \(N\).
INPUT:
either nothing or something that can be turned into an element of this lattice.
OUTPUT:
if no arguments were given, a
toric sublattice
, otherwise the corresponding element of it.
EXAMPLES:
sage: C2_Z2 = Cone([(1,0), (1,2)]) # C^2/Z_2 sage: c1, c2 = C2_Z2.facets() # needs sage.graphs sage: c2.sublattice() # needs sage.graphs Sublattice <N(1, 2)> sage: c2.sublattice_complement() # needs sage.graphs Sublattice <N(0, 1)>
A more complicated example:
sage: c = Cone([(1,2,3), (4,-5,1)]) sage: c.sublattice() Sublattice <N(4, -5, 1), N(1, 2, 3)> sage: c.sublattice_complement() Sublattice <N(2, -3, 0)> sage: m = matrix( c.sublattice().gens() + c.sublattice_complement().gens() ) sage: m [ 4 -5 1] [ 1 2 3] [ 2 -3 0] sage: m.det() -1
- sublattice_quotient(*args, **kwds)#
The quotient of the ambient lattice by the sublattice spanned by the cone.
INPUT:
either nothing or something that can be turned into an element of this lattice.
OUTPUT:
if no arguments were given, a
quotient of a toric lattice
, otherwise the corresponding element of it.
EXAMPLES:
sage: # needs sage.graphs sage: C2_Z2 = Cone([(1,0), (1,2)]) # C^2/Z_2 sage: c1, c2 = C2_Z2.facets() sage: c2.sublattice_quotient() 1-d lattice, quotient of 2-d lattice N by Sublattice <N(1, 2)> sage: N = C2_Z2.lattice() sage: n = N(1,1) sage: n_bar = c2.sublattice_quotient(n); n_bar N[1, 1] sage: n_bar.lift() N(1, 1) sage: vector(n_bar) (-1)
- class sage.geometry.cone.IntegralRayCollection(rays, lattice)#
Bases:
SageObject
,Hashable
,Iterable
Create a collection of integral rays.
Warning
No correctness check or normalization is performed on the input data. This class is designed for internal operations and you probably should not use it directly.
This is a base class for
convex rational polyhedral cones
andfans
.Ray collections are immutable, but they cache most of the returned values.
INPUT:
rays
– list of immutable vectors inlattice
;lattice
–ToricLattice
, \(\ZZ^n\), or any other object that behaves like these. IfNone
, it will be determined asparent()
of the first ray. Of course, this cannot be done if there are no rays, so in this case you must give an appropriatelattice
directly. Note thatNone
is not the default value - you always must give this argument explicitly, even if it isNone
.
OUTPUT:
collection of given integral rays.
- ambient_dim()#
Return the dimension of the ambient lattice of
self
.An alias is
ambient_dim()
.OUTPUT:
integer.
EXAMPLES:
sage: c = Cone([(1,0)]) sage: c.lattice_dim() 2 sage: c.dim() 1
- ambient_vector_space(base_field=None)#
Return the ambient vector space.
It is the ambient lattice (
lattice()
) tensored with a field.INPUT:
base_field
– (default: the rationals) a field.
EXAMPLES:
sage: c = Cone([(1,0)]) sage: c.ambient_vector_space() Vector space of dimension 2 over Rational Field sage: c.ambient_vector_space(AA) # needs sage.rings.number_field Vector space of dimension 2 over Algebraic Real Field
- cartesian_product(other, lattice=None)#
Return the Cartesian product of
self
withother
.INPUT:
other
– anIntegralRayCollection
;lattice
– (optional) the ambient lattice for the result. By default, the direct sum of the ambient lattices ofself
andother
is constructed.
OUTPUT:
By the Cartesian product of ray collections \((r_0, \dots, r_{n-1})\) and \((s_0, \dots, s_{m-1})\) we understand the ray collection of the form \(((r_0, 0), \dots, (r_{n-1}, 0), (0, s_0), \dots, (0, s_{m-1}))\), which is suitable for Cartesian products of cones and fans. The ray order is guaranteed to be as described.
EXAMPLES:
sage: c = Cone([(1,)]) sage: c.cartesian_product(c) # indirect doctest 2-d cone in 2-d lattice N+N sage: _.rays() N+N(1, 0), N+N(0, 1) in 2-d lattice N+N
- codim()#
Return the codimension of
self
.The codimension of a collection of rays (of a cone/fan) is the difference between the dimension of the ambient space and the dimension of the subspace spanned by those rays (of the cone/fan).
OUTPUT:
A nonnegative integer representing the codimension of
self
.See also
EXAMPLES:
The codimension of the nonnegative orthant is zero, since the span of its generators equals the entire ambient space:
sage: K = cones.nonnegative_orthant(3) sage: K.codim() 0
However, if we remove a ray so that the entire cone is contained within the \(x\)-\(y\) plane, then the resulting cone will have codimension one, because the \(z\)-axis is perpendicular to every element of the cone:
sage: K = Cone([(1,0,0), (0,1,0)]) sage: K.codim() 1
If our cone is all of \(\mathbb{R}^{2}\), then its codimension is zero:
sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) sage: K.is_full_space() True sage: K.codim() 0
And if the cone is trivial in any space, then its codimension is equal to the dimension of the ambient space:
sage: K = cones.trivial(0) sage: K.lattice_dim() 0 sage: K.codim() 0 sage: K = cones.trivial(1) sage: K.lattice_dim() 1 sage: K.codim() 1 sage: K = cones.trivial(2) sage: K.lattice_dim() 2 sage: K.codim() 2
- codimension()#
Return the codimension of
self
.The codimension of a collection of rays (of a cone/fan) is the difference between the dimension of the ambient space and the dimension of the subspace spanned by those rays (of the cone/fan).
OUTPUT:
A nonnegative integer representing the codimension of
self
.See also
EXAMPLES:
The codimension of the nonnegative orthant is zero, since the span of its generators equals the entire ambient space:
sage: K = cones.nonnegative_orthant(3) sage: K.codim() 0
However, if we remove a ray so that the entire cone is contained within the \(x\)-\(y\) plane, then the resulting cone will have codimension one, because the \(z\)-axis is perpendicular to every element of the cone:
sage: K = Cone([(1,0,0), (0,1,0)]) sage: K.codim() 1
If our cone is all of \(\mathbb{R}^{2}\), then its codimension is zero:
sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) sage: K.is_full_space() True sage: K.codim() 0
And if the cone is trivial in any space, then its codimension is equal to the dimension of the ambient space:
sage: K = cones.trivial(0) sage: K.lattice_dim() 0 sage: K.codim() 0 sage: K = cones.trivial(1) sage: K.lattice_dim() 1 sage: K.codim() 1 sage: K = cones.trivial(2) sage: K.lattice_dim() 2 sage: K.codim() 2
- dim()#
Return the dimension of the subspace spanned by rays of
self
.OUTPUT:
integer.
EXAMPLES:
sage: c = Cone([(1,0)]) sage: c.lattice_dim() 2 sage: c.dim() 1
- dual_lattice()#
Return the dual of the ambient lattice of
self
.OUTPUT:
lattice. If possible (that is, if
lattice()
has adual()
method), the dual lattice is returned. Otherwise, \(\ZZ^n\) is returned, where \(n\) is the dimension oflattice()
.
EXAMPLES:
sage: c = Cone([(1,0)]) sage: c.dual_lattice() 2-d lattice M sage: Cone([], ZZ^3).dual_lattice() Ambient free module of rank 3 over the principal ideal domain Integer Ring
- lattice()#
Return the ambient lattice of
self
.OUTPUT:
lattice.
EXAMPLES:
sage: c = Cone([(1,0)]) sage: c.lattice() 2-d lattice N sage: Cone([], ZZ^3).lattice() Ambient free module of rank 3 over the principal ideal domain Integer Ring
- lattice_dim()#
Return the dimension of the ambient lattice of
self
.An alias is
ambient_dim()
.OUTPUT:
integer.
EXAMPLES:
sage: c = Cone([(1,0)]) sage: c.lattice_dim() 2 sage: c.dim() 1
- nrays()#
Return the number of rays of
self
.OUTPUT:
integer.
EXAMPLES:
sage: c = Cone([(1,0), (0,1)]) sage: c.nrays() 2
- plot(**options)#
Plot
self
.INPUT:
any options for toric plots (see
toric_plotter.options
), none are mandatory.
OUTPUT:
a plot.
EXAMPLES:
sage: quadrant = Cone([(1,0), (0,1)]) sage: quadrant.plot() # needs sage.plot Graphics object consisting of 9 graphics primitives
- ray(n)#
Return the
n
-th ray ofself
.INPUT:
n
– integer, an index of a ray ofself
. Enumeration of rays starts with zero.
OUTPUT:
ray, an element of the lattice of
self
.
EXAMPLES:
sage: c = Cone([(1,0), (0,1)]) sage: c.ray(0) N(1, 0)
- rays(*args)#
Return (some of the) rays of
self
.INPUT:
ray_list
– a list of integers, the indices of the requested rays. If not specified, all rays ofself
will be returned.
OUTPUT:
a
PointCollection
of primitive integral ray generators.
EXAMPLES:
sage: c = Cone([(1,0), (0,1), (-1, 0)]) sage: c.rays() N( 0, 1), N( 1, 0), N(-1, 0) in 2-d lattice N sage: c.rays([0, 2]) N( 0, 1), N(-1, 0) in 2-d lattice N
You can also give ray indices directly, without packing them into a list:
sage: c.rays(0, 2) N( 0, 1), N(-1, 0) in 2-d lattice N
- span(base_ring=None)#
Return the span of
self
.INPUT:
base_ring
– (default: from lattice) the base ring to usefor the generated module.
OUTPUT:
A module spanned by the generators of
self
.EXAMPLES:
The span of a single ray is a one-dimensional sublattice:
sage: K1 = Cone([(1,)]) sage: K1.span() Sublattice <N(1)> sage: K2 = Cone([(1,0)]) sage: K2.span() Sublattice <N(1, 0)>
The span of the nonnegative orthant is the entire ambient lattice:
sage: K = cones.nonnegative_orthant(3) sage: K.span() == K.lattice() True
By specifying a
base_ring
, we can obtain a vector space:sage: K = Cone([(1,0,0),(0,1,0),(0,0,1)]) sage: K.span(base_ring=QQ) Vector space of degree 3 and dimension 3 over Rational Field Basis matrix: [1 0 0] [0 1 0] [0 0 1]
- sage.geometry.cone.classify_cone_2d(ray0, ray1, check=True)#
Return \((d,k)\) classifying the lattice cone spanned by the two rays.
INPUT:
ray0
,ray1
– two primitive integer vectors. The generators of the two rays generating the two-dimensional cone.check
– boolean (default:True
). Whether to check the input rays for consistency.
OUTPUT:
A pair \((d,k)\) of integers classifying the cone up to \(GL(2, \ZZ)\) equivalence. See Proposition 10.1.1 of [CLS2011] for the definition. We return the unique \((d,k)\) with minimal \(k\), see Proposition 10.1.3 of [CLS2011].
EXAMPLES:
sage: ray0 = vector([1,0]) sage: ray1 = vector([2,3]) sage: from sage.geometry.cone import classify_cone_2d sage: classify_cone_2d(ray0, ray1) (3, 2) sage: ray0 = vector([2,4,5]) sage: ray1 = vector([5,19,11]) sage: classify_cone_2d(ray0, ray1) (3, 1) sage: m = matrix(ZZ, [(19, -14, -115), (-2, 5, 25), (43, -42, -298)]) sage: m.det() # check that it is in GL(3,ZZ) -1 sage: classify_cone_2d(m*ray0, m*ray1) (3, 1)
- sage.geometry.cone.integral_length(v)#
Compute the integral length of a given rational vector.
INPUT:
v
– any object which can be converted to a list of rationals
OUTPUT:
Rational number \(r`\) such that
v = r * u
, whereu
is the primitive integral vector in the direction ofv
.EXAMPLES:
sage: from sage.geometry.cone import integral_length sage: integral_length([1, 2, 4]) 1 sage: integral_length([2, 2, 4]) 2 sage: integral_length([2/3, 2, 4]) 2/3
- sage.geometry.cone.is_Cone(x)#
Check if
x
is a cone.INPUT:
x
– anything.
OUTPUT:
True
ifx
is a cone andFalse
otherwise.
EXAMPLES:
sage: from sage.geometry.cone import is_Cone sage: is_Cone(1) False sage: quadrant = Cone([(1,0), (0,1)]) sage: quadrant 2-d cone in 2-d lattice N sage: is_Cone(quadrant) True
- sage.geometry.cone.normalize_rays(rays, lattice)#
Normalize a list of rational rays: make them primitive and immutable.
INPUT:
rays
– list of rays which can be converted to the rational extension oflattice
;lattice
–ToricLattice
, \(\ZZ^n\), or any other object that behaves like these. IfNone
, an attempt will be made to determine an appropriate toric lattice automatically.
OUTPUT:
list of immutable primitive vectors of the
lattice
in the same directions as originalrays
.
EXAMPLES:
sage: from sage.geometry.cone import normalize_rays sage: normalize_rays([(0, 1), (0, 2), (3, 2), (5/7, 10/3)], None) [N(0, 1), N(0, 1), N(3, 2), N(3, 14)] sage: L = ToricLattice(2, "L") sage: normalize_rays([(0, 1), (0, 2), (3, 2), (5/7, 10/3)], L.dual()) [L*(0, 1), L*(0, 1), L*(3, 2), L*(3, 14)] sage: ray_in_L = L(0,1) sage: normalize_rays([ray_in_L, (0, 2), (3, 2), (5/7, 10/3)], None) [L(0, 1), L(0, 1), L(3, 2), L(3, 14)] sage: normalize_rays([(0, 1), (0, 2), (3, 2), (5/7, 10/3)], ZZ^2) [(0, 1), (0, 1), (3, 2), (3, 14)] sage: normalize_rays([(0, 1), (0, 2), (3, 2), (5/7, 10/3)], ZZ^3) Traceback (most recent call last): ... TypeError: cannot convert (0, 1) to Vector space of dimension 3 over Rational Field! sage: normalize_rays([], ZZ^3) []
- sage.geometry.cone.random_cone(lattice=None, min_ambient_dim=0, max_ambient_dim=None, min_rays=0, max_rays=None, strictly_convex=None, solid=None)#
Generate a random convex rational polyhedral cone.
Lower and upper bounds may be provided for both the dimension of the ambient space and the number of generating rays of the cone. If a lower bound is left unspecified, it defaults to zero. Unspecified upper bounds will be chosen randomly, unless you set
solid
, in which case they are chosen a little more wisely.You may specify the ambient
lattice
for the returned cone. In that case, themin_ambient_dim
andmax_ambient_dim
parameters are ignored.You may also request that the returned cone be strictly convex (or not). Likewise you may request that it be (non-)solid.
Warning
If you request a large number of rays in a low-dimensional space, you might be waiting for a while. For example, in three dimensions, it is possible to obtain an octagon raised up to height one (all z-coordinates equal to one). But in practice, we usually generate the entire three-dimensional space with six rays before we get to the eight rays needed for an octagon. We therefore have to throw the cone out and start over from scratch. This process repeats until we get lucky.
We also refrain from “adjusting” the min/max parameters given to us when a (non-)strictly convex or (non-)solid cone is requested. This means that it may take a long time to generate such a cone if the parameters are chosen unwisely.
For example, you may want to set
min_rays
close tomin_ambient_dim
if you desire a solid cone. Or, if you desire a non-strictly-convex cone, then they all contain at least two generating rays. So that might be a good candidate formin_rays
.INPUT:
lattice
(default: random) – AToricLattice
object in which the returned cone will live. By default a new lattice will be constructed with a randomly-chosen rank (subject tomin_ambient_dim
andmax_ambient_dim
).min_ambient_dim
(default: zero) – A nonnegative integer representing the minimum dimension of the ambient lattice.max_ambient_dim
(default: random) – A nonnegative integer representing the maximum dimension of the ambient lattice.min_rays
(default: zero) – A nonnegative integer representing the minimum number of generating rays of the cone.max_rays
(default: random) – A nonnegative integer representing the maximum number of generating rays of the cone.strictly_convex
(default: random) – Whether or not to make the returned cone strictly convex. SpecifyTrue
for a strictly convex cone,False
for a non-strictly-convex cone, orNone
if you don’t care.solid
(default: random) – Whether or not to make the returned cone solid. SpecifyTrue
for a solid cone,False
for a non-solid cone, orNone
if you don’t care.
OUTPUT:
A new, randomly generated cone.
A
ValueError
will be thrown under the following conditions:Any of
min_ambient_dim
,max_ambient_dim
,min_rays
, ormax_rays
are negative.max_ambient_dim
is less thanmin_ambient_dim
.max_rays
is less thanmin_rays
.Both
max_ambient_dim
andlattice
are specified.min_rays
is greater than four butmax_ambient_dim
is less than three.min_rays
is greater than four butlattice
has dimension less than three.min_rays
is greater than two butmax_ambient_dim
is less than two.min_rays
is greater than two butlattice
has dimension less than two.min_rays
is positive butmax_ambient_dim
is zero.min_rays
is positive butlattice
has dimension zero.A trivial lattice is supplied and a non-strictly-convex cone is requested.
A non-strictly-convex cone is requested but
max_rays
is less than two.A solid cone is requested but
max_rays
is less thanmin_ambient_dim
.A solid cone is requested but
max_rays
is less than the dimension oflattice
.A non-solid cone is requested but
max_ambient_dim
is zero.A non-solid cone is requested but
lattice
has dimension zero.A non-solid cone is requested but
min_rays
is so large that it guarantees a solid cone.
ALGORITHM:
First, a lattice is determined from
min_ambient_dim
andmax_ambient_dim
(or from the suppliedlattice
).Then, lattice elements are generated one at a time and added to a cone. This continues until either the cone meets the user’s requirements, or the cone is equal to the entire space (at which point it is futile to generate more).
We check whether or not the resulting cone meets the user’s requirements; if it does, it is returned. If not, we throw it away and start over. This process repeats indefinitely until an appropriate cone is generated.
EXAMPLES:
Generate a trivial cone in a trivial space:
sage: random_cone(max_ambient_dim=0, max_rays=0) 0-d cone in 0-d lattice N
We can predict the ambient dimension when
min_ambient_dim == max_ambient_dim
:sage: K = random_cone(min_ambient_dim=4, max_ambient_dim=4) sage: K.lattice_dim() 4
Likewise for the number of rays when
min_rays == max_rays
:sage: K = random_cone(min_rays=3, max_rays=3) sage: K.nrays() 3
If we specify a lattice, then the returned cone will live in it:
sage: L = ToricLattice(5, "L") sage: K = random_cone(lattice=L) sage: K.lattice() is L True
We can also request a strictly convex cone:
sage: K = random_cone(max_ambient_dim=8, max_rays=10, ....: strictly_convex=True) sage: K.is_strictly_convex() True
Or one that isn’t strictly convex:
sage: K = random_cone(min_ambient_dim=5, min_rays=2, ....: strictly_convex=False) sage: K.is_strictly_convex() False
An example with all parameters set:
sage: K = random_cone(min_ambient_dim=4, max_ambient_dim=7, ....: min_rays=2, max_rays=10, ....: strictly_convex=False, solid=True) sage: 4 <= K.lattice_dim() and K.lattice_dim() <= 7 True sage: 2 <= K.nrays() and K.nrays() <= 10 True sage: K.is_strictly_convex() False sage: K.is_solid() True