The identity is always Lyapunov-like in a nontrivial space::
sage: set_random_seed()
- sage: K = random_cone(min_ambient_dim = 1, max_ambient_dim = 8)
+ sage: K = random_cone(min_ambient_dim=1, max_ambient_dim=8)
sage: L = identity_matrix(K.lattice_dim())
sage: is_lyapunov_like(L,K)
True
As is the "zero" transformation::
- sage: K = random_cone(min_ambient_dim = 1, max_ambient_dim = 8)
+ sage: K = random_cone(min_ambient_dim=1, max_ambient_dim=8)
sage: R = K.lattice().vector_space().base_ring()
sage: L = zero_matrix(R, K.lattice_dim())
sage: is_lyapunov_like(L,K)
Everything in ``K.lyapunov_like_basis()`` should be Lyapunov-like
on ``K``::
- sage: K = random_cone(min_ambient_dim = 1, max_ambient_dim = 6)
+ sage: K = random_cone(min_ambient_dim=1, max_ambient_dim=6)
sage: all([ is_lyapunov_like(L,K) for L in K.lyapunov_like_basis() ])
True
sage: random_element(K)
(0, 0, 0)
+ A random element of the nonnegative orthant should have all
+ components nonnegative::
+
+ sage: set_random_seed()
+ sage: K = Cone([(1,0,0),(0,1,0),(0,0,1)])
+ sage: all([ x >= 0 for x in random_element(K) ])
+ True
+
TESTS:
- Any cone should contain an element of itself::
+ Any cone should contain a random element of itself::
sage: set_random_seed()
- sage: K = random_cone(max_rays = 8)
+ sage: K = random_cone(max_ambient_dim=8)
sage: K.contains(random_element(K))
True
+ A strictly convex cone contains no lines, and thus no negative
+ multiples of any of its elements besides zero::
+
+ sage: set_random_seed()
+ sage: K = random_cone(max_ambient_dim=8, strictly_convex=True)
+ sage: x = random_element(K)
+ sage: x.is_zero() or not K.contains(-x)
+ True
+
+ The sum of random elements of a cone lies in the cone::
+
+ sage: set_random_seed()
+ sage: K = random_cone(max_ambient_dim=8)
+ sage: K.contains(sum([random_element(K) for i in range(10)]))
+ True
+
"""
V = K.lattice().vector_space()
- F = V.base_ring()
- coefficients = [ F.random_element().abs() for i in range(K.nrays()) ]
- vector_gens = map(V, K.rays())
- scaled_gens = [ coefficients[i]*vector_gens[i]
- for i in range(len(vector_gens)) ]
+ scaled_gens = [ V.base_field().random_element().abs()*V(r) for r in K ]
# Make sure we return a vector. Without the coercion, we might
# return ``0`` when ``K`` has no rays.
- v = V(sum(scaled_gens))
- return v
+ return V(sum(scaled_gens))
+
+
+def pointed_decomposition(K):
+ """
+ Every convex cone is the direct sum of a pointed cone and a linear
+ subspace. Return a pair ``(P,S)`` of cones such that ``P`` is
+ pointed, ``S`` is a subspace, and ``K`` is the direct sum of ``P``
+ and ``S``.
+
+ OUTPUT:
+
+ An ordered pair ``(P,S)`` of closed convex polyhedral cones where
+ ``P`` is pointed, ``S`` is a subspace, and ``K`` is the direct sum
+ of ``P`` and ``S``.
+
+ TESTS:
+
+ A random point in the cone should belong to either the pointed
+ subcone ``P`` or the subspace ``S``. If the point is nonzero, it
+ should lie in one but not both of them::
+
+ sage: set_random_seed()
+ sage: K = random_cone(max_ambient_dim=8)
+ sage: (P,S) = pointed_decomposition(K)
+ sage: x = random_element(K)
+ sage: P.contains(x) or S.contains(x)
+ True
+ sage: x.is_zero() or (P.contains(x) != S.contains(x))
+ True
+ """
+ linspace_gens = [ copy(b) for b in K.linear_subspace().basis() ]
+ linspace_gens += [ -b for b in linspace_gens ]
+
+ S = Cone(linspace_gens, K.lattice())
+
+ # Since ``S`` is a subspace, its dual is its orthogonal complement
+ # (albeit in the wrong lattice).
+ S_perp = Cone(S.dual(), K.lattice())
+ P = K.intersection(S_perp)
+ return (P,S)
def positive_operator_gens(K):
r"""
A positive operator on a cone should send its generators into the cone::
sage: set_random_seed()
- sage: K = random_cone(max_ambient_dim = 5)
+ sage: K = random_cone(max_ambient_dim=5)
sage: pi_of_K = positive_operator_gens(K)
sage: all([K.contains(p*x) for p in pi_of_K for x in K.rays()])
True
corollary in my paper::
sage: set_random_seed()
- sage: K = random_cone(max_ambient_dim = 5)
+ sage: K = random_cone(max_ambient_dim=5)
sage: n = K.lattice_dim()
sage: m = K.dim()
sage: l = K.lineality()
corollary in my paper::
sage: set_random_seed()
- sage: K = random_cone(max_ambient_dim = 5)
+ sage: K = random_cone(max_ambient_dim=5)
sage: n = K.lattice_dim()
sage: pi_of_K = positive_operator_gens(K)
sage: L = ToricLattice(n**2)
sage: expected = n**2 - K.dim()*K.dual().dim()
sage: actual == expected
True
+
+ The cone ``K`` is proper if and only if the cone of positive
+ operators on ``K`` is proper::
+
+ sage: set_random_seed()
+ sage: K = random_cone(max_ambient_dim=5)
+ sage: pi_of_K = positive_operator_gens(K)
+ sage: L = ToricLattice(K.lattice_dim()**2)
+ sage: pi_cone = Cone([p.list() for p in pi_of_K], lattice=L)
+ sage: K.is_proper() == pi_cone.is_proper()
+ True
"""
# Matrices are not vectors in Sage, so we have to convert them
# to vectors explicitly before we can find a basis. We need these
The Z-property is possessed by every Z-transformation::
sage: set_random_seed()
- sage: K = random_cone(max_ambient_dim = 6)
+ sage: K = random_cone(max_ambient_dim=6)
sage: Z_of_K = Z_transformation_gens(K)
sage: dcs = K.discrete_complementarity_set()
sage: all([(z*x).inner_product(s) <= 0 for z in Z_of_K
The lineality space of Z is LL::
sage: set_random_seed()
- sage: K = random_cone(min_ambient_dim = 1, max_ambient_dim = 6)
+ sage: K = random_cone(min_ambient_dim=1, max_ambient_dim=6)
sage: lls = span([ vector(l.list()) for l in K.lyapunov_like_basis() ])
sage: z_cone = Cone([ z.list() for z in Z_transformation_gens(K) ])
sage: z_cone.linear_subspace() == lls
And thus, the lineality of Z is the Lyapunov rank::
sage: set_random_seed()
- sage: K = random_cone(min_ambient_dim = 1, max_ambient_dim = 6)
- sage: z_cone = Cone([ z.list() for z in Z_transformation_gens(K) ])
+ sage: K = random_cone(max_ambient_dim=6)
+ sage: Z_of_K = Z_transformation_gens(K)
+ sage: L = ToricLattice(K.lattice_dim()**2)
+ sage: z_cone = Cone([ z.list() for z in Z_of_K ], lattice=L)
sage: z_cone.lineality() == K.lyapunov_rank()
True
+
+ The lineality spaces of pi-star and Z-star are equal:
+
+ sage: set_random_seed()
+ sage: K = random_cone(max_ambient_dim=5)
+ sage: pi_of_K = positive_operator_gens(K)
+ sage: Z_of_K = Z_transformation_gens(K)
+ sage: L = ToricLattice(K.lattice_dim()**2)
+ sage: pi_star = Cone([p.list() for p in pi_of_K], lattice=L).dual()
+ sage: z_star = Cone([ z.list() for z in Z_of_K], lattice=L).dual()
+ sage: pi_star.linear_subspace() == z_star.linear_subspace()
+ True
"""
# Matrices are not vectors in Sage, so we have to convert them
# to vectors explicitly before we can find a basis. We need these
# not cross-positive ones.
M = MatrixSpace(F, n)
return [ -M(v.list()) for v in Sigma_cone.rays() ]
+
+
+def Z_cone(K):
+ gens = Z_transformation_gens(K)
+ L = None
+ if len(gens) == 0:
+ L = ToricLattice(0)
+ return Cone([ g.list() for g in gens ], lattice=L)
+
+def pi_cone(K):
+ gens = positive_operator_gens(K)
+ L = None
+ if len(gens) == 0:
+ L = ToricLattice(0)
+ return Cone([ g.list() for g in gens ], lattice=L)
projects / sage.d.git / blobdiff