X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Fcone%2Fcone.py;h=b7456e21abc170d6479f9010a2b7ccdd5ab9438d;hb=1bbade9f41ffbfe366b15d0db657f666bc1f025d;hp=87cdf704580e68b55fa72cd93b8dfa6c1d08a484;hpb=0bdf2bb8ca97eeb065e7dd3c36bdac6879a52116;p=sage.d.git diff --git a/mjo/cone/cone.py b/mjo/cone/cone.py index 87cdf70..b7456e2 100644 --- a/mjo/cone/cone.py +++ b/mjo/cone/cone.py @@ -1,967 +1,23 @@ -# Sage doesn't load ~/.sage/init.sage during testing (sage -t), so we -# have to explicitly mangle our sitedir here so that "mjo.cone" -# resolves. -from os.path import abspath -from site import addsitedir -addsitedir(abspath('../../')) - from sage.all import * +def LL_cone(K): + gens = K.lyapunov_like_basis() + L = ToricLattice(K.lattice_dim()**2) + return Cone(( g.list() for g in gens ), lattice=L, check=False) -def basically_the_same(K1,K2): - r""" - ``True`` if ``K1`` and ``K2`` are basically the same, and ``False`` - otherwise. - """ - if K1.lattice_dim() != K2.lattice_dim(): - return False - - if K1.nrays() != K2.nrays(): - return False - - if K1.dim() != K2.dim(): - return False - - if lineality(K1) != lineality(K2): - return False - - if K1.is_solid() != K2.is_solid(): - return False - - if K1.is_strictly_convex() != K2.is_strictly_convex(): - return False - - if len(LL(K1)) != len(LL(K2)): - return False - - C_of_K1 = discrete_complementarity_set(K1) - C_of_K2 = discrete_complementarity_set(K2) - if len(C_of_K1) != len(C_of_K2): - return False - - if len(K1.facets()) != len(K2.facets()): - return False - - return True - - - -def iso_space(K): - r""" - Construct the space `W \times W^{\perp}` isomorphic to the ambient space - of ``K`` where `W` is equal to the span of ``K``. - """ - V = K.lattice().vector_space() - - # Create the space W \times W^{\perp} isomorphic to V. - # First we get an orthogonal (but not normal) basis... - M = matrix(V.base_field(), K.rays()) - W_basis,_ = M.gram_schmidt() - - W = V.subspace_with_basis(W_basis) - W_perp = W.complement() - - return W.cartesian_product(W_perp) - - -def ips_iso(K): - r""" - Construct the IPS isomorphism and its inverse from our paper. - - Given a cone ``K``, the returned isomorphism will split its ambient - vector space `V` into a cartesian product `W \times W^{\perp}` where - `W` equals the span of ``K``. - """ - V = K.lattice().vector_space() - V_iso = iso_space(K) - (W, W_perp) = V_iso.cartesian_factors() - - # A space equivalent to V, but using our basis. - V_user = V.subspace_with_basis( W.basis() + W_perp.basis() ) - - def phi(v): - # Write v in terms of our custom basis, where the first dim(W) - # coordinates are for the W-part of the basis. - cs = V_user.coordinates(v) - - w1 = sum([ V_user.basis()[idx]*cs[idx] - for idx in range(0, W.dimension()) ]) - w2 = sum([ V_user.basis()[idx]*cs[idx] - for idx in range(W.dimension(), V.dimension()) ]) - - return V_iso( (w1, w2) ) - - - def phi_inv( pair ): - # Crash if the arguments are in the wrong spaces. - V_iso(pair) - - #w = sum([ sub_w[idx]*W.basis()[idx] for idx in range(0,m) ]) - #w_prime = sum([ sub_w_prime[idx]*W_perp.basis()[idx] - # for idx in range(0,n-m) ]) - - return sum( pair.cartesian_factors() ) - - - return (phi,phi_inv) - - - -def unrestrict_span(K, K2=None): - if K2 is None: - K2 = K - - _,phi_inv = ips_iso(K2) - V_iso = iso_space(K2) - (W, W_perp) = V_iso.cartesian_factors() - - rays = [] - for r in K.rays(): - w = sum([ r[idx]*W.basis()[idx] for idx in range(0,len(r)) ]) - pair = V_iso( (w, W_perp.zero()) ) - rays.append( phi_inv(pair) ) - - L = ToricLattice(W.dimension() + W_perp.dimension()) - - return Cone(rays, lattice=L) - - - -def restrict_span(K, K2=None): - r""" - Restrict ``K`` into its own span, or the span of another cone. - - INPUT: - - - ``K2`` -- another cone whose lattice has the same rank as this cone. - - OUTPUT: - - A new cone in a sublattice. - - EXAMPLES:: - - sage: K = Cone([(1,)]) - sage: restrict_span(K) == K - True - - sage: K2 = Cone([(1,0)]) - sage: restrict_span(K2).rays() - N(1) - in 1-d lattice N - sage: K3 = Cone([(1,0,0)]) - sage: restrict_span(K3).rays() - N(1) - in 1-d lattice N - sage: restrict_span(K2) == restrict_span(K3) - True - - TESTS: - - The projected cone should always be solid:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8) - sage: K_S = restrict_span(K) - sage: K_S.is_solid() - True - - And the resulting cone should live in a space having the same - dimension as the space we restricted it to:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8) - sage: K_S = restrict_span(K, K.dual() ) - sage: K_S.lattice_dim() == K.dual().dim() - True - - This function has ``unrestrict_span()`` as its inverse:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, solid=True) - sage: J = restrict_span(K) - sage: K == unrestrict_span(J,K) - True - - This function should not affect the dimension of a cone:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8) - sage: K.dim() == restrict_span(K).dim() - True - - Nor should it affect the lineality of a cone:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8) - sage: lineality(K) == lineality(restrict_span(K)) - True - - No matter which space we restrict to, the lineality should not - increase:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8) - sage: lineality(K) >= lineality(restrict_span(K)) - True - sage: lineality(K) >= lineality(restrict_span(K, K.dual())) - True - - If we do this according to our paper, then the result is proper:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, strictly_convex=False, solid=False) - sage: K_S = restrict_span(K) - sage: P = restrict_span(K_S.dual()).dual() - sage: P.is_proper() - True - sage: P = restrict_span(K_S, K_S.dual()) - sage: P.is_proper() - True - - :: +def Sigma_cone(K): + gens = K.cross_positive_operators_gens() + L = ToricLattice(K.lattice_dim()**2) + return Cone(( g.list() for g in gens ), lattice=L, check=False) - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, strictly_convex=True, solid=False) - sage: K_S = restrict_span(K) - sage: P = restrict_span(K_S.dual()).dual() - sage: P.is_proper() - True - sage: P = restrict_span(K_S, K_S.dual()) - sage: P.is_proper() - True +def Z_cone(K): + gens = K.Z_operators_gens() + L = ToricLattice(K.lattice_dim()**2) + return Cone(( g.list() for g in gens ), lattice=L, check=False) - :: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, strictly_convex=False, solid=True) - sage: K_S = restrict_span(K) - sage: P = restrict_span(K_S.dual()).dual() - sage: P.is_proper() - True - sage: P = restrict_span(K_S, K_S.dual()) - sage: P.is_proper() - True - - :: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, strictly_convex=True, solid=True) - sage: K_S = restrict_span(K) - sage: P = restrict_span(K_S.dual()).dual() - sage: P.is_proper() - True - sage: P = restrict_span(K_S, K_S.dual()) - sage: P.is_proper() - True - - Test the proposition in our paper concerning the duals, where the - subspace `W` is the span of `K^{*}`:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, solid=False, strictly_convex=False) - sage: K_W = restrict_span(K, K.dual()) - sage: K_star_W_star = restrict_span(K.dual()).dual() - sage: basically_the_same(K_W, K_star_W_star) - True - - :: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, solid=True, strictly_convex=False) - sage: K_W = restrict_span(K, K.dual()) - sage: K_star_W_star = restrict_span(K.dual()).dual() - sage: basically_the_same(K_W, K_star_W_star) - True - - :: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, solid=False, strictly_convex=True) - sage: K_W = restrict_span(K, K.dual()) - sage: K_star_W_star = restrict_span(K.dual()).dual() - sage: basically_the_same(K_W, K_star_W_star) - True - - :: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, solid=True, strictly_convex=True) - sage: K_W = restrict_span(K, K.dual()) - sage: K_star_W_star = restrict_span(K.dual()).dual() - sage: basically_the_same(K_W, K_star_W_star) - True - - """ +def pi_cone(K1, K2=None): if K2 is None: - K2 = K - - phi,_ = ips_iso(K2) - (W, W_perp) = iso_space(K2).cartesian_factors() - - ray_pairs = [ phi(r) for r in K.rays() ] - - # Shouldn't matter? - # - #if any([ w2 != W_perp.zero() for (_, w2) in ray_pairs ]): - # msg = 'Cone has nonzero components in W-perp!' - # raise ValueError(msg) - - # Represent the cone in terms of a basis for W, i.e. with smaller - # vectors. - ws = [ W.coordinate_vector(w1) for (w1, _) in ray_pairs ] - - L = ToricLattice(W.dimension()) - - return Cone(ws, lattice=L) - - - -def lineality(K): - r""" - Compute 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: - - .. [Rockafellar] R.T. Rockafellar. Convex Analysis. Princeton - University Press, Princeton, 1970. - - EXAMPLES: - - The lineality of the nonnegative orthant is zero, since it clearly - contains no lines:: - - sage: K = Cone([(1,0,0), (0,1,0), (0,0,1)]) - sage: lineality(K) - 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: lineality(K) - 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: lineality(K) - 2 - - Per the definition, the lineality of the trivial cone in a trivial - space is zero:: - - sage: K = Cone([], lattice=ToricLattice(0)) - sage: lineality(K) - 0 - - TESTS: - - The lineality of a cone should be an integer between zero and the - dimension of the ambient space, inclusive:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8) - sage: l = lineality(K) - sage: l in ZZ - True - sage: (0 <= l) and (l <= K.lattice_dim()) - True - - A strictly convex cone should have lineality zero:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, strictly_convex = True) - sage: lineality(K) - 0 - - """ - return K.linear_subspace().dimension() - - -def codim(K): - r""" - Compute the codimension of this cone. - - The codimension of a cone is the dimension of the space of all - elements perpendicular to every element of the cone. In other words, - the codimension is the difference between the dimension of the - ambient space and the dimension of the cone itself. - - OUTPUT: - - A nonnegative integer representing the dimension of the space of all - elements perpendicular to this cone. - - .. seealso:: - - :meth:`dim`, :meth:`lattice_dim` - - EXAMPLES: - - The codimension of the nonnegative orthant is zero, since the span of - its generators equals the entire ambient space:: - - sage: K = Cone([(1,0,0), (0,1,0), (0,0,1)]) - sage: codim(K) - 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: codim(K) - 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: codim(K) - 0 - - And if the cone is trivial in any space, then its codimension is - equal to the dimension of the ambient space:: - - sage: K = Cone([], lattice=ToricLattice(0)) - sage: K.lattice_dim() - 0 - sage: codim(K) - 0 - - sage: K = Cone([(0,)]) - sage: K.lattice_dim() - 1 - sage: codim(K) - 1 - - sage: K = Cone([(0,0)]) - sage: K.lattice_dim() - 2 - sage: codim(K) - 2 - - TESTS: - - The codimension of a cone should be an integer between zero and - the dimension of the ambient space, inclusive:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8) - sage: c = codim(K) - sage: c in ZZ - True - sage: (0 <= c) and (c <= K.lattice_dim()) - True - - A solid cone should have codimension zero:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, solid = True) - sage: codim(K) - 0 - - The codimension of a cone is equal to the lineality of its dual:: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, solid = True) - sage: codim(K) == lineality(K.dual()) - True - - """ - return (K.lattice_dim() - K.dim()) - - -def discrete_complementarity_set(K): - r""" - Compute the discrete complementarity set of this cone. - - The complementarity set of this cone is the set of all orthogonal - pairs `(x,s)` such that `x` is in this cone, and `s` is in its - dual. The discrete complementarity set restricts `x` and `s` to be - generators of their respective cones. - - OUTPUT: - - A list of pairs `(x,s)` such that, - - * `x` is in this cone. - * `x` is a generator of this cone. - * `s` is in this cone's dual. - * `s` is a generator of this cone's dual. - * `x` and `s` are orthogonal. - - EXAMPLES: - - The discrete complementarity set of the nonnegative orthant consists - of pairs of standard basis vectors:: - - sage: K = Cone([(1,0),(0,1)]) - sage: discrete_complementarity_set(K) - [((1, 0), (0, 1)), ((0, 1), (1, 0))] - - If the cone consists of a single ray, the second components of the - discrete complementarity set should generate the orthogonal - complement of that ray:: - - sage: K = Cone([(1,0)]) - sage: discrete_complementarity_set(K) - [((1, 0), (0, 1)), ((1, 0), (0, -1))] - sage: K = Cone([(1,0,0)]) - sage: discrete_complementarity_set(K) - [((1, 0, 0), (0, 1, 0)), - ((1, 0, 0), (0, -1, 0)), - ((1, 0, 0), (0, 0, 1)), - ((1, 0, 0), (0, 0, -1))] - - When the cone is the entire space, its dual is the trivial cone, so - the discrete complementarity set is empty:: - - sage: K = Cone([(1,0),(-1,0),(0,1),(0,-1)]) - sage: discrete_complementarity_set(K) - [] - - TESTS: - - The complementarity set of the dual can be obtained by switching the - components of the complementarity set of the original cone:: - - sage: set_random_seed() - sage: K1 = random_cone(max_dim=6) - sage: K2 = K1.dual() - sage: expected = [(x,s) for (s,x) in discrete_complementarity_set(K2)] - sage: actual = discrete_complementarity_set(K1) - sage: sorted(actual) == sorted(expected) - True - - """ - V = K.lattice().vector_space() - - # Convert the rays to vectors so that we can compute inner - # products. - xs = [V(x) for x in K.rays()] - ss = [V(s) for s in K.dual().rays()] - - return [(x,s) for x in xs for s in ss if x.inner_product(s) == 0] - - -def LL(K): - r""" - Compute the space `\mathbf{LL}` of all Lyapunov-like transformations - on this cone. - - OUTPUT: - - A list of matrices forming a basis for the space of all - Lyapunov-like transformations on the given cone. - - EXAMPLES: - - The trivial cone has no Lyapunov-like transformations:: - - sage: L = ToricLattice(0) - sage: K = Cone([], lattice=L) - sage: LL(K) - [] - - The Lyapunov-like transformations on the nonnegative orthant are - simply diagonal matrices:: - - sage: K = Cone([(1,)]) - sage: LL(K) - [[1]] - - sage: K = Cone([(1,0),(0,1)]) - sage: LL(K) - [ - [1 0] [0 0] - [0 0], [0 1] - ] - - sage: K = Cone([(1,0,0),(0,1,0),(0,0,1)]) - sage: LL(K) - [ - [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 `L^{3}_{1}` and - `L^{3}_{\infty}` cones [Rudolf et al.]_:: - - sage: L31 = Cone([(1,0,1), (0,-1,1), (-1,0,1), (0,1,1)]) - sage: LL(L31) - [ - [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: LL(L3infty) - [ - [1 0 0] - [0 1 0] - [0 0 1] - ] - - If our cone is the entire space, then every transformation on it is - Lyapunov-like:: - - sage: K = Cone([(1,0), (-1,0), (0,1), (0,-1)]) - sage: M = MatrixSpace(QQ,2) - sage: M.basis() == LL(K) - True - - TESTS: - - The inner product `\left< L\left(x\right), s \right>` is zero for - every pair `\left( x,s \right)` in the discrete complementarity set - of the cone:: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8) - sage: C_of_K = discrete_complementarity_set(K) - sage: l = [ (L*x).inner_product(s) for (x,s) in C_of_K for L in LL(K) ] - sage: sum(map(abs, l)) - 0 - - The Lyapunov-like transformations on a cone and its dual are related - by transposition, but we're not guaranteed to compute transposed - elements of `LL\left( K \right)` as our basis for `LL\left( K^{*} - \right)` - - sage: set_random_seed() - sage: K = random_cone(max_dim=8) - sage: LL2 = [ L.transpose() for L in LL(K.dual()) ] - sage: V = VectorSpace( K.lattice().base_field(), K.lattice_dim()^2) - sage: LL1_vecs = [ V(m.list()) for m in LL(K) ] - sage: LL2_vecs = [ V(m.list()) for m in LL2 ] - sage: V.span(LL1_vecs) == V.span(LL2_vecs) - True - - """ - V = K.lattice().vector_space() - - C_of_K = discrete_complementarity_set(K) - - tensor_products = [ s.tensor_product(x) for (x,s) in C_of_K ] - - # Sage doesn't think matrices are vectors, so we have to convert - # our matrices to vectors explicitly before we can figure out how - # many are linearly-indepenedent. - # - # The space W has the same base ring as V, but dimension - # dim(V)^2. So it has the same dimension as the space of linear - # transformations on V. In other words, it's just the right size - # to create an isomorphism between it and our matrices. - W = VectorSpace(V.base_ring(), V.dimension()**2) - - # Turn our matrices into long vectors... - vectors = [ W(m.list()) for m in tensor_products ] - - # Vector space representation of Lyapunov-like matrices - # (i.e. vec(L) where L is Luapunov-like). - LL_vector = W.span(vectors).complement() - - # Now construct an ambient MatrixSpace in which to stick our - # transformations. - M = MatrixSpace(V.base_ring(), V.dimension()) - - matrix_basis = [ M(v.list()) for v in LL_vector.basis() ] - - return matrix_basis - - - -def lyapunov_rank(K): - r""" - Compute the Lyapunov (or bilinearity) rank of this cone. - - The Lyapunov rank of a cone can be thought of in (mainly) two ways: - - 1. The dimension of the Lie algebra of the automorphism group of the - cone. - - 2. The dimension of the linear space of all Lyapunov-like - transformations on the cone. - - INPUT: - - A closed, convex polyhedral cone. - - OUTPUT: - - An integer representing the Lyapunov rank of the cone. If the - dimension of the ambient vector space is `n`, then the Lyapunov rank - will be between `1` and `n` inclusive; however a rank of `n-1` is - not possible (see the first reference). - - .. note:: - - In the references, the cones are always assumed to be proper. We - do not impose this restriction. - - .. seealso:: - - :meth:`is_proper` - - ALGORITHM: - - The codimension formula from the second reference is used. We find - all pairs `(x,s)` in the complementarity set of `K` such that `x` - and `s` are rays of our cone. It is known that these vectors are - sufficient to apply the codimension formula. Once we have all such - pairs, we "brute force" the codimension formula by finding all - linearly-independent `xs^{T}`. - - REFERENCES: - - .. [Gowda/Tao] M.S. Gowda and J. Tao. On the bilinearity rank of a proper - cone and Lyapunov-like transformations, Mathematical Programming, 147 - (2014) 155-170. - - .. [Orlitzky/Gowda] M. Orlitzky and M. S. Gowda. The Lyapunov Rank of an - Improper Cone. Work in-progress. - - .. [Rudolf et al.] G. Rudolf, N. Noyan, D. Papp, and F. Alizadeh, Bilinear - optimality constraints for the cone of positive polynomials, - Mathematical Programming, Series B, 129 (2011) 5-31. - - EXAMPLES: - - The nonnegative orthant in `\mathbb{R}^{n}` always has rank `n` - [Rudolf et al.]_:: - - sage: positives = Cone([(1,)]) - sage: lyapunov_rank(positives) - 1 - sage: quadrant = Cone([(1,0), (0,1)]) - sage: lyapunov_rank(quadrant) - 2 - sage: octant = Cone([(1,0,0), (0,1,0), (0,0,1)]) - sage: lyapunov_rank(octant) - 3 - - The full space `\mathbb{R}^{n}` has Lyapunov rank `n^{2}` - [Orlitzky/Gowda]_:: - - sage: R5 = VectorSpace(QQ, 5) - sage: gs = R5.basis() + [ -r for r in R5.basis() ] - sage: K = Cone(gs) - sage: lyapunov_rank(K) - 25 - - The `L^{3}_{1}` cone is known to have a Lyapunov rank of one - [Rudolf et al.]_:: - - sage: L31 = Cone([(1,0,1), (0,-1,1), (-1,0,1), (0,1,1)]) - sage: lyapunov_rank(L31) - 1 - - Likewise for the `L^{3}_{\infty}` cone [Rudolf et al.]_:: - - sage: L3infty = Cone([(0,1,1), (1,0,1), (0,-1,1), (-1,0,1)]) - sage: lyapunov_rank(L3infty) - 1 - - A single ray in `n` dimensions should have Lyapunov rank `n^{2} - n - + 1` [Orlitzky/Gowda]_:: - - sage: K = Cone([(1,0,0,0,0)]) - sage: lyapunov_rank(K) - 21 - sage: K.lattice_dim()**2 - K.lattice_dim() + 1 - 21 - - A subspace (of dimension `m`) in `n` dimensions should have a - Lyapunov rank of `n^{2} - m\left(n - m)` [Orlitzky/Gowda]_:: - - sage: e1 = (1,0,0,0,0) - sage: neg_e1 = (-1,0,0,0,0) - sage: e2 = (0,1,0,0,0) - sage: neg_e2 = (0,-1,0,0,0) - sage: z = (0,0,0,0,0) - sage: K = Cone([e1, neg_e1, e2, neg_e2, z, z, z]) - sage: lyapunov_rank(K) - 19 - sage: K.lattice_dim()**2 - K.dim()*codim(K) - 19 - - The Lyapunov rank should be additive on a product of proper cones - [Rudolf et al.]_:: - - 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: lyapunov_rank(K) == lyapunov_rank(L31) + lyapunov_rank(octant) - True - - Two isomorphic cones should have the same Lyapunov rank [Rudolf et al.]_. - The cone ``K`` in the following example is isomorphic to the nonnegative - octant in `\mathbb{R}^{3}`:: - - sage: K = Cone([(1,2,3), (-1,1,0), (1,0,6)]) - sage: lyapunov_rank(K) - 3 - - The dual cone `K^{*}` of ``K`` should have the same Lyapunov rank as ``K`` - itself [Rudolf et al.]_:: - - sage: K = Cone([(2,2,4), (-1,9,0), (2,0,6)]) - sage: lyapunov_rank(K) == lyapunov_rank(K.dual()) - True - - TESTS: - - The Lyapunov rank should be additive on a product of proper cones - [Rudolf et al.]_:: - - sage: set_random_seed() - sage: K1 = random_cone(max_dim=8, strictly_convex=True, solid=True) - sage: K2 = random_cone(max_dim=8, strictly_convex=True, solid=True) - sage: K = K1.cartesian_product(K2) - sage: lyapunov_rank(K) == lyapunov_rank(K1) + lyapunov_rank(K2) - True - - The dual cone `K^{*}` of ``K`` should have the same Lyapunov rank as ``K`` - itself [Rudolf et al.]_:: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8) - sage: lyapunov_rank(K) == lyapunov_rank(K.dual()) - True - - Make sure we exercise the non-strictly-convex/non-solid case:: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=False, solid=False) - sage: lyapunov_rank(K) == lyapunov_rank(K.dual()) - True - - Let's check the other permutations as well, just to be sure:: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=False, solid=True) - sage: lyapunov_rank(K) == lyapunov_rank(K.dual()) - True - - :: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=True, solid=False) - sage: lyapunov_rank(K) == lyapunov_rank(K.dual()) - True - - :: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=True, solid=True) - sage: lyapunov_rank(K) == lyapunov_rank(K.dual()) - True - - The Lyapunov rank of a proper polyhedral cone in `n` dimensions can - be any number between `1` and `n` inclusive, excluding `n-1` - [Gowda/Tao]_. By accident, the `n-1` restriction will hold for the - trivial cone in a trivial space as well. However, in zero dimensions, - the Lyapunov rank of the trivial cone will be zero:: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=True, solid=True) - sage: b = lyapunov_rank(K) - sage: n = K.lattice_dim() - sage: (n == 0 or 1 <= b) and b <= n - True - sage: b == n-1 - False - - In fact [Orlitzky/Gowda]_, no closed convex polyhedral cone can have - Lyapunov rank `n-1` in `n` dimensions:: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8) - sage: b = lyapunov_rank(K) - sage: n = K.lattice_dim() - sage: b == n-1 - False - - The calculation of the Lyapunov rank of an improper cone can be - reduced to that of a proper cone [Orlitzky/Gowda]_:: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8) - sage: actual = lyapunov_rank(K) - sage: K_S = restrict_span(K) - sage: P = restrict_span(K_S.dual()).dual() - sage: l = lineality(K) - sage: c = codim(K) - sage: expected = lyapunov_rank(P) + K.dim()*(l + c) + c**2 - sage: actual == expected - True - - The Lyapunov rank of a proper cone is just the dimension of ``LL(K)``:: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=True, solid=True) - sage: lyapunov_rank(K) == len(LL(K)) - True - - In fact the same can be said of any cone. These additional tests - just increase our confidence that the reduction scheme works:: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=True, solid=False) - sage: lyapunov_rank(K) == len(LL(K)) - True - - :: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=False, solid=True) - sage: lyapunov_rank(K) == len(LL(K)) - True - - :: - - sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=False, solid=False) - sage: lyapunov_rank(K) == len(LL(K)) - True - - """ - K_orig = K - beta = 0 - - m = K.dim() - n = K.lattice_dim() - l = lineality(K) - - if m < n: - # K is not solid, project onto its span. - K = restrict_span(K) - - # Lemma 2 - beta += m*(n - m) + (n - m)**2 - - if l > 0: - # K is not pointed, project its dual onto its span. - # Uses a proposition from our paper, i.e. this is - # equivalent to K = restrict_span(K.dual()).dual() - #K = restrict_span(intersect_span(K,K.dual()), K.dual()) - K = restrict_span(K, K.dual()) - - # Lemma 3 - beta += m * l - - beta += len(LL(K)) - return beta + K2 = K1 + gens = K1.positive_operators_gens(K2) + L = ToricLattice(K1.lattice_dim()*K2.lattice_dim()) + return Cone(( g.list() for g in gens ), lattice=L, check=False)