X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=mjo%2Fcone%2Fcone.py;h=f3543a147ad8da3c5000015f3c53e837781180e5;hb=fbaecc56ec029d6f813d76e26bd8891a41416bf0;hp=ba5f51ea880ccdc2cc3344cb8b91022ff3e5b8cf;hpb=e041595c10751828f196db2cda86bd0f15a81191;p=sage.d.git diff --git a/mjo/cone/cone.py b/mjo/cone/cone.py index ba5f51e..f3543a1 100644 --- a/mjo/cone/cone.py +++ b/mjo/cone/cone.py @@ -8,23 +8,55 @@ addsitedir(abspath('../../')) from sage.all import * -def drop_dependent(vs): +def _basically_the_same(K1, K2): r""" - Return the largest linearly-independent subset of ``vs``. - """ - result = [] - m = matrix(vs).echelon_form() - for idx in range(0, m.nrows()): - if not m[idx].is_zero(): - result.append(m[idx]) + Test whether or not ``K1`` and ``K2`` are "basically the same." - return result + This is a hack to get around the fact that it's difficult to tell + when two cones are linearly isomorphic. We have a proposition that + equates two cones, but represented over `\mathbb{Q}`, they are + merely linearly isomorphic (not equal). So rather than test for + equality, we test a list of properties that should be preserved + under an invertible linear transformation. + OUTPUT: -def basically_the_same(K1,K2): - r""" ``True`` if ``K1`` and ``K2`` are basically the same, and ``False`` otherwise. + + EXAMPLES: + + Any proper cone with three generators in `\mathbb{R}^{3}` is + basically the same as the nonnegative orthant:: + + sage: K1 = Cone([(1,0,0), (0,1,0), (0,0,1)]) + sage: K2 = Cone([(1,2,3), (3, 18, 4), (66, 51, 0)]) + sage: _basically_the_same(K1, K2) + True + + Negating a cone gives you another cone that is basically the same:: + + sage: K = Cone([(0,2,-5), (-6, 2, 4), (0, 51, 0)]) + sage: _basically_the_same(K, -K) + True + + TESTS: + + Any cone is basically the same as itself:: + + sage: K = random_cone(max_ambient_dim = 8) + sage: _basically_the_same(K, K) + True + + After applying an invertible matrix to the rows of a cone, the + result should be basically the same as the cone we started with:: + + sage: K1 = random_cone(max_ambient_dim = 8) + sage: A = random_matrix(QQ, K1.lattice_dim(), algorithm='unimodular') + sage: K2 = Cone( [ A*r for r in K1.rays() ], lattice=K1.lattice()) + sage: _basically_the_same(K1, K2) + True + """ if K1.lattice_dim() != K2.lattice_dim(): return False @@ -35,7 +67,7 @@ def basically_the_same(K1,K2): if K1.dim() != K2.dim(): return False - if lineality(K1) != lineality(K2): + if K1.lineality() != K2.lineality(): return False if K1.is_solid() != K2.is_solid(): @@ -59,33 +91,41 @@ def basically_the_same(K1,K2): -def rho(K, K2=None): +def _restrict_to_space(K, W): r""" - Restrict ``K`` into its own span, or the span of another cone. + Restrict this cone a subspace of its ambient space. INPUT: - - ``K2`` -- another cone whose lattice has the same rank as this cone. + - ``W`` -- The subspace into which this cone will be restricted. OUTPUT: - A new cone in a sublattice. + A new cone in a sublattice corresponding to ``W``. + + EXAMPLES: - EXAMPLES:: + When this cone is solid, restricting it into its own span should do + nothing:: sage: K = Cone([(1,)]) - sage: rho(K) == K + sage: _restrict_to_space(K, K.span()) == K True + A single ray restricted into its own span gives the same output + regardless of the ambient space:: + sage: K2 = Cone([(1,0)]) - sage: rho(K2).rays() + sage: K2_S = _restrict_to_space(K2, K2.span()).rays() + sage: K2_S N(1) in 1-d lattice N sage: K3 = Cone([(1,0,0)]) - sage: rho(K3).rays() + sage: K3_S = _restrict_to_space(K3, K3.span()).rays() + sage: K3_S N(1) in 1-d lattice N - sage: rho(K2) == rho(K3) + sage: K2_S == K3_S True TESTS: @@ -93,339 +133,110 @@ def rho(K, K2=None): The projected cone should always be solid:: sage: set_random_seed() - sage: K = random_cone(max_dim = 8) - sage: K_S = rho(K) - sage: K_S.is_solid() + sage: K = random_cone(max_ambient_dim = 8) + sage: _restrict_to_space(K, K.span()).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 = rho(K, K.dual() ) - sage: K_S.lattice_dim() == K.dual().dim() + sage: K = random_cone(max_ambient_dim = 8) + sage: K_P = _restrict_to_space(K, K.dual().span()) + sage: K_P.lattice_dim() == K.dual().dim() 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() == rho(K).dim() + sage: K = random_cone(max_ambient_dim = 8) + sage: K.dim() == _restrict_to_space(K,K.span()).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(rho(K)) + sage: K = random_cone(max_ambient_dim = 8) + sage: K.lineality() == _restrict_to_space(K, K.span()).lineality() 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(rho(K)) + sage: K = random_cone(max_ambient_dim = 8) + sage: S = K.span(); P = K.dual().span() + sage: K.lineality() >= _restrict_to_space(K,S).lineality() True - sage: lineality(K) >= lineality(rho(K, K.dual())) + sage: K.lineality() >= _restrict_to_space(K,P).lineality() 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 = rho(K) - sage: P = rho(K_S.dual()).dual() - sage: P.is_proper() + sage: K = random_cone(max_ambient_dim = 8) + sage: K_S = _restrict_to_space(K, K.span()) + sage: K_SP = _restrict_to_space(K_S.dual(), K_S.dual().span()).dual() + sage: K_SP.is_proper() True - sage: P = rho(K_S, K_S.dual()) - sage: P.is_proper() + sage: K_SP = _restrict_to_space(K_S, K_S.dual().span()) + sage: K_SP.is_proper() True - :: + Test the proposition in our paper concerning the duals and + restrictions. Generate a random cone, then create a subcone of + it. The operation of dual-taking should then commute with + _restrict_to_space:: sage: set_random_seed() - sage: K = random_cone(max_dim = 8, strictly_convex=True, solid=False) - sage: K_S = rho(K) - sage: P = rho(K_S.dual()).dual() - sage: P.is_proper() - True - sage: P = rho(K_S, K_S.dual()) - sage: P.is_proper() - True - - :: - - sage: set_random_seed() - sage: K = random_cone(max_dim = 8, strictly_convex=False, solid=True) - sage: K_S = rho(K) - sage: P = rho(K_S.dual()).dual() - sage: P.is_proper() - True - sage: P = rho(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 = rho(K) - sage: P = rho(K_S.dual()).dual() - sage: P.is_proper() - True - sage: P = rho(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 = rho(K, K.dual()) - sage: K_star_W_star = rho(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 = rho(K, K.dual()) - sage: K_star_W_star = rho(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 = rho(K, K.dual()) - sage: K_star_W_star = rho(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 = rho(K, K.dual()) - sage: K_star_W_star = rho(K.dual()).dual() - sage: basically_the_same(K_W, K_star_W_star) + sage: J = random_cone(max_ambient_dim = 8) + sage: K = Cone(random_sublist(J.rays(), 0.5), lattice=J.lattice()) + sage: K_W_star = _restrict_to_space(K, J.span()).dual() + sage: K_star_W = _restrict_to_space(K.dual(), J.span()) + sage: _basically_the_same(K_W_star, K_star_W) True """ - if K2 is None: - K2 = K - - # First we project K onto the span of K2. This can be done with - # cones (i.e. without converting to vector spaces), but it's - # annoying to deal with lattice mismatches. - span_K2 = Cone(K2.rays() + (-K2).rays(), lattice=K.lattice()) - K = K.intersection(span_K2) - - V = K.lattice().vector_space() - - # Create the space W \times W^{\perp} isomorphic to V. - # First we get an orthogonal (but not normal) basis... - W_basis = drop_dependent(K2.rays()) - W = V.subspace_with_basis(W_basis) + # First we want to intersect ``K`` with ``W``. The easiest way to + # do this is via cone intersection, so we turn the subspace ``W`` + # into a cone. + W_cone = Cone(W.basis() + [-b for b in W.basis()], lattice=K.lattice()) + K = K.intersection(W_cone) # We've already intersected K with the span of K2, so every # generator of K should belong to W now. - W_rays = [ W.coordinate_vector(r) for r in K.rays() ] - - L = ToricLattice(K2.dim()) - return Cone(W_rays, lattice=L) + K_W_rays = [ W.coordinate_vector(r) for r in K.rays() ] + L = ToricLattice(W.dimension()) + return Cone(K_W_rays, 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. + Compute a 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. + A discrete complementarity set of `K` is the set of all orthogonal + pairs `(x,s)` such that `x \in G_{1}` and `s \in G_{2}` for some + generating sets `G_{1}` of `K` and `G_{2}` of its dual. Polyhedral + convex cones are input in terms of their generators, so "the" (this + particular) discrete complementarity set corresponds to ``G1 + == K.rays()`` and ``G2 == K.dual().rays()``. 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. + * Both `x` and `s` are vectors (not rays). + * `x` is one of ``K.rays()``. + * `s` is one of ``K.dual().rays()``. * `x` and `s` are orthogonal. + REFERENCES: + + .. [Orlitzky/Gowda] M. Orlitzky and M. S. Gowda. The Lyapunov Rank of an + Improper Cone. Work in-progress. + EXAMPLES: The discrete complementarity set of the nonnegative orthant consists @@ -456,25 +267,43 @@ def discrete_complementarity_set(K): sage: discrete_complementarity_set(K) [] + Likewise when this cone is trivial (its dual is the entire space):: + + sage: L = ToricLattice(0) + sage: K = Cone([], ToricLattice(0)) + 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: K1 = random_cone(max_ambient_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 + The pairs in the discrete complementarity set are in fact + complementary:: + + sage: set_random_seed() + sage: K = random_cone(max_ambient_dim=6) + sage: dcs = discrete_complementarity_set(K) + sage: sum([x.inner_product(s).abs() for (x,s) in dcs]) + 0 + """ V = K.lattice().vector_space() - # Convert the rays to vectors so that we can compute inner - # products. + # Convert rays to vectors so that we can compute inner products. xs = [V(x) for x in K.rays()] + + # We also convert the generators of the dual cone so that we + # return pairs of vectors and not (vector, ray) pairs. 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] @@ -555,7 +384,7 @@ def LL(K): of the cone:: sage: set_random_seed() - sage: K = random_cone(max_dim=8) + sage: K = random_cone(max_ambient_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)) @@ -567,7 +396,7 @@ def LL(K): \right)` sage: set_random_seed() - sage: K = random_cone(max_dim=8) + sage: K = random_cone(max_ambient_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) ] @@ -611,7 +440,7 @@ def LL(K): def lyapunov_rank(K): r""" - Compute the Lyapunov (or bilinearity) rank of this cone. + Compute the Lyapunov rank (or bilinearity rank) of this cone. The Lyapunov rank of a cone can be thought of in (mainly) two ways: @@ -630,16 +459,7 @@ def lyapunov_rank(K): 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` + not possible (see [Orlitzky/Gowda]_). ALGORITHM: @@ -720,7 +540,7 @@ def lyapunov_rank(K): 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) + sage: K.lattice_dim()**2 - K.dim()*K.codim() 19 The Lyapunov rank should be additive on a product of proper cones @@ -753,45 +573,30 @@ def lyapunov_rank(K): [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: K1 = random_cone(max_ambient_dim=8, + ....: strictly_convex=True, + ....: solid=True) + sage: K2 = random_cone(max_ambient_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:: + The Lyapunov rank is invariant under a linear isomorphism + [Orlitzky/Gowda]_:: - sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=False, solid=True) - sage: lyapunov_rank(K) == lyapunov_rank(K.dual()) + sage: K1 = random_cone(max_ambient_dim = 8) + sage: A = random_matrix(QQ, K1.lattice_dim(), algorithm='unimodular') + sage: K2 = Cone( [ A*r for r in K1.rays() ], lattice=K1.lattice()) + sage: lyapunov_rank(K1) == lyapunov_rank(K2) 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 - - :: + 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, strictly_convex=True, solid=True) + sage: K = random_cone(max_ambient_dim=8) sage: lyapunov_rank(K) == lyapunov_rank(K.dual()) True @@ -802,7 +607,9 @@ def lyapunov_rank(K): 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: K = random_cone(max_ambient_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 @@ -814,7 +621,7 @@ def lyapunov_rank(K): Lyapunov rank `n-1` in `n` dimensions:: sage: set_random_seed() - sage: K = random_cone(max_dim=8) + sage: K = random_cone(max_ambient_dim=8) sage: b = lyapunov_rank(K) sage: n = K.lattice_dim() sage: b == n-1 @@ -824,68 +631,106 @@ def lyapunov_rank(K): reduced to that of a proper cone [Orlitzky/Gowda]_:: sage: set_random_seed() - sage: K = random_cone(max_dim=8) + sage: K = random_cone(max_ambient_dim=8) sage: actual = lyapunov_rank(K) - sage: K_S = rho(K) - sage: P = rho(K_S.dual()).dual() - sage: l = lineality(K) - sage: c = codim(K) - sage: expected = lyapunov_rank(P) + K.dim()*(l + c) + c**2 + sage: K_S = _restrict_to_space(K, K.span()) + sage: K_SP = _restrict_to_space(K_S.dual(), K_S.dual().span()).dual() + sage: l = K.lineality() + sage: c = K.codim() + sage: expected = lyapunov_rank(K_SP) + 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 - - :: + The Lyapunov rank of any cone is just the dimension of ``LL(K)``:: sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=False, solid=True) + sage: K = random_cone(max_ambient_dim=8) sage: lyapunov_rank(K) == len(LL(K)) True - :: + We can make an imperfect cone perfect by adding a slack variable + (a Theorem in [Orlitzky/Gowda]_):: sage: set_random_seed() - sage: K = random_cone(max_dim=8, strictly_convex=False, solid=False) - sage: lyapunov_rank(K) == len(LL(K)) + sage: K = random_cone(max_ambient_dim=8, + ....: strictly_convex=True, + ....: solid=True) + sage: L = ToricLattice(K.lattice_dim() + 1) + sage: K = Cone([ r.list() + [0] for r in K.rays() ], lattice=L) + sage: lyapunov_rank(K) >= K.lattice_dim() True """ - K_orig = K beta = 0 m = K.dim() n = K.lattice_dim() - l = lineality(K) + l = K.lineality() if m < n: - # K is not solid, project onto its span. - K = rho(K) + # K is not solid, restrict to its span. + K = _restrict_to_space(K, K.span()) - # Lemma 2 - beta += m*(n - m) + (n - m)**2 + # Non-solid reduction lemma. + beta += (n - m)*n 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 = rho(K.dual()).dual() - K = rho(K, K.dual()) + # K is not pointed, restrict to the span of its dual. Uses a + # proposition from our paper, i.e. this is equivalent to K = + # _rho(K.dual()).dual(). + K = _restrict_to_space(K, K.dual().span()) - # Lemma 3 - beta += m * l + # Non-pointed reduction lemma. + beta += l * m beta += len(LL(K)) return beta + + + +def is_lyapunov_like(L,K): + r""" + Determine whether or not ``L`` is Lyapunov-like on ``K``. + + We say that ``L`` is Lyapunov-like on ``K`` if `\left\langle + L\left\lparenx\right\rparen,s\right\rangle = 0` for all pairs + `\left\langle x,s \right\rangle` in the complementarity set of + ``K``. It is known [Orlitzky]_ that this property need only be + checked for generators of ``K`` and its dual. + + INPUT: + + - ``L`` -- A linear transformation or matrix. + + - ``K`` -- A polyhedral closed convex cone. + + OUTPUT: + + ``True`` if it can be proven that ``L`` is Lyapunov-like on ``K``, + and ``False`` otherwise. + + .. WARNING:: + + If this function returns ``True``, then ``L`` is Lyapunov-like + on ``K``. However, if ``False`` is returned, that could mean one + of two things. The first is that ``L`` is definitely not + Lyapunov-like on ``K``. The second is more of an "I don't know" + answer, returned (for example) if we cannot prove that an inner + product is zero. + + REFERENCES: + + .. [Orlitzky] M. Orlitzky. The Lyapunov rank of an + improper cone (preprint). + + EXAMPLES: + + todo. + + TESTS: + + todo. + + """ + return all([(L*x).inner_product(s) == 0 + for (x,s) in discrete_complementarity_set(K)])