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gitweb.michael.orlitzky.com - sage.d.git/blob - mjo/cone/cone.py
28a84231d1eb461a1e12d0df258f980e48f21f2d
3 def is_lyapunov_like(L
,K
):
5 Determine whether or not ``L`` is Lyapunov-like on ``K``.
7 We say that ``L`` is Lyapunov-like on ``K`` if `\left\langle
8 L\left\lparenx\right\rparen,s\right\rangle = 0` for all pairs
9 `\left\langle x,s \right\rangle` in the complementarity set of
10 ``K``. It is known [Orlitzky]_ that this property need only be
11 checked for generators of ``K`` and its dual.
15 - ``L`` -- A linear transformation or matrix.
17 - ``K`` -- A polyhedral closed convex cone.
21 ``True`` if it can be proven that ``L`` is Lyapunov-like on ``K``,
22 and ``False`` otherwise.
26 If this function returns ``True``, then ``L`` is Lyapunov-like
27 on ``K``. However, if ``False`` is returned, that could mean one
28 of two things. The first is that ``L`` is definitely not
29 Lyapunov-like on ``K``. The second is more of an "I don't know"
30 answer, returned (for example) if we cannot prove that an inner
35 M. Orlitzky. The Lyapunov rank of an improper cone.
36 http://www.optimization-online.org/DB_HTML/2015/10/5135.html
40 The identity is always Lyapunov-like in a nontrivial space::
42 sage: set_random_seed()
43 sage: K = random_cone(min_ambient_dim=1, max_ambient_dim=8)
44 sage: L = identity_matrix(K.lattice_dim())
45 sage: is_lyapunov_like(L,K)
48 As is the "zero" transformation::
50 sage: K = random_cone(min_ambient_dim=1, max_ambient_dim=8)
51 sage: R = K.lattice().vector_space().base_ring()
52 sage: L = zero_matrix(R, K.lattice_dim())
53 sage: is_lyapunov_like(L,K)
56 Everything in ``K.lyapunov_like_basis()`` should be Lyapunov-like
59 sage: K = random_cone(min_ambient_dim=1, max_ambient_dim=6)
60 sage: all([ is_lyapunov_like(L,K) for L in K.lyapunov_like_basis() ])
64 return all([(L
*x
).inner_product(s
) == 0
65 for (x
,s
) in K
.discrete_complementarity_set()])
68 def motzkin_decomposition(K
):
70 Return the pair of components in the Motzkin decomposition of this cone.
72 Every convex cone is the direct sum of a strictly convex cone and a
73 linear subspace [Stoer-Witzgall]_. Return a pair ``(P,S)`` of cones
74 such that ``P`` is strictly convex, ``S`` is a subspace, and ``K``
75 is the direct sum of ``P`` and ``S``.
79 An ordered pair ``(P,S)`` of closed convex polyhedral cones where
80 ``P`` is strictly convex, ``S`` is a subspace, and ``K`` is the
81 direct sum of ``P`` and ``S``.
85 .. [Stoer-Witzgall] J. Stoer and C. Witzgall. Convexity and
86 Optimization in Finite Dimensions I. Springer-Verlag, New
91 The nonnegative orthant is strictly convex, so it is its own
92 strictly convex component and its subspace component is trivial::
94 sage: K = Cone([(1,0,0),(0,1,0),(0,0,1)])
95 sage: (P,S) = motzkin_decomposition(K)
96 sage: K.is_equivalent(P)
101 Likewise, full spaces are their own subspace components::
103 sage: K = Cone([(1,0),(-1,0),(0,1),(0,-1)])
104 sage: K.is_full_space()
106 sage: (P,S) = motzkin_decomposition(K)
107 sage: K.is_equivalent(S)
114 A random point in the cone should belong to either the strictly
115 convex component or the subspace component. If the point is nonzero,
116 it cannot be in both::
118 sage: set_random_seed()
119 sage: K = random_cone(max_ambient_dim=8)
120 sage: (P,S) = motzkin_decomposition(K)
121 sage: x = K.random_element()
122 sage: P.contains(x) or S.contains(x)
124 sage: x.is_zero() or (P.contains(x) != S.contains(x))
127 The strictly convex component should always be strictly convex, and
128 the subspace component should always be a subspace::
130 sage: set_random_seed()
131 sage: K = random_cone(max_ambient_dim=8)
132 sage: (P,S) = motzkin_decomposition(K)
133 sage: P.is_strictly_convex()
135 sage: S.lineality() == S.dim()
138 The generators of the components are obtained from orthogonal
139 projections of the original generators [Stoer-Witzgall]_::
141 sage: set_random_seed()
142 sage: K = random_cone(max_ambient_dim=8)
143 sage: (P,S) = motzkin_decomposition(K)
144 sage: A = S.linear_subspace().complement().matrix()
145 sage: proj_S_perp = A.transpose() * (A*A.transpose()).inverse() * A
146 sage: expected_P = Cone([ proj_S_perp*g for g in K ], K.lattice())
147 sage: P.is_equivalent(expected_P)
149 sage: A = S.linear_subspace().matrix()
150 sage: proj_S = A.transpose() * (A*A.transpose()).inverse() * A
151 sage: expected_S = Cone([ proj_S*g for g in K ], K.lattice())
152 sage: S.is_equivalent(expected_S)
155 # The lines() method only returns one generator per line. For a true
156 # line, we also need a generator pointing in the opposite direction.
157 S_gens
= [ direction
*gen
for direction
in [1,-1] for gen
in K
.lines() ]
158 S
= Cone(S_gens
, K
.lattice())
160 # Since ``S`` is a subspace, the rays of its dual generate its
161 # orthogonal complement.
162 S_perp
= Cone(S
.dual(), K
.lattice())
163 P
= K
.intersection(S_perp
)
168 def positive_operator_gens(K
):
170 Compute generators of the cone of positive operators on this cone.
174 A list of `n`-by-``n`` matrices where ``n == K.lattice_dim()``.
175 Each matrix ``P`` in the list should have the property that ``P*x``
176 is an element of ``K`` whenever ``x`` is an element of
177 ``K``. Moreover, any nonnegative linear combination of these
178 matrices shares the same property.
182 The trivial cone in a trivial space has no positive operators::
184 sage: K = Cone([], ToricLattice(0))
185 sage: positive_operator_gens(K)
188 Positive operators on the nonnegative orthant are nonnegative matrices::
190 sage: K = Cone([(1,)])
191 sage: positive_operator_gens(K)
194 sage: K = Cone([(1,0),(0,1)])
195 sage: positive_operator_gens(K)
197 [1 0] [0 1] [0 0] [0 0]
198 [0 0], [0 0], [1 0], [0 1]
201 Every operator is positive on the ambient vector space::
203 sage: K = Cone([(1,),(-1,)])
204 sage: K.is_full_space()
206 sage: positive_operator_gens(K)
209 sage: K = Cone([(1,0),(-1,0),(0,1),(0,-1)])
210 sage: K.is_full_space()
212 sage: positive_operator_gens(K)
214 [1 0] [-1 0] [0 1] [ 0 -1] [0 0] [ 0 0] [0 0] [ 0 0]
215 [0 0], [ 0 0], [0 0], [ 0 0], [1 0], [-1 0], [0 1], [ 0 -1]
220 A positive operator on a cone should send its generators into the cone::
222 sage: set_random_seed()
223 sage: K = random_cone(max_ambient_dim=5)
224 sage: pi_of_K = positive_operator_gens(K)
225 sage: all([K.contains(p*x) for p in pi_of_K for x in K.rays()])
228 The dimension of the cone of positive operators is given by the
229 corollary in my paper::
231 sage: set_random_seed()
232 sage: K = random_cone(max_ambient_dim=5)
233 sage: n = K.lattice_dim()
235 sage: l = K.lineality()
236 sage: pi_of_K = positive_operator_gens(K)
237 sage: L = ToricLattice(n**2)
238 sage: actual = Cone([p.list() for p in pi_of_K], lattice=L).dim()
239 sage: expected = n**2 - l*(m - l) - (n - m)*m
240 sage: actual == expected
243 The lineality of the cone of positive operators is given by the
244 corollary in my paper::
246 sage: set_random_seed()
247 sage: K = random_cone(max_ambient_dim=5)
248 sage: n = K.lattice_dim()
249 sage: pi_of_K = positive_operator_gens(K)
250 sage: L = ToricLattice(n**2)
251 sage: actual = Cone([p.list() for p in pi_of_K], lattice=L).lineality()
252 sage: expected = n**2 - K.dim()*K.dual().dim()
253 sage: actual == expected
256 The cone ``K`` is proper if and only if the cone of positive
257 operators on ``K`` is proper::
259 sage: set_random_seed()
260 sage: K = random_cone(max_ambient_dim=5)
261 sage: pi_of_K = positive_operator_gens(K)
262 sage: L = ToricLattice(K.lattice_dim()**2)
263 sage: pi_cone = Cone([p.list() for p in pi_of_K], lattice=L)
264 sage: K.is_proper() == pi_cone.is_proper()
267 # Matrices are not vectors in Sage, so we have to convert them
268 # to vectors explicitly before we can find a basis. We need these
269 # two values to construct the appropriate "long vector" space.
270 F
= K
.lattice().base_field()
273 tensor_products
= [ s
.tensor_product(x
) for x
in K
for s
in K
.dual() ]
275 # Convert those tensor products to long vectors.
276 W
= VectorSpace(F
, n
**2)
277 vectors
= [ W(tp
.list()) for tp
in tensor_products
]
279 # Create the *dual* cone of the positive operators, expressed as
281 pi_dual
= Cone(vectors
, ToricLattice(W
.dimension()))
283 # Now compute the desired cone from its dual...
284 pi_cone
= pi_dual
.dual()
286 # And finally convert its rays back to matrix representations.
287 M
= MatrixSpace(F
, n
)
288 return [ M(v
.list()) for v
in pi_cone
.rays() ]
291 def Z_transformation_gens(K
):
293 Compute generators of the cone of Z-transformations on this cone.
297 A list of `n`-by-``n`` matrices where ``n == K.lattice_dim()``.
298 Each matrix ``L`` in the list should have the property that
299 ``(L*x).inner_product(s) <= 0`` whenever ``(x,s)`` is an element the
300 discrete complementarity set of ``K``. Moreover, any nonnegative
301 linear combination of these matrices shares the same property.
305 Z-transformations on the nonnegative orthant are just Z-matrices.
306 That is, matrices whose off-diagonal elements are nonnegative::
308 sage: K = Cone([(1,0),(0,1)])
309 sage: Z_transformation_gens(K)
311 [ 0 -1] [ 0 0] [-1 0] [1 0] [ 0 0] [0 0]
312 [ 0 0], [-1 0], [ 0 0], [0 0], [ 0 -1], [0 1]
314 sage: K = Cone([(1,0,0,0),(0,1,0,0),(0,0,1,0),(0,0,0,1)])
315 sage: all([ z[i][j] <= 0 for z in Z_transformation_gens(K)
316 ....: for i in range(z.nrows())
317 ....: for j in range(z.ncols())
321 The trivial cone in a trivial space has no Z-transformations::
323 sage: K = Cone([], ToricLattice(0))
324 sage: Z_transformation_gens(K)
327 Z-transformations on a subspace are Lyapunov-like and vice-versa::
329 sage: K = Cone([(1,0),(-1,0),(0,1),(0,-1)])
330 sage: K.is_full_space()
332 sage: lls = span([ vector(l.list()) for l in K.lyapunov_like_basis() ])
333 sage: zs = span([ vector(z.list()) for z in Z_transformation_gens(K) ])
339 The Z-property is possessed by every Z-transformation::
341 sage: set_random_seed()
342 sage: K = random_cone(max_ambient_dim=6)
343 sage: Z_of_K = Z_transformation_gens(K)
344 sage: dcs = K.discrete_complementarity_set()
345 sage: all([(z*x).inner_product(s) <= 0 for z in Z_of_K
346 ....: for (x,s) in dcs])
349 The lineality space of Z is LL::
351 sage: set_random_seed()
352 sage: K = random_cone(min_ambient_dim=1, max_ambient_dim=6)
353 sage: lls = span([ vector(l.list()) for l in K.lyapunov_like_basis() ])
354 sage: z_cone = Cone([ z.list() for z in Z_transformation_gens(K) ])
355 sage: z_cone.linear_subspace() == lls
358 And thus, the lineality of Z is the Lyapunov rank::
360 sage: set_random_seed()
361 sage: K = random_cone(max_ambient_dim=6)
362 sage: Z_of_K = Z_transformation_gens(K)
363 sage: L = ToricLattice(K.lattice_dim()**2)
364 sage: z_cone = Cone([ z.list() for z in Z_of_K ], lattice=L)
365 sage: z_cone.lineality() == K.lyapunov_rank()
368 The lineality spaces of pi-star and Z-star are equal:
370 sage: set_random_seed()
371 sage: K = random_cone(max_ambient_dim=5)
372 sage: pi_of_K = positive_operator_gens(K)
373 sage: Z_of_K = Z_transformation_gens(K)
374 sage: L = ToricLattice(K.lattice_dim()**2)
375 sage: pi_star = Cone([p.list() for p in pi_of_K], lattice=L).dual()
376 sage: z_star = Cone([ z.list() for z in Z_of_K], lattice=L).dual()
377 sage: pi_star.linear_subspace() == z_star.linear_subspace()
380 # Matrices are not vectors in Sage, so we have to convert them
381 # to vectors explicitly before we can find a basis. We need these
382 # two values to construct the appropriate "long vector" space.
383 F
= K
.lattice().base_field()
386 # These tensor products contain generators for the dual cone of
387 # the cross-positive transformations.
388 tensor_products
= [ s
.tensor_product(x
)
389 for (x
,s
) in K
.discrete_complementarity_set() ]
391 # Turn our matrices into long vectors...
392 W
= VectorSpace(F
, n
**2)
393 vectors
= [ W(m
.list()) for m
in tensor_products
]
395 # Create the *dual* cone of the cross-positive operators,
396 # expressed as long vectors..
397 Sigma_dual
= Cone(vectors
, lattice
=ToricLattice(W
.dimension()))
399 # Now compute the desired cone from its dual...
400 Sigma_cone
= Sigma_dual
.dual()
402 # And finally convert its rays back to matrix representations.
403 # But first, make them negative, so we get Z-transformations and
404 # not cross-positive ones.
405 M
= MatrixSpace(F
, n
)
406 return [ -M(v
.list()) for v
in Sigma_cone
.rays() ]
410 gens
= Z_transformation_gens(K
)
414 return Cone([ g
.list() for g
in gens
], lattice
=L
)
417 gens
= positive_operator_gens(K
)
421 return Cone([ g
.list() for g
in gens
], lattice
=L
)