Add restrict_to_subspace function and use it to test the sage lyapunov_rank().

[sage.d.git] / mjo / cone / cone.py

diff --git a/mjo/cone/cone.py b/mjo/cone/cone.py
index e9d70e42ef61e0b44d492e6d46687f383ab8b1c6..1ab6b97c128cde3d1e176032cf0d90f601057166 100644 (file)
--- a/mjo/cone/cone.py
+++ b/mjo/cone/cone.py
@@ -197,6 +197,18 @@ def positive_operator_gens(K):
         sage: actual == expected
         True
 
+    The lineality of the cone of positive operators is given by the
+    corollary in my paper::
+
+        sage: set_random_seed()
+        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: actual = Cone([p.list() for p in pi_of_K], lattice=L).lineality()
+        sage: expected = n**2 - K.dim()*K.dual().dim()
+        sage: actual == expected
+        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
@@ -289,6 +301,13 @@ def Z_transformation_gens(K):
         sage: z_cone.linear_subspace() == lls
         True
 
+    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: z_cone.lineality() == K.lyapunov_rank()
+        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