Simplify implementation of positive_operators().

diff --git a/mjo/cone/cone.py b/mjo/cone/cone.py
index f6f378e61cd71c93eb4abf3de017272d11192e0f..3f915ed66fb4ea33629bc1c30ff4e021f59f8607 100644 (file)
--- a/mjo/cone/cone.py
+++ b/mjo/cone/cone.py
@@ -637,13 +637,11 @@ def positive_operators(K):
     A positive operator on a cone should send its generators into the cone::
 
         sage: K = random_cone(max_ambient_dim = 6)
-        sage: pi_of_k = positive_operators(K)
-        sage: all([K.contains(p*x) for p in pi_of_k for x in K.rays()])
+        sage: pi_of_K = positive_operators(K)
+        sage: all([K.contains(p*x) for p in pi_of_K for x in K.rays()])
         True
 
     """
-    V = K.lattice().vector_space()
-
     # 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.
@@ -652,12 +650,10 @@ def positive_operators(K):
     # 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.
+    V = K.lattice().vector_space()
     W = VectorSpace(V.base_ring(), V.dimension()**2)
 
-    G1 = [ V(x) for x in K.rays() ]
-    G2 = [ V(s) for s in K.dual().rays() ]
-
-    tensor_products = [ s.tensor_product(x) for x in G1 for s in G2 ]
+    tensor_products = [ s.tensor_product(x) for x in K for s in K.dual() ]
 
     # Turn our matrices into long vectors...
     vectors = [ W(m.list()) for m in tensor_products ]