mjo/cone: drop is_symmetric_p{s,}d() methods.

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

diff --git a/mjo/cone/doubly_nonnegative.py b/mjo/cone/doubly_nonnegative.py
index 373557c6c72dde4974dfdce30efe435d0d526cc2..5e10e1aebaad4c17de9e129681a57bb736e72886 100644 (file)
--- a/mjo/cone/doubly_nonnegative.py
+++ b/mjo/cone/doubly_nonnegative.py
@@ -14,9 +14,8 @@ It is represented typically by either `\mathcal{D}^{n}` or
 from sage.all import *
 
 from mjo.cone.symmetric_psd import (factor_psd,
-                                    is_symmetric_psd,
                                     random_symmetric_psd)
-from mjo.matrix_vector import basis_representation
+from mjo.basis_repr import basis_repr
 
 
 def is_doubly_nonnegative(A):
@@ -63,7 +62,7 @@ def is_doubly_nonnegative(A):
 
     # It's nonnegative, so all we need to do is check that it's
     # symmetric positive-semidefinite.
-    return is_symmetric_psd(A)
+    return A.is_positive_semidefinite()
 
 
 
@@ -354,7 +353,7 @@ def is_extreme_doubly_nonnegative(A):
         # Short circuit, we know the zero matrix is extreme.
         return True
 
-    if not is_symmetric_psd(A):
+    if not A.is_positive_semidefinite():
         return False
 
     # Step 1.5, appeal to Theorem 3.1 in reference #1 to short
@@ -383,7 +382,7 @@ def is_extreme_doubly_nonnegative(A):
     # can't compute the dimension of a set of matrices anyway, so we
     # convert them all to vectors and just ask for the dimension of the
     # resulting vector space.
-    (phi, phi_inverse) = basis_representation(A.matrix_space())
+    (phi, phi_inverse) = basis_repr(A.matrix_space())
     vectors = map(phi,spanning_set)
 
     V = span(vectors, A.base_ring())