# Is slicing matrix a view or copy in cvxopt?

It is known that Numpy basic matrix slicing will generate a view, whereas advanced slicing a copy. Is this true in cvxopt? I tried

from cvxopt import spmatrix
import numpy as np
A = spmatrix([2,-1,2,-2,1,4,3], [1,2,0,2,3,2,0], [0,0,1,1,2,3,4])
A_view = A[0:3,0:3]           # basic slicing
A_cp = A[[0,1,2],[0,1,2]]     # advanced slicing
np.may_share_memory(A,A_view) # returned false
np.may_share_memory(A,A_cp)   # returned false


That is a sparse matrix. Numpy doesn't have sparse matrices, and I think neither scipy.sparse nor cvxopt use memoryviews for sparse matrices.
Even for dense matrices it appears that cvxopt uses a low level Python API instead of something like Cython. I don't see any references to memoryview interfaces so perhaps even dense matrices in cvxopt use copying instead of views.