ub, vb = TrialFunction(Vb), TestFunction(Vb)
b = [[0, 0], [0, 0]]
b[0][0] = inner(grad(u), grad(v)) * dx + inner(u, v) * dx
b[0][1] = inner(grad(ub), grad(v)) * dx + inner(ub, v) * dx
b[1][0] = inner(grad(u), grad(vb)) * dx + inner(u, vb) * dx
b[1][1] = inner(grad(ub), grad(vb)) * dx + inner(ub, vb) * dx
BB = ii_assemble(b)
x = Function(V).vector()
x.set_local(np.random.rand(x.local_size()))
y = Function(Vb).vector()
y.set_local(np.random.rand(y.local_size()))
bb = block_vec([x, y])
z_block = BB * bb
# Make into a monolithic matrix
BB_m = ii_convert(BB)
R = ReductionOperator([2], W=[V, Vb])
z = (R.T) * BB_m * (R * bb)
print(z - z_block).norm()
y = BB_m * (R * bb)
print np.linalg.norm(
np.hstack([bi.get_local() for bi in z_block]) - y.get_local())
Vb = VectorFunctionSpace(mesh, 'Bubble', 3)
u, v = TrialFunction(V), TestFunction(V)
ub, vb = TrialFunction(Vb), TestFunction(Vb)
b = [[0, 0], [0, 0]]
b[0][0] = inner(grad(u), grad(v))*dx + inner(u, v)*dx
b[0][1] = inner(grad(ub), grad(v))*dx + inner(ub, v)*dx
b[1][0] = inner(grad(u), grad(vb))*dx + inner(u, vb)*dx
b[1][1] = inner(grad(ub), grad(vb))*dx + inner(ub, vb)*dx
BB = ii_assemble(b)
x = Function(V).vector(); x.set_local(np.random.rand(x.local_size()))
y = Function(Vb).vector(); y.set_local(np.random.rand(y.local_size()))
bb = block_vec([x, y])
z_block = BB*bb
# Make into a monolithic matrix
BB_m = ii_convert(BB)
R = ReductionOperator([2], W=[V, Vb])
z = (R.T)*BB_m*(R*bb)
print (z - z_block).norm()
y = BB_m*(R*bb)
print np.linalg.norm(np.hstack([bi.get_local() for bi in z_block])-y.get_local())
