diff --git a/ctmrg.py b/ctmrg.py
index ee5071350e0f3ed2dbd7e9b8ddf45486ae4f5a5b..b752fc4fd5c9b5fba3c6916072b12cb16d0a6d1f 100644
--- a/ctmrg.py
+++ b/ctmrg.py
@@ -1,13 +1,10 @@
 import torch
 from itertools import permutations
 
-def ctmrg(Tin, d, Dcut, no_iter):
+def ctmrg(T, d, Dcut, no_iter):
     
     #symmetrize
-    T = torch.zeros_like(Tin)
-    for i in permutations([0, 1, 2, 3], 4):
-        T = T + Tin.permute(i)
-    T = T/24. 
+    T = (T + T.permute(3, 1, 2, 0) + T.permute(0, 2, 1, 3) + T.permute(2, 3, 0, 1) + T.permute(1, 0, 3, 2))/5. 
     
     lnZ = 0.0
     truncation_error = 0.0
@@ -55,7 +52,7 @@ def ctmrg(Tin, d, Dcut, no_iter):
 
     Z3 = torch.einsum('ab,bc,cd,da', (C,C,C,C))
     Z2 = torch.einsum('ab,bcd,de,fa,gcf,ge',(C,E,C,C,E,C))
-    print ('Z1, Z2, Z3:', Z1.item(), Z2.item(), Z3.item())
+    print ('    Z1, Z2, Z3:', Z1.item(), Z2.item(), Z3.item())
     lnZ += torch.log(Z1.abs()) + torch.log(Z3.abs()) - 2.*torch.log(Z2.abs())
 
     return lnZ, truncation_error
diff --git a/dimer_covering.py b/dimer_covering.py
index bc59bc9f5f63bcc205efa5f7a1da57330ecf8e16..5159f943b53c3aeb7d702c545038de102cc8e980 100644
--- a/dimer_covering.py
+++ b/dimer_covering.py
@@ -35,9 +35,7 @@ if __name__=='__main__':
 
     B = 0.01* torch.randn(d, D, D, D, D, dtype=dtype, device=device)
     #symmetrize
-    A = torch.zeros_like(B)
-    for i in permutations([1, 2, 3, 4], 4):
-        A = A + B.permute([0]+list(i))/24. 
+    A = (B + B.permute(0, 4, 2, 3, 1) + B.permute(0, 1, 3, 2, 4) + B.permute(0, 3, 4, 1, 2) + B.permute(0, 2, 1, 4, 3))/5. 
     A = A.view(d, D**4)
     A = torch.nn.Parameter(A)
 
diff --git a/ising.py b/ising.py
index 0223199c06d8bd5258dc3ef5bc9fb6059af037a8..fd2bd0dc22c5be0b90cac73b21f64f1d2fb11169 100644
--- a/ising.py
+++ b/ising.py
@@ -4,8 +4,9 @@ import torch
 torch.set_num_threads(4)
 torch.manual_seed(42)
 
-from hotrg2 import hotrg as contraction
+#from hotrg2 import hotrg as contraction
 #from trg import levin_nave_trg as contraction
+from ctmrg import ctmrg as contraction
 
 if __name__=='__main__':
     import time
@@ -29,7 +30,11 @@ if __name__=='__main__':
     Niter = args.Niter
     beta = torch.tensor([args.beta], dtype=dtype, device=device).requires_grad_()
 
-    A = torch.nn.Parameter(0.01* torch.randn(d, D**4, dtype=dtype, device=device, requires_grad=True))
+    B = 0.01* torch.randn(d, D, D, D, D, dtype=dtype, device=device)
+    #symmetrize
+    A = (B + B.permute(0, 4, 2, 3, 1) + B.permute(0, 1, 3, 2, 4) + B.permute(0, 3, 4, 1, 2) + B.permute(0, 2, 1, 4, 3))/5. 
+    A = A.view(d, D**4)
+    A = torch.nn.Parameter(A)
 
     #3D Ising
     c = torch.sqrt(torch.cosh(beta))
@@ -38,9 +43,10 @@ if __name__=='__main__':
     T = torch.einsum('ai,aj,ak,al,am,an->ijklmn', (M, M, M, M, M, M))
     T = T.view(d, d**4, d)
 
-    optimizer = torch.optim.LBFGS([A], max_iter=50)
+    optimizer = torch.optim.LBFGS([A], max_iter=20)
     
     def closure():
+        if beta.grad is not None: beta.grad.zero_()
         optimizer.zero_grad()
 
         T1 = torch.einsum('xa,xby,yc' , (A,T,A)).view(D,D,D,D, d,d,d,d, D,D,D,D).permute(0,4,8, 1,5,9, 2,6,10, 3,7,11).contiguous().view(D**2*d, D**2*d, D**2*d, D**2*d)
@@ -50,15 +56,16 @@ if __name__=='__main__':
         t0=time.time()
         lnT1, error1 = contraction(T1, D**2*d, Dcut, Niter)
         lnT2, error2 = contraction(T2, D**2, Dcut, Niter)
-        loss = (-lnT1 + lnT2)/2**Niter  # loss = -lnZ of Ising
-        print ('contraction done {:.3f}s'.format(time.time()-t0))
-        print ('truncation error', error1.item(), error2.item())
+        loss = (-lnT1 + lnT2)  # loss = -lnZ of Ising
+        print ('    contraction done {:.3f}s'.format(time.time()-t0))
+        print ('    loss, error', loss.item(), error1.item(), error2.item())
 
         t0=time.time()
         loss.backward(retain_graph=True)
-        print ('backward done {:.3f}s'.format(time.time()-t0))
-        print ('free energy, energy', loss.item(), beta.grad.item()) # En = -d lnZ / d beta
-        beta.grad.zero_()
+        print ('    backward done {:.3f}s'.format(time.time()-t0))
         return loss
 
-    optimizer.step(closure)
+    for epoch in range(100):
+        loss = optimizer.step(closure)
+        En = beta.grad.item() # En = -d lnZ / d beta
+        print ('epoch, free energy, energy', epoch, loss.item(), En)