better starting point in CTMRG, symmetrize T

taurg_sweep_3d.py

@@ -5,18 +5,18 @@ torch.manual_seed(42)
 torch.set_num_threads(1)
 from torch.utils.checkpoint import checkpoint
 
-from tensornets import CTMRG, TRG 
+from tensornets import CTMRG
 
 def symmetrize(A):
     '''
-    A(phy, up, left, down, right)
+    A(phy, up, left, down, right, phy')
     left-right, up-down, diagonal symmetrize
     '''
-    Asymm = (A + A.permute(0, 3, 2, 1))/2.           # left-right symmetry
-    Asymm = (Asymm + Asymm.permute(2, 1, 0, 3))/2.   # up-down symmetry
-    Asymm = (Asymm + Asymm.permute(3, 2, 1, 0))/2.   # skew-diagonal symmetry
-    Asymm = (Asymm + Asymm.permute(1, 0, 3, 2))/2.   # diagonal symmetry
-
+    Asymm = (A + A.permute(0, 1, 4, 3, 2, 5))/2.           # left-right symmetry
+    Asymm = (Asymm + Asymm.permute(0, 3, 2, 1, 4, 5))/2.   # up-down symmetry
+    Asymm = (Asymm + Asymm.permute(0, 4, 3, 2, 1, 5))/2.   # skew-diagonal symmetry
+    Asymm = (Asymm + Asymm.permute(0, 2, 1, 4, 3, 5))/2.   # diagonal symmetry
+ 
     return Asymm
 
 def ctmrg(T, chi, nctmrg):
@@ -79,8 +79,9 @@ class Ising(torch.nn.Module):
             else:
                 T = renormalize(*tensors)
 
+            T = symmetrize(T)
+
         T = torch.einsum('xabcdx->abcd', (T)) #trace 
-        #T = symmetrize(T)
         #T = torch.einsum('xabcdy,yefghx->aebfcgdh', (T, T)).contiguous().view(self.D**2, self.D**2, self.D**2, self.D**2)
 
         f = T.abs().max()

tensornets/ctmrg.py

@@ -59,8 +59,8 @@ def CTMRG(T, chi, max_iter, use_checkpoint=False):
     threshold = 1E-12 if T.dtype is torch.float64 else 1E-6 # ctmrg convergence threshold
 
     # C(down, right), E(up,right,down)
-    C = torch.rand(chi, chi, dtype=T.dtype, device=T.device) #T.sum((0,1))  #
-    E = torch.rand(chi, T.shape[0], chi, dtype=T.dtype, device=T.device)#T.sum(1).permute(0,2,1)
+    C = T.sum((0,1))  #
+    E = T.sum(1).permute(0,2,1)
 
     truncation_error = 0.0
     sold = torch.zeros(chi, dtype=T.dtype, device=T.device)