correct symmetry

ctmrg.py

@@ -4,7 +4,10 @@ from itertools import permutations
 def ctmrg(T, d, Dcut, no_iter):
     
     #symmetrize
-    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. 
+    T = (T + T.permute(3, 1, 2, 0))/2. 
+    T = (T + T.permute(0, 2, 1, 3))/2. 
+    T = (T + T.permute(2, 3, 0, 1))/2. 
+    T = (T + T.permute(1, 0, 3, 2))/2. 
     
     lnZ = 0.0
     truncation_error = 0.0

dimer_covering.py

@@ -35,9 +35,11 @@ if __name__=='__main__':
 
     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)
+    B = (B + B.permute(0, 4, 2, 3, 1))/2. 
+    B = (B + B.permute(0, 1, 3, 2, 4))/2. 
+    B = (B + B.permute(0, 3, 4, 1, 2))/2. 
+    B = (B + B.permute(0, 2, 1, 4, 3))/2. 
+    A = torch.nn.Parameter(B.view(d, D**4))
 
     #dimer covering
     T = torch.zeros(d, d, d, d, d, d, dtype=dtype, device=device)
@@ -63,12 +65,12 @@ if __name__=='__main__':
         lnT, error1 = contraction(T1, D**2*d, Dcut, Niter)
         lnZ, error2 = contraction(T2, D**2, Dcut, Niter)
         loss = (-lnT + lnZ)
-        print ('contraction done {:.3f}s'.format(time.time()-t0))
-        print ('loss, error', loss.item(), error1.item(), error2.item())
+        print ('    contraction done {:.3f}s'.format(time.time()-t0))
+        print ('    loss, error', loss.item(), error1.item(), error2.item())
 
         t0=time.time()
         loss.backward()
-        print ('backward done {:.3f}s'.format(time.time()-t0))
+        print ('    backward done {:.3f}s'.format(time.time()-t0))
         return loss
     
     for epoch in range(100):

ising.py

@@ -15,7 +15,8 @@ if __name__=='__main__':
     parser.add_argument("-D", type=int, default=2, help="D")
     parser.add_argument("-Dcut", type=int, default=20, help="Dcut")
     parser.add_argument("-beta", type=float, default=0.22, help="beta")
-    parser.add_argument("-Niter", type=int, default=20, help="Niter")
+    parser.add_argument("-Niter", type=int, default=32, help="Niter")
+    parser.add_argument("-Nepochs", type=int, default=100, help="Nepochs")
 
     parser.add_argument("-float32", action='store_true', help="use float32")
     parser.add_argument("-cuda", type=int, default=-1, help="use GPU")
@@ -32,9 +33,11 @@ if __name__=='__main__':
 
     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)
+    B = (B + B.permute(0, 4, 2, 3, 1))/2. 
+    B = (B + B.permute(0, 1, 3, 2, 4))/2. 
+    B = (B + B.permute(0, 3, 4, 1, 2))/2. 
+    B = (B + B.permute(0, 2, 1, 4, 3))/2. 
+    A = torch.nn.Parameter(B.view(d, D**4))
 
     #3D Ising
     c = torch.sqrt(torch.cosh(beta))
@@ -65,7 +68,7 @@ if __name__=='__main__':
         print ('    backward done {:.3f}s'.format(time.time()-t0))
         return loss
 
-    for epoch in range(100):
+    for epoch in range(args.Nepochs):
         loss = optimizer.step(closure)
         En = beta.grad.item() # En = -d lnZ / d beta
         print ('epoch, free energy, energy', epoch, loss.item(), En)