symmetrize T in vmps

dimer_covering.py

@@ -62,8 +62,8 @@ if __name__=='__main__':
         T2 = (A.t()@A).view(D, D, D, D, D, D, D, D).permute(0,4, 1,5, 2,6, 3,7).contiguous().view(D**2, D**2, D**2, D**2)
         t0=time.time()
 
-        lnT, error1 = contraction(T1, D**2*d, Dcut, Niter)
-        lnZ, error2 = contraction(T2, D**2, Dcut, Niter)
+        lnT, _ = contraction(T1, D**2*d, Dcut, Niter)
+        lnZ, _ = contraction(T2, D**2, Dcut, Niter)
         loss = (-lnT + lnZ)
         print ('    contraction done {:.3f}s'.format(time.time()-t0))
         print ('    total loss', loss.item())

vmps.py

@@ -28,11 +28,15 @@ def mpsrg(A, T):
     #    C = torch.mm(C, C)
     return -lnZ1 + lnZ2 
 
-def vmps(T, d, D, no_iter, Nepochs=5):
+def vmps(T, d, D, no_iter, Nepochs=50):
+
+    #symmetrize
+    T = (T + T.permute(3, 1, 2, 0))/2. #left-right
+    T = (T + T.permute(0, 2, 1, 3))/2. #up-down
     
     A = torch.nn.Parameter(0.01*torch.randn(D, d, D, dtype=T.dtype, device=T.device))
 
-    optimizer = torch.optim.LBFGS([A], max_iter=10)
+    optimizer = torch.optim.LBFGS([A], max_iter=20)
     def closure():
         optimizer.zero_grad()
         #print ('einsum', time.time()- t0)
@@ -40,7 +44,7 @@ def vmps(T, d, D, no_iter, Nepochs=5):
         #t0 = time.time()
         loss = mpsrg(A, T.detach()) # loss = -lnZ , here we optimize over A 
         #print ('mpsrg', time.time()- t0)
-        print ('            loss', loss.item())
+        #print ('            loss', loss.item())
         #t0 = time.time()
         loss.backward(retain_graph=False)
         #print ('backward', time.time()- t0)
@@ -48,7 +52,7 @@ def vmps(T, d, D, no_iter, Nepochs=5):
 
     for epoch in range(Nepochs):
         loss = optimizer.step(closure)
-        print ('        epoch, free energy', epoch, loss.item())
+        #print ('        epoch, free energy', epoch, loss.item())
 
     return -mpsrg(A.detach(), T), None # pass lnZ out, we need to optimize over T