even better encapsulation; inner loop optimize A (boundary MPS) , outer loop...

even better encapsulation; inner loop optimize A (boundary MPS) , outer loop optimize T (boundary PEPS)

vmps.py

@@ -38,7 +38,7 @@ def vmps(T, d, D, no_iter, Nepochs=5):
         #print ('einsum', time.time()- t0)
         #print ((B-B.t()).abs().sum(), (C-C.t()).abs().sum())
         #t0 = time.time()
-        loss = mpsrg(A, T) # -lnZ
+        loss = mpsrg(A, T.detach()) # loss = -lnZ , here we optimize over A 
         #print ('mpsrg', time.time()- t0)
         print ('            loss', loss.item())
         #t0 = time.time()
@@ -50,7 +50,7 @@ def vmps(T, d, D, no_iter, Nepochs=5):
         loss = optimizer.step(closure)
         print ('        epoch, free energy', epoch, loss.item())
 
-    return loss, A
+    return -mpsrg(A.detach(), T), None # pass lnZ out, we need to optimize over T
 
 if __name__=='__main__':
     import time 
@@ -75,9 +75,8 @@ if __name__=='__main__':
     T = torch.einsum('ai,aj,ak,al->ijkl', (M, M, M, M))
     
     #optimization
-    _, A = vmps(T.detach(), 2, args.Dcut, args.Niter, args.Nepochs)
+    lnZ, _ = vmps(T, 2, args.Dcut, args.Niter, args.Nepochs)
     #recompute lnZ using optimized A
-    lnZ = -mpsrg(A.detach(), T) 
     dlnZ = torch.autograd.grad(lnZ, K, create_graph=True)[0] #  En = -d lnZ / d beta
     print (-dlnZ.item())
     #second order derivative evaluated in this way seems not correct, no effect of envioment