Advanced Computing Platform for Theoretical Physics

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Commit a144be5b by Lei Wang

### 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)`
parent fc796aba
 ... ... @@ -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 ... ...
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