Advanced Computing Platform for Theoretical Physics

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Commit 400ab95a authored by Lei Wang's avatar Lei Wang
Browse files

correct symmetry

parent 5d8e8921
......@@ -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
......
......@@ -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):
......
......@@ -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)
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