la.print_diagram()
lb.print_diagram()
#print(la)
#print(lb)
## Evov:
Elast = 0
for i in range(10000):
A.set_labels([-1,0,-2])
B.set_labels([-3,1,-4])
la.set_labels([-2,-3])
lb.set_labels([-4,-5])
## contract all
X = cytnx.Contract(cytnx.Contract(A,la),cytnx.Contract(B,lb))
#X.print_diagram()
lb.set_label(1,new_label=-1)
X = cytnx.Contract(lb,X)
## X =
# (0) (1)
# | |
# (-4) --lb-A-la-B-lb-- (-5)
#
#X.print_diagram()
Xt = X.clone()
## calculate norm and energy for this step
# Note that X,Xt contract will result a rank-0 tensor, which can use item() toget element
# ----- -----A[0]--- -----A[1]---
# | | | | |
# ML---- LR[0]--M----- LR[1]--M----- ......
# | | | | |
# ----- -----A*[0]-- -----A*[1]--
#
#
# L_AMAH.net file is used to contract the LR[i]
##>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
LR = [None for i in range(Nsites + 1)]
LR[0] = L0
LR[-1] = R0
for p in range(Nsites - 1):
s, A[p], vt = cytnx.linalg.Svd(A[p])
A[p + 1] = cytnx.Contract(cytnx.Contract(s, vt), A[p + 1])
#A[p+1].print_diagram()
#A[p].print_diagram()
anet = cytnx.Network("L_AMAH.net")
anet.PutUniTensors(["L", "A", "A_Conj", "M"],
[LR[p], A[p], A[p].Conj(), M],
is_clone=False)
LR[p + 1] = anet.Launch(optimal=True)
_, A[-1] = cytnx.linalg.Svd(A[-1], is_U=True, is_vT=False) ## last one.
## DMRG sweep
## Now we are ready for sweeping procedure!
##>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
Ekeep = []
prevlnZ = 0;
for i in range(Maxiter):
print(i)
## LR:
#
# 1 4
# | |
# 0--cT--2 2--cT2--5
# | |
# 3 6
#
cT2 = cT.clone()
cT2.set_labels([2,4,5,6])
cT = cytnx.Contract(cT,cT2)
## Now, let's check the dimension growth onto a point where truncation is needed:
if(cT.shape()[1]*cT.shape()[3]>chi):
# * if combined bond dimension > chi then:
# 1) Do Hosvd get only U and D, with it's Ls matrices.
cT.permute_([1,4,3,6,0,5],by_label=True)
U,D,Lu,Ld=cytnx.linalg.Hosvd(cT,[2,2],is_core=False,is_Ls=True)
# 2) Using Ls matrix to determine if U is used to truncate or D is used to truncate
if(Lu.get_block_()[chi:].Norm().item() < Ld.get_block_()[chi:].Norm().item()):
U, D = D,U
## truncate, and permute back to the original form:
# chi
# |
## gates:
eHu = cy.linalg.ExpH(Hu, -tau)
eHd = cy.linalg.ExpH(Hd, -tau)
## iterator:
Eup_old, Edown_old = 0, 0
cov = False
for i in range(maxit):
## first do up >>>>>>>>>>>>>>>>>>
Nup = cy.Network("up.net")
Nup.PutUniTensors(["A", "B", "C", "L2A", "L2B", "L2C", "eHu", "s1"],
[A, B, C, Ls2[0], Ls2[1], Ls2[2], eHu, s1])
T = Nup.Launch(True)
Nrm = cy.Contract(T, T).item()
T /= np.sqrt(Nrm)
#normalize for numerical stability.
A, B, C, s1, Ls1[0], Ls1[1], Ls1[2] = cy.linalg.Hosvd(
T, [2, 2, 2], True, True, [D, D, D])
## de-contract Ls'
Ls2[0].set_labels([-10, A.labels()[0]])
Ls2[0] = 1. / Ls2[0]
Ls2[1].set_labels([-11, B.labels()[0]])
Ls2[1] = 1. / Ls2[1]
Ls2[2].set_labels([-12, C.labels()[0]])
Ls2[2] = 1. / Ls2[2]
A = cy.Contract(Ls2[0], A)
B = cy.Contract(Ls2[1], B)
C = cy.Contract(Ls2[2], C)
# 1) Absorb s into A and put it in shape
# -------------
# / \
# virt ____| |____ phys
# | |
# | |____ virt
# \ /
# -------------
# 2) Do Svd, and get the growing n+1 right site, and the right singular values sR
# [Note] we don't need U!!
#
# --A--s --B-- => --sR--A' --B--
# | | | |
#
A.set_rowrank(1)
sR, _, A = cytnx.linalg.Svd(cytnx.Contract(A, s1))
## rotate right
# 1) Absorb s into B and put it in shape
# -------------
# / \
# virt ____| |____ virt
# | |
# phys ____| |
# \ /
# -------------
# 2) Do Svd, and get the growing n+1 left site, and the left singular values sL
# [Note] we don't need vT!!
#
# --A-- s--B-- => --A-- B'--sL
# | | | |
