Skip to content

Faster Sloop #205

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
dartsushi wants to merge 5 commits into from
Closed

Faster Sloop #205

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
8bd563d
Faster optimization for SloopTNR
dartsushi Aug 19, 2026
2d6a4f4
Update gradient tolerance and max iterations in SLoopTNR
dartsushi Aug 19, 2026
f24c0a7
Got rid of Zygote
dartsushi Aug 19, 2026
ffd49f4
fptensor
dartsushi Aug 19, 2026
48d3d0a
Merge pull request #1 from dartsushi/fptensor
dartsushi Aug 19, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 4 additions & 1 deletion src/TNRKit.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ using MatrixAlgebraKit
using MatrixAlgebraKit: TruncationStrategy
using LoggingExtras, Printf
using KrylovKit
using OptimKit, Zygote
using OptimKit
using DocStringExtensions
using SpecialFunctions
using FastGaussQuadrature
Expand Down Expand Up @@ -138,6 +138,9 @@ include("utility/transfer_matrix.jl")
include("utility/cft.jl")
export CFTData, extract_tau_and_c

include("utility/fixed_point_tensor.jl")
export fixed_point_tensor, fixed_point_tensor_4x4

include("utility/gs_degeneracy.jl")
export ground_state_degeneracy, gu_wen_ratio

Expand Down
81 changes: 61 additions & 20 deletions src/schemes/symmetric_looptnr.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@ mutable struct SLoopTNR{E, S, TT <: AbstractTensorMap{E, S, 4, 0}} <: TNRScheme{

"Gradient optimization algorithm"
gradalg::OptimKit.LBFGS
function SLoopTNR(T::TT; gradalg = LBFGS(10; verbosity = 0, gradtol = 6.0e-7, maxiter = 40000)) where {E, S, TT <: AbstractTensorMap{E, S, 4, 0}}
function SLoopTNR(T::TT; gradalg = LBFGS(10; verbosity = 0, gradtol = 1.0e-6, maxiter = 200)) where {E, S, TT <: AbstractTensorMap{E, S, 4, 0}}
return new{E, S, TT}(T, gradalg)
end
end
Expand All @@ -48,52 +48,93 @@ classical_ising_inv() = classical_ising_inv(ising_βc)

########## utility functions ##########
function trnorm_2x2(T)
@tensor TT[-1 -2; -3 -4] := T[1 -1 2 -3] * conj(T[1 -2 2 -4])
@tensoropt TT[-1 -2; -3 -4] := T[1 -1 2 -3] * conj(T[1 -2 2 -4])
return sqrt(TTtoNorm(TT))
end

########## Cost function ##########
function StoSS(S)
V = domain(S)[1]
b = isomorphism(V, V')
@tensor SS[-1 -2 -3 -4] := S[-1 -2; 1] * S[-3 -4; 2] * b[1 2]
@tensoropt SS[-1 -2 -3 -4] := S[-1 -2; 1] * S[-3 -4; 2] * b[1 2]
return SS
end

function TTtoNorm(TT)
V = domain(TT)
b = isomorphism(V[1] ⊗ V[2], V[1]' ⊗ V[2]')
TTb = TT * b
@tensor T4[-1 -2; -3 -4] := TT[-1 -2; 1 2] * TTb[-3 -4; 1 2]
@tensoropt T4[-1 -2; -3 -4] := TT[-1 -2; 1 2] * TTb[-3 -4; 1 2]
V = domain(T4)
b = isomorphism(V[1] ⊗ V[2], V[1]' ⊗ V[2]')
T4b = T4 * b
@tensor T8[-1 -2; -3 -4] := T4[-1 -2; 1 2] * T4b[-3 -4; 1 2]
@tensoropt T8[-1 -2; -3 -4] := T4[-1 -2; 1 2] * T4b[-3 -4; 1 2]
V = domain(T8)
b = isomorphism(V[1] ⊗ V[2], V[1]' ⊗ V[2]')
return tr(T8 * b)
end

function cost_looptnr(S, T)
function TtoNorm(T)
@tensoropt TT[-1 -2; -3 -4] := T[1 -1 2 -3] * conj(T[1 -2 2 -4])
return TTtoNorm(TT)
end

function cost_looptnr(S, T, n_TT)
@assert eltype(S) == Float64 "Modification is needed for complex numbers!"
SS = StoSS(S)
@tensor TT[-1 -2; -3 -4] := T[1 -1 2 -3] * conj(T[1 -2 2 -4])
@tensor TSS[-1 -2; -3 -4] := T[1 -1 2 -3] * conj(SS[1 -2 2 -4])
@tensor S4[-1 -2; -3 -4] := SS[1 -1 2 -3] * conj(SS[1 -2 2 -4])
# T
return TTtoNorm(TT) + TTtoNorm(S4) - 2 * TTtoNorm(TSS)

@tensoropt TSS[-1 -2; -3 -4] := T[1 -1 2 -3] * conj(SS[1 -2 2 -4])
@tensoropt S4[-1 -2; -3 -4] := SS[1 -1 2 -3] * conj(SS[1 -2 2 -4])
return n_TT + TTtoNorm(S4) - 2 * TTtoNorm(TSS)
end

########## Gradient Optimization ##########
function fg(f, A)
f_out, g = Zygote.withgradient(f, A)
function loop_environment(TT)
V = domain(TT)
b = isomorphism(V[1] ⊗ V[2], V[1]' ⊗ V[2]')
TTb = TT * b
@tensoropt T4[-1 -2; -3 -4] := TT[-1 -2; 1 2] * TTb[-3 -4; 1 2]
return T4 * b * TT
end

function StoSS_pullback(S, gSS)
V = domain(S)[1]
b = isomorphism(V, V')
Sb = S * b
@tensoropt gS[-1 -2; -3] := gSS[-1 -2 1 2] * conj(Sb[1 2; -3])
return 2 * gS
end

function gradient_looptnr(S, T, SS, TSS, S4)
# One of the eight equal contributions from the S-S overlap.
env_SS = loop_environment(S4)
@tensoropt gSS[-1 -2 -3 -4] := SS[-1 1 -3 2] * env_SS[-2 1; -4 2]
gS_SS = StoSS_pullback(S, gSS)

# One of the four equal contributions from the T-S overlap.
env_TS = loop_environment(TSS)
env_TS_transposed = permute(env_TS, ((2, 1), (4, 3)))
@tensoropt gTS_dual[-1 -2 -3 -4] := conj(T[-1 1 -3 2]) * env_TS_transposed[-2 1; -4 2]
gTS = flip(gTS_dual, (1, 2, 3, 4))
gS_TS = StoSS_pullback(S, gTS)

return 8 * gS_SS - 2 * 4 * gS_TS
end

function cost_looptnr_fg(S, T, n_TT)
@assert eltype(S) == Float64 "Modification is needed for complex numbers!"
SS = StoSS(S)

return f_out, g[1]
@tensoropt TSS[-1 -2; -3 -4] := T[1 -1 2 -3] * conj(SS[1 -2 2 -4])
@tensoropt S4[-1 -2; -3 -4] := SS[1 -1 2 -3] * conj(SS[1 -2 2 -4])
cost = n_TT + TTtoNorm(S4) - 2 * TTtoNorm(TSS)
grad = gradient_looptnr(S, T, SS, TSS, S4)
return cost, grad
end

function optimize_S(scheme, S)
opt_fun(x) = cost_looptnr(x, scheme.T)
opt_fg(x) = fg(opt_fun, x)
n_TT = TtoNorm(scheme.T)
opt_fg(x) = cost_looptnr_fg(x, scheme.T, n_TT)
Sopt, fx, gx, numfg, normgradhistory = optimize(
opt_fg, S,
scheme.gradalg
Expand Down Expand Up @@ -126,7 +167,7 @@ end

function entanglement_filtering(T; trunc = trunctol(atol = 1.0e-12))
entanglement_function(steps, data) = abs(data[end])
entanglement_criterion = maxiter(100) & convcrit(1.0e-12, entanglement_function)
entanglement_criterion = maxiter(200) & convcrit(1.0e-12, entanglement_function)

psi_center = Ψ_center(T)
psi_corner = Ψ_corner(T)
Expand All @@ -146,7 +187,7 @@ function entanglement_filtering(T; trunc = trunctol(atol = 1.0e-12))
P_top = PL_list[3]
P_left = PL_list[3]

@tensor T_new[-1 -2 -3 -4] := T[1 2 3 4] * P_left[-1; 1] * P_bottom[-2; 2] *
@tensoropt T_new[-1 -2 -3 -4] := T[1 2 3 4] * P_left[-1; 1] * P_bottom[-2; 2] *
P_top[-3; 3] * P_right[-4; 4]
return T_new
end
Expand All @@ -168,14 +209,14 @@ function ef_oneloop(T, trunc::TruncationStrategy)
criterion, trunc
)
i = 1
@tensor S[-2 -1; -3] := ΨB[i][-1; -2 2] * PRs[mod(i, 8) + 1][2; -3]
@tensoropt S[-2 -1; -3] := ΨB[i][-1; -2 2] * PRs[mod(i, 8) + 1][2; -3]
return S
end

########## Updating the tensor ##########
function combine_4S(S)
Sflip = flip(S, (1, 2))
@tensor Tnew[-1 -2 -3 -4] := S[1 2; -4] * Sflip[1 4; -3] * S[3 4; -1] * Sflip[3 2; -2]
@tensoropt Tnew[-1 -2 -3 -4] := S[1 2; -4] * Sflip[1 4; -3] * S[3 4; -1] * Sflip[3 2; -2]
return Tnew
end

Expand Down
Loading
Loading