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Add :FixedIter and :LeftMoves safeguard, pdate tests
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@pbrehmer
pbrehmer committed Jul 11, 2024
1 parent 584380c commit 3de5685
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Showing 4 changed files with 101 additions and 35 deletions.
2 changes: 1 addition & 1 deletion src/PEPSKit.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ module Defaults
const fpgrad_tol = 1e-6
end

export SVDAdjoint, IterSVD, FixedSVD, NonTruncSVDAdjoint
export SVDAdjoint, IterSVD, NonTruncSVDAdjoint
export FixedSpaceTruncation, ProjectorAlg, CTMRG, CTMRGEnv, correlation_length
export LocalOperator
export expectation_value, costfun
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66 changes: 53 additions & 13 deletions src/algorithms/peps_opt.jl
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Expand Up @@ -2,9 +2,15 @@ abstract type GradMode{F} end

"""
struct GeomSum(; maxiter=Defaults.fpgrad_maxiter, tol=Defaults.fpgrad_tol,
verbosity=0, iterscheme=:FixedIter) <: GradMode
verbosity=0, iterscheme=:FixedIter) <: GradMode{iterscheme}
Gradient mode for CTMRG using explicit evaluation of the geometric sum.
With `iterscheme` the style of CTMRG iteration which is being differentiated can be chosen.
If set to `:FixedIter`, the differentiated CTMRG iteration is assumed to have a pre-computed
SVD of the environments with a fixed set of gauges. Alternatively, if set to `:DiffGauge`,
the differentiated iteration consists of a CTMRG iteration and a subsequent gauge fixing step,
such that `gauge_fix` will also be differentiated everytime a CTMRG derivative is computed.
"""
struct GeomSum{F} <: GradMode{F}
maxiter::Int
Expand All @@ -22,9 +28,15 @@ end

"""
struct ManualIter(; maxiter=Defaults.fpgrad_maxiter, tol=Defaults.fpgrad_tol,
verbosity=0, iterscheme=:FixedIter) <: GradMode
verbosity=0, iterscheme=:FixedIter) <: GradMode{iterscheme}
Gradient mode for CTMRG using manual iteration to solve the linear problem.
With `iterscheme` the style of CTMRG iteration which is being differentiated can be chosen.
If set to `:FixedIter`, the differentiated CTMRG iteration is assumed to have a pre-computed
SVD of the environments with a fixed set of gauges. Alternatively, if set to `:DiffGauge`,
the differentiated iteration consists of a CTMRG iteration and a subsequent gauge fixing step,
such that `gauge_fix` will also be differentiated everytime a CTMRG derivative is computed.
"""
struct ManualIter{F} <: GradMode{F}
maxiter::Int
Expand All @@ -41,10 +53,16 @@ function ManualIter(;
end

"""
struct LinSolver(; solver=KrylovKit.GMRES(), iterscheme=:FixedIter) <: GradMode
struct LinSolver(; solver=KrylovKit.GMRES(), iterscheme=:FixedIter) <: GradMode{iterscheme}
Gradient mode wrapper around `KrylovKit.LinearSolver` for solving the gradient linear
problem using iterative solvers.
With `iterscheme` the style of CTMRG iteration which is being differentiated can be chosen.
If set to `:FixedIter`, the differentiated CTMRG iteration is assumed to have a pre-computed
SVD of the environments with a fixed set of gauges. Alternatively, if set to `:DiffGauge`,
the differentiated iteration consists of a CTMRG iteration and a subsequent gauge fixing step,
such that `gauge_fix` will also be differentiated everytime a CTMRG derivative is computed.
"""
struct LinSolver{F} <: GradMode{F}
solver::KrylovKit.LinearSolver
Expand All @@ -57,8 +75,8 @@ function LinSolver(;
end

"""
PEPSOptimize{G}(; boundary_alg = CTMRG(), optimizer::OptimKit.OptimizationAlgorithm = LBFGS()
reuse_env::Bool = true, gradient_alg::G, verbosity::Int = 0)
PEPSOptimize{G}(; boundary_alg=CTMRG(), optimizer::OptimKit.OptimizationAlgorithm=LBFGS()
reuse_env::Bool=true, gradient_alg::G=LinSolver(), verbosity::Int=0)
Algorithm struct that represent PEPS ground-state optimization using AD.
Set the algorithm to contract the infinite PEPS in `boundary_alg`;
Expand All @@ -69,14 +87,36 @@ step by setting `reuse_env` to true. Otherwise a random environment is used at e
step. The CTMRG gradient itself is computed using the `gradient_alg` algorithm.
Different levels of output verbosity can be activated using `verbosity` (0, 1 or 2).
"""
@kwdef struct PEPSOptimize{G}
boundary_alg::CTMRG = CTMRG() # Algorithm to find boundary environment
optimizer::OptimKit.OptimizationAlgorithm = LBFGS(
4; maxiter=100, gradtol=1e-4, verbosity=2
)
reuse_env::Bool = true # Reuse environment of previous optimization as initial guess for next
gradient_alg::G = GeomSum() # Algorithm to solve gradient linear problem
verbosity::Int = 0
struct PEPSOptimize{G}
boundary_alg::CTMRG
optimizer::OptimKit.OptimizationAlgorithm
reuse_env::Bool
gradient_alg::G
verbosity::Int

function PEPSOptimize( # Inner constructor to prohibit illegal setting combinations
boundary_alg::CTMRG{S},
optimizer,
reuse_env,
gradient_alg::G,
verbosity,
) where {S,G}
if gradient_alg isa GradMode
if S == :LeftMoves && G.parameters[1] == :FixedIter
throw(ArgumentError(":LeftMoves and :FixedIter are not compatible"))
end
end
return new{G}(boundary_alg, optimizer, reuse_env, gradient_alg, verbosity)
end
end
function PEPSOptimize(;
boundary_alg=CTMRG(),
optimizer=LBFGS(4; maxiter=100, gradtol=1e-4, verbosity=2),
reuse_env=true,
gradient_alg=LinSolver(),
verbosity=0,
)
return PEPSOptimize(boundary_alg, optimizer, reuse_env, gradient_alg, verbosity)
end

"""
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63 changes: 44 additions & 19 deletions test/ctmrg/fixedsvd.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@ using Random
using TensorKit
using PEPSKit
using PEPSKit:
FixedSVD,
ctmrg_iter,
gauge_fix,
fix_relative_phases,
Expand All @@ -14,22 +15,46 @@ using PEPSKit:
χenv = 16
ctm_alg = CTMRG(; tol=1e-12, miniter=4, maxiter=100, verbosity=1, ctmrgscheme=:AllSides)

# initialize states
Random.seed!(91283219347)
psi = InfinitePEPS(2, χbond)
env_conv1 = leading_boundary(CTMRGEnv(psi; Venv=ComplexSpace(χenv)), psi, ctm_alg)

# do extra iteration to get SVD
env_conv2, info = ctmrg_iter(psi, env_conv1, ctm_alg)
env_fix, signs = gauge_fix(env_conv1, env_conv2)
@test check_elementwise_convergence(env_conv1, env_fix)

# fix gauge of SVD
U_fix, V_fix = fix_relative_phases(info.U, info.V, signs)
svd_alg_fix = SVDAdjoint(; fwd_alg=FixedSVD(U_fix, info.S, V_fix))
ctm_alg_fix = CTMRG(; svd_alg=svd_alg_fix, trscheme=notrunc(), ctmrgscheme=:AllSides)

# do iteration with FixedSVD
env_fixedsvd, = ctmrg_iter(psi, env_conv1, ctm_alg_fix)
env_fixedsvd = fix_global_phases(env_conv1, env_fixedsvd)
@test check_elementwise_convergence(env_conv1, env_fixedsvd)
@testset "(1, 1) unit cell" begin
# initialize states
Random.seed!(91283219347)
psi = InfinitePEPS(2, χbond)
env_conv1 = leading_boundary(CTMRGEnv(psi; Venv=ComplexSpace(χenv)), psi, ctm_alg)

# do extra iteration to get SVD
env_conv2, info = ctmrg_iter(psi, env_conv1, ctm_alg)
env_fix, signs = gauge_fix(env_conv1, env_conv2)
@test check_elementwise_convergence(env_conv1, env_fix)

# fix gauge of SVD
U_fix, V_fix = fix_relative_phases(info.U, info.V, signs)
svd_alg_fix = SVDAdjoint(; fwd_alg=FixedSVD(U_fix, info.S, V_fix))
ctm_alg_fix = CTMRG(; svd_alg=svd_alg_fix, trscheme=notrunc(), ctmrgscheme=:AllSides)

# do iteration with FixedSVD
env_fixedsvd, = ctmrg_iter(psi, env_conv1, ctm_alg_fix)
env_fixedsvd = fix_global_phases(env_conv1, env_fixedsvd)
@test check_elementwise_convergence(env_conv1, env_fixedsvd)
end

@testset "(3, 4) unit cell" begin
# initialize states
Random.seed!(91283219347)
psi = InfinitePEPS(2, χbond; unitcell=(3, 4))
env_conv1 = leading_boundary(CTMRGEnv(psi; Venv=ComplexSpace(χenv)), psi, ctm_alg)

# do extra iteration to get SVD
env_conv2, info = ctmrg_iter(psi, env_conv1, ctm_alg)
env_fix, signs = gauge_fix(env_conv1, env_conv2)
@test check_elementwise_convergence(env_conv1, env_fix)

# fix gauge of SVD
U_fix, V_fix = fix_relative_phases(info.U, info.V, signs)
svd_alg_fix = SVDAdjoint(; fwd_alg=FixedSVD(U_fix, info.S, V_fix))
ctm_alg_fix = CTMRG(; svd_alg=svd_alg_fix, trscheme=notrunc(), ctmrgscheme=:AllSides)

# do iteration with FixedSVD
env_fixedsvd, = ctmrg_iter(psi, env_conv1, ctm_alg_fix)
env_fixedsvd = fix_global_phases(env_conv1, env_fixedsvd)
@test check_elementwise_convergence(env_conv1, env_fixedsvd)
end
5 changes: 3 additions & 2 deletions test/heisenberg.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -14,12 +14,13 @@ ctm_alg = CTMRG(;
maxiter=100,
verbosity=1,
trscheme=truncdim(χenv),
svd_alg=SVDAdjoint(; fwd_alg=TensorKit.SVD(), rrule_alg=GMRES(; tol=1e-12)),
svd_alg=SVDAdjoint(; fwd_alg=TensorKit.SVD(), rrule_alg=GMRES(; tol=1e-10)),
ctmrgscheme=:AllSides
)
opt_alg = PEPSOptimize(;
boundary_alg=ctm_alg,
optimizer=LBFGS(4; maxiter=100, gradtol=1e-3, verbosity=2),
gradient_alg=LinSolver(; solver=GMRES(; tol=1e-6, maxiter=100)),
gradient_alg=LinSolver(; solver=GMRES(; tol=1e-6, maxiter=100), iterscheme=:FixedIter),
reuse_env=true,
verbosity=2,
)
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