Extend CTMRGEnv constructor for nontrivial unit cells

README.md

@@ -52,7 +52,7 @@ opt_alg = PEPSOptimize(;
 
 # ground state search
 state = InfinitePEPS(2, D)
-ctm = leading_boundary(CTMRGEnv(state; Venv=ComplexSpace(chi)), state, ctm_alg)
+ctm = leading_boundary(CTMRGEnv(state, ComplexSpace(chi)), state, ctm_alg)
 result = fixedpoint(state, H, opt_alg, ctm)
 
 @show result.E # -0.6625...

docs/src/index.md

@@ -37,7 +37,7 @@ opt_alg = PEPSOptimize(;
 
 # ground state search
 state = InfinitePEPS(2, D)
-ctm = leading_boundary(CTMRGEnv(state; Venv=ComplexSpace(chi)), state, ctm_alg)
+ctm = leading_boundary(CTMRGEnv(state, ComplexSpace(chi)), state, ctm_alg)
 result = fixedpoint(state, H, opt_alg, ctm)
 
 @show result.E # -0.6625...

examples/boundary_mps.jl

@@ -32,7 +32,7 @@ mps, envs, ϵ = leading_boundary(mps, T, VUMPS())
 N = abs(prod(expectation_value(mps, T)))
 
 # This can be compared to the result obtained using the CTMRG algorithm
-ctm = leading_boundary(peps, CTMRG(; verbosity=1), CTMRGEnv(peps; Venv=ComplexSpace(20)))
+ctm = leading_boundary(peps, CTMRG(; verbosity=1), CTMRGEnv(peps, ComplexSpace(20)))
 N´ = abs(norm(peps, ctm))
 
 @show abs(N - N´) / N
@@ -53,7 +53,7 @@ mps2 = PEPSKit.initializeMPS(T2, fill(ComplexSpace(20), 2, 2))
 mps2, envs2, ϵ = leading_boundary(mps2, T2, VUMPS())
 N2 = abs(prod(expectation_value(mps2, T2)))
 
-ctm2 = leading_boundary(peps2, CTMRG(; verbosity=1), CTMRGEnv(peps2; Venv=ComplexSpace(20)))
+ctm2 = leading_boundary(peps2, CTMRG(; verbosity=1), CTMRGEnv(peps2, ComplexSpace(20)))
 N2´ = abs(norm(peps2, ctm2))
 
 @show abs(N2 - N2´) / N2

examples/heisenberg.jl

@@ -27,6 +27,6 @@ opt_alg = PEPSOptimize(;
 # E/N = −0.6694421, which is a QMC estimate from https://arxiv.org/abs/1101.3281.
 # Of course there is a noticable bias for small χbond and χenv.
 ψ₀ = InfinitePEPS(2, χbond)
-env₀ = leading_boundary(CTMRGEnv(ψ₀; Venv=ℂ^χenv), ψ₀, ctm_alg)
+env₀ = leading_boundary(CTMRGEnv(ψ₀, ℂ^χenv), ψ₀, ctm_alg)
 result = fixedpoint(ψ₀, H, opt_alg, env₀)
 @show result.E

src/algorithms/ctmrg.jl

@@ -64,7 +64,7 @@ Contract `state` using CTMRG and return the CTM environment.
 Per default, a random initial environment is used.
 """
 function MPSKit.leading_boundary(state, alg::CTMRG)
-    return MPSKit.leading_boundary(CTMRGEnv(state), state, alg)
+    return MPSKit.leading_boundary(CTMRGEnv(state, oneunit(spacetype(state))), state, alg)
 end
 function MPSKit.leading_boundary(envinit, state, alg::CTMRG)
     normold = 1.0

src/algorithms/peps_opt.jl

@@ -60,7 +60,7 @@ in `alg`. The initial environment `env₀` serves as an initial guess for the fi
 By default, a random initial environment is used.
 """
 function fixedpoint(
-    ψ₀::InfinitePEPS{T}, H, alg::PEPSOptimize, env₀::CTMRGEnv=CTMRGEnv(ψ₀; Venv=field(T)^20)
+    ψ₀::InfinitePEPS{T}, H, alg::PEPSOptimize, env₀::CTMRGEnv=CTMRGEnv(ψ₀, field(T)^20)
 ) where {T}
     (peps, env), E, ∂E, info = optimize(
         (ψ₀, env₀), alg.optimizer; retract=my_retract, inner=my_inner