92 lines
2.8 KiB
Julia
92 lines
2.8 KiB
Julia
using Arpack, SparseArrays, LRUCache
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using DelimitedFiles, Plots
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include("../ho_basis.jl")
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Λ = 0
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m = 1.0
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# Distinguishable particles: V12 = bound and V13 & V23 = resonant
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Va_of_r(r) = -2 * exp(-r^2/4)
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Vb_of_r(r) = -exp(-r^2 / 3) + exp(-r^2 / 10)
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E_max = 40
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μω_global = 0.4 * exp(-2im * pi / 9)
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# due to Jacobi coordinates
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μ1ω1 = μω_global * 1/2
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μ2ω2 = μω_global * 2
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μ1 = m * 1/2
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μ2 = m * 2/3
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println("No of threads = ", Threads.nthreads())
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basis = ho_basis_2B(E_max, Λ)
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println("Basis size = ", basis.dim)
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@time "T1" T1 = get_sp_T_matrix(basis.n1s, basis.l1s, [basis.n2s, basis.l2s]; μω_gen=μ1ω1, μ=μ1)
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@time "T2" T2 = get_sp_T_matrix(basis.n2s, basis.l2s, [basis.n1s, basis.l1s]; μω_gen=μ2ω2, μ=μ2)
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@time "Va" Va = get_jacobi_V1_matrix(Va_of_r, basis, μ1ω1)
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@time "Vb" Vb = get_jacobi_V2_matrix(Vb_of_r, basis, μω_global)
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@time "Ha" Ha = T1 + T2 + Va
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@time "Eigenvalues" target_evals, _ = eigs(Ha, nev=5, ncv=50, which=:SR, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
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display(target_evals)
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# free memory
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basis = T1 = T2 = V1_cache = V_relative_cache = V1 = V_relative = U = V2 = nothing
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GC.gc()
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training_c = [-0.5, -0.65, -0.8, -1, -1.2]
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extrapolating_c = [0.8, 0.6, 0.4, 0.2, 0.1, 0.0, -0.1, -0.2, -0.3]
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current_E = -0.5173809356244544
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exact = ComplexF64[]
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training = ComplexF64[]
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extrapolated = ComplexF64[]
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training_vecs = Vector{ComplexF64}[]
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for c in training_c
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print("Training for c = $c: ")
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H = Ha + c .* Vb
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evals, evecs = eigs(H, nev=3, ncv=24, which=:SR, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
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global current_E = nearest(evals, current_E)
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println(current_E)
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push!(training, current_E)
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push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
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end
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training_vecs = vcat(training_vecs, conj(training_vecs)) # CA-EC
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println("Original EC dimensionality = $(length(training_vecs))")
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@time "Gram-Schmidt" training_vecs = gram_schmidt!(training_vecs; verbose=true) # orthonormalization
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EC_basis = hcat(training_vecs...)
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Ha_EC = transpose(EC_basis) * Ha * EC_basis
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Vb_EC = transpose(EC_basis) * Vb * EC_basis
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current_E = -0.3005521915662689 - 0.13612069020686351im
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for c in extrapolating_c
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print("Extrapolating for c = $c: ")
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H = Ha + c .* Vb
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evals, evecs = eigs(H, nev=3, ncv=24, which=:SR, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
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global current_E = nearest(evals, current_E)
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println(current_E)
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push!(exact, current_E)
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# extrapolation
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H_EC = Ha_EC + c .* Vb_EC
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evals = eigvals(H_EC)
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push!(extrapolated, nearest(evals, current_E))
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end
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# exportCSV("temp/dis_HO_B2R.csv", (training, exact, extrapolated), ("training", "exact", "extrapolated"))
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scatter(real.(training),imag.(training), label="training")
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scatter!(real.(exact),imag.(exact), label="exact")
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scatter!(real.(extrapolated),imag.(extrapolated), label="extrapolated")
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savefig("temp/dis_HO_B2R.pdf") |