3-body XZ in p-space (not working)

EC.jl

@@ -0,0 +1,173 @@
+using Statistics, SparseArrays, LinearAlgebra, Arpack, Plots
+include("common.jl")
+
+"EC model for a Hamiltonian family H(c) = H0 + c * H1"
+mutable struct affine_EC
+    H0::AbstractMatrix{ComplexF64}
+    H1::AbstractMatrix{ComplexF64}
+    weights::Vector{ComplexF64}
+    trained::Bool
+
+    H0_EC
+    H1_EC
+    N_EC
+
+    ensemble_size::Int
+    H0_EC_ensemble
+    H1_EC_ensemble
+    N_EC_ensemble
+
+    training_E::Vector{ComplexF64}
+    exact_E::Vector{ComplexF64}
+    extrapolated_E::Vector{ComplexF64}
+    extrapolated_CI::Vector{ComplexF64}
+
+    affine_EC(H0::AbstractMatrix{ComplexF64}, H1::AbstractMatrix{ComplexF64}, weights::Vector{ComplexF64}=ones(ComplexF64, size(H0, 1)); ensemble_size=0) =
+        new(H0, H1, weights, false, nothing, nothing, nothing, ensemble_size, Matrix[], Matrix[], Matrix[], ComplexF64[], ComplexF64[], ComplexF64[], ComplexF64[])
+end
+
+"Train an EC model for a given range of c values.
+If a list is provided for ref_eval, they are used as reference values for picking the closest eigenvalues at each sampling point.
+If a single number is provided for ref_eval, it is used as a reference for the first point, and the previous eigenvalue is used as the reference for each successive point.
+If orthonormalize_threshold > 0, Gram-Schmidt orthonormalization is performed, using this value as the threshold for dropping redundant vectors."
+function train!(EC::affine_EC, c_vals; ref_eval=-10.0, CAEC=false, gram_schmidt_threshold=0, verbose=true, tol=1e-5)
+    training_vecs = Vector{ComplexF64}[]
+
+    for c in c_vals
+        verbose && println("Training for c = $c")
+
+        global current_E
+        if ref_eval isa Number
+            current_E = ref_eval
+            ref_eval = nothing
+        elseif !isnothing(ref_eval)
+            current_E = popfirst!(ref_eval)
+        end
+
+        H = EC.H0 + c .* EC.H1
+
+        evals, evecs = eigs(H, sigma=current_E, maxiter=5000, tol=tol, ritzvec=true, check=1)
+        current_E = nearest(evals, current_E)
+
+        push!(EC.training_E, current_E)
+        push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
+    end
+
+    CAEC && append!(training_vecs, conj.(training_vecs))
+
+    (EC.H0_EC, EC.H1_EC, EC.N_EC) = get_reduced_matrices(EC, training_vecs, gram_schmidt_threshold; verbose=true)
+
+    for _ in 1:EC.ensemble_size
+        subsample = resample(length(training_vecs))
+        if gram_schmidt_threshold > 0
+            (H0_EC, H1_EC, N_EC) = get_reduced_matrices(EC, training_vecs, gram_schmidt_threshold, subsample; verbose=false)
+            push!(EC.H0_EC_ensemble, H0_EC)
+            push!(EC.H1_EC_ensemble, H1_EC)
+            push!(EC.N_EC_ensemble, N_EC)
+        else
+            push!(EC.H0_EC_ensemble, EC.H0_EC[subsample, subsample])
+            push!(EC.H1_EC_ensemble, EC.H1_EC[subsample, subsample])
+            push!(EC.N_EC_ensemble, EC.N_EC[subsample, subsample])
+        end
+    end
+
+    EC.trained = true
+end
+
+function get_reduced_matrices(EC::affine_EC, training_vecs, gram_schmidt_threshold, subsample=1:length(training_vecs); verbose=false)
+    vecs = deepcopy(training_vecs[subsample])
+
+    if gram_schmidt_threshold > 0; vecs = gram_schmidt!(vecs, EC.weights, gram_schmidt_threshold; verbose=verbose); end
+
+    EC_basis = hcat(vecs...)
+    weights_mat = spdiagm(EC.weights)
+    H0_EC = transpose(EC_basis) * weights_mat * EC.H0 * EC_basis
+    H1_EC = transpose(EC_basis) * weights_mat * EC.H1 * EC_basis
+    N_EC = transpose(EC_basis) * weights_mat * EC_basis
+
+    return (H0_EC, H1_EC, N_EC)
+end
+
+resample(n::Int) = rand(1:n, n) |> unique |> sort
+
+"Extrapolate using a trained EC model for a given range of c values
+If a list is provided for ref_eval, they are used as reference values for picking the closest eigenvalues at each point.
+If a single number is provided for ref_eval, it is used as a reference for the first point, and the previous eigenvalue is used as the reference for each successive point.
+If precalculated_exact_E is provided, ref_eval is ignored.
+If pseudo_inv_tol > 0, the GEVP is avoided using Moore-Penrose psuedoinverse, using this value as the relative tolerance for dropping redundant vectors."
+function extrapolate!(EC::affine_EC, c_vals; ref_eval=EC.training_E[end], pseudo_inv_tol=0, verbose=true, tol=1e-5, precalculated_exact_E=nothing)
+    @assert EC.trained "EC model must be trained using train() before extrapolation"
+
+    for c in c_vals
+        global current_E
+
+        if isnothing(precalculated_exact_E)
+            if ref_eval isa Number
+                current_E = ref_eval
+                ref_eval = nothing
+            elseif !isnothing(ref_eval)
+                current_E = popfirst!(ref_eval)
+            end
+
+            verbose && println("Extact solution for c = $c")
+            
+            H = EC.H0 + c .* EC.H1
+            evals, _ = eigs(H, sigma=current_E, maxiter=5000, tol=tol, ritzvec=false, check=1)
+            current_E = nearest(evals, current_E)
+        else
+            current_E = popfirst!(precalculated_exact_E)
+        end
+
+        push!(EC.exact_E, current_E)
+
+        verbose && println("Extrapolating for c = $c")
+
+        evals = get_extrapolated_evals(EC.H0_EC, EC.H1_EC, EC.N_EC, c, pseudo_inv_tol)
+        push!(EC.extrapolated_E, nearest(evals, current_E))
+
+        if EC.ensemble_size > 0
+            E_ensemble = zeros(ComplexF64, EC.ensemble_size)
+            for i in 1:EC.ensemble_size
+                evals = get_extrapolated_evals(EC.H0_EC_ensemble[i], EC.H1_EC_ensemble[i], EC.N_EC_ensemble[i], c, pseudo_inv_tol)
+                E_ensemble[i] = nearest(evals, current_E)
+            end
+            re_CI = std(real.(E_ensemble))
+            im_CI = std(imag.(E_ensemble))
+            push!(EC.extrapolated_CI, complex(re_CI, im_CI))
+        end
+    end
+end
+
+"Solve the GEVP with or without Moore-Penrose psuedoinverse"
+function get_extrapolated_evals(H0_EC, H1_EC, N_EC, c, pseudo_inv_tol)
+    H_EC = H0_EC + c .* H1_EC
+    if pseudo_inv_tol > 0
+        inv_N_EC = pinv(N_EC; atol=pseudo_inv_tol)
+        H_EC = inv_N_EC * H_EC
+        return eigvals(H_EC)
+    else
+        return eigvals(H_EC, N_EC)
+    end
+end
+
+"Export EC data as CSV file"
+exportCSV(EC::affine_EC, filename) = exportCSV(filename, (EC.training_E, EC.exact_E, EC.extrapolated_E), ("training", "exact", "extrapolated"))
+
+"Plot EC data and optionally save figure to a file"
+function plot(EC::affine_EC, save_fig_filename=nothing; basis_points=nothing, basis_contour=nothing, xlims=nothing, ylims=nothing)
+    scatter(real.(EC.training_E), imag.(EC.training_E), label="training")
+    scatter!(real.(EC.exact_E), imag.(EC.exact_E), label="exact", markercolor=:white)
+    if EC.ensemble_size > 0
+        scatter!(real.(EC.extrapolated_E), imag.(EC.extrapolated_E), xerror=real.(EC.extrapolated_CI), yerror=imag.(EC.extrapolated_CI), label="extrapolated", m=:x)
+    else
+        scatter!(real.(EC.extrapolated_E), imag.(EC.extrapolated_E), label="extrapolated", m=:x)
+    end
+
+    isnothing(basis_points) || scatter!(real.(basis_points), imag.(basis_points), m=:x, label="basis")
+    isnothing(basis_contour) || plot!(real.(basis_contour), imag.(basis_contour), label="contour")
+
+    isnothing(xlims) || xlims!(xlims...)
+    isnothing(ylims) || ylims!(ylims...)
+
+    isnothing(save_fig_filename) || savefig(save_fig_filename)
+end

Project.toml

@@ -1,5 +1,7 @@
 [deps]
 Arpack = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 FastGaussQuadrature = "442a2c76-b920-505d-bb47-c5924d526838"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
@@ -7,6 +9,7 @@ LRUCache = "8ac3fa9e-de4c-5943-b1dc-09c6b5f20637"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
+Roots = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 WignerSymbols = "9f57e263-0b3d-5e2e-b1be-24f2bb48858b"

berggren_3body_resonance.jl

@@ -1,5 +1,5 @@
 using LinearAlgebra, SparseArrays, Arpack
-include("helper.jl")
+include("common.jl")
 include("p_space.jl")
 include("ho_basis.jl")
 include("berggren.jl")

calculations/2body_data.jl

@@ -0,0 +1,33 @@
+using Roots, DelimitedFiles
+
+include("../EC.jl")
+include("../common.jl")
+include("../p_space.jl")
+
+μ = 0.5
+V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q)) # ResonanceEC: Eq. (20)
+
+# determining c0 with EC
+temp_c = range(1.1, 0.9, 3)
+p, w = get_mesh([0, 8], [256])
+H0 = get_T_matrix(p, μ)
+V = get_V_matrix(V_system(1), p, w)
+EC = affine_EC(H0, V, w)
+train!(EC, temp_c; ref_eval=-0.2, CAEC=false, verbose=false)
+quick_extrapolate(c) = minimum(abs2, get_extrapolated_evals(EC.H0_EC, EC.H1_EC, EC.N_EC, c, 0))
+c0 = find_zero(quick_extrapolate, 0.85)
+
+training_c = range(1.2, 0.9, 9) # original: range(1.35, 0.9, 5)
+extrapolating_c = range(0.78, 0.45, 7) # original: range(0.75, 0.40, 8)
+
+data_c = vcat(training_c, extrapolating_c)
+data_E = [quick_pole_E(V_system(c)) for c in data_c]
+
+# export to CSV
+file = "temp/2body_data.csv"
+delim = ','
+open(file, "w") do f
+    writedlm(f, ["c" "re_E" "im_E"], delim)
+    writedlm(f, [c0 0 0], delim) # first entry for the threshold
+    writedlm(f, hcat(data_c, real.(data_E), imag.(data_E)), delim)
+end

calculations/2in3body_HO_B2R_EC.jl

@@ -1,83 +0,0 @@
-using Arpack, SparseArrays, LRUCache
-using DelimitedFiles, Plots
-include("../ho_basis.jl")
-
-Λ = 0
-m = 1.0
-V_of_r(r) = -5 * exp(-r^2 / 3) + 2 * exp(-r^2 / 10)
-
-E_max = 40
-μω_global = 0.2 * exp(-2im * pi / 9)
-
-# due to Jacobi coordinates
-μ1ω1 = μω_global * 1/2
-μ2ω2 = μω_global * 2
-μ1 = m * 1/2
-μ2 = m * 2/3
-
-println("No of threads = ", Threads.nthreads())
-
-basis = ho_basis_2B(E_max, Λ)
-println("Basis size = ", basis.dim)
-
-@time "T1" T1 = get_sp_T_matrix(basis.n1s, basis.l1s, [basis.n2s, basis.l2s]; μω_gen=μ1ω1, μ=μ1)
-@time "T2" T2 = get_sp_T_matrix(basis.n2s, basis.l2s, [basis.n1s, basis.l1s]; μω_gen=μ2ω2, μ=μ2)
-
-@time "V" Vb = get_jacobi_V_matrix(V_of_r, basis, μ1ω1, μω_global)
-
-@time "H0" Ha = T1 + T2
-
-# free memory
-basis = T1 = T2 = V1_cache = V_relative_cache = V1 = V_relative = U = V2 = nothing
-GC.gc()
-
-current_E = -0.26141959851000807
-
-training_c = range(1.1, 0.9, 5)
-extrapolating_c = range(0.78, 0.45, 7)
-
-exact = ComplexF64[]
-training = ComplexF64[]
-extrapolated = ComplexF64[]
-training_vecs = Vector{ComplexF64}[]
-
-for c in training_c
-    println("Training for c = $c")
-    H = Ha + c .* Vb
-    evals, evecs = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
-
-    global current_E = nearest(evals, current_E)
-    push!(training, current_E)
-    push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
-end
-
-# CA-EC
-training_vecs = vcat(training_vecs, conj(training_vecs))
-
-EC_basis = hcat(training_vecs...)
-N_EC = transpose(EC_basis) * EC_basis
-Ha_EC = transpose(EC_basis) * Ha * EC_basis
-Vb_EC = transpose(EC_basis) * Vb * EC_basis
-
-current_E = 0.05387926316280764 - 0.008900278183544552im
-
-for c in extrapolating_c
-    println("Extrapolating for c = $c")
-    H = Ha + c .* Vb
-    evals, evecs = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
-
-    global current_E = nearest(evals, current_E)
-    push!(exact, current_E)
-
-    # extrapolation
-    H_EC = Ha_EC + c .* Vb_EC
-    evals = eigvals(H_EC, N_EC)
-    push!(extrapolated, nearest(evals, current_E))
-end
-
-scatter(real.(training),imag.(training), label="training")
-scatter!(real.(exact),imag.(exact), label="exact")
-scatter!(real.(extrapolated),imag.(extrapolated), label="extrapolated")
-xlims!(-0.3, 0.3)
-ylims!(-0.2, 0)
-savefig("temp/2in3_HO_B2R.pdf")

calculations/3body_Berggren_B2R_EC.jl

@@ -1,82 +1,40 @@
-using Plots
+include("../p_space.jl")
+include("../EC.jl")
+
+Λ = 0
+m = 1.0
+V_of_r(r) =  2 * exp(-(r-3)^2 / (1.5)^2)
+
+ϕ = 0.1
+vertices = [0, 4 * exp(-1im * ϕ), 5, 6]
+subdivisions = [40, 12, 15]
+jmax = 6
+
+E_max = 40
+μω_global = 0.5
+
+H0, weights = get_3b_H_matrix(jacobi, V_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m)
+
+# Vp = perturbation to make the state artificially bound
+Vp_of_r(r) = -exp(-(r/3)^2)
+Vp, _ = get_3b_H_matrix(jacobi, Vp_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m, false, true)
 
 training_c = [2.6, 2.4, 2.2, 2.0, 1.8]
 extrapolating_c = 0.0 : 0.2 : 1.2
 
 training_ref = -2.22 # complete list not needed because identification is simple
 
-exact_ref = reverse([4.076662025307587-0.012709842443350328im,
+extrapolating_ref = [4.076662025307587-0.012709842443350328im,
     3.613318119833891-0.007335804709990623im,
     3.1453431847006783-0.004030580410326795im,
     2.672967129943755-0.00211498327461944im,
     2.196542557810288-0.0010719835443437104im,
     1.7164583929199813-0.0005455212208182736im,
-    1.233088227541505-0.0003070320106485624im])
+    1.233088227541505-0.0003070320106485624im]
 
-include("../p_space_3body_resonance.jl")
+EC = affine_EC(H0, Vp, weights)
-H0 = H
+train!(EC, training_c; ref_eval=training_ref, CAEC=true)
+extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)
 
-# Vp = perturbation to make the state artificially bound
+exportCSV(EC, "temp/Berggren_B2R.csv")
-Vp_of_r(r) = -exp(-(r/3)^2)
+plot(EC, "temp/Berggren_B2R.pdf")
-Vp_l(j, k, kp) = Vl_mat_elem(Vp_of_r, j, k, kp; atol=atol, maxevals=maxevals, R_cutoff=R_cutoff)
-
-@time "Vp block diagonal part" begin
-    Vpb_blocks = [kron_sum(get_V_matrix((k, kp) -> Vp_l(j1, k, kp), ks, ws), spzeros(length(ks), length(ks))) for (j1, _) in js]
-    Vpb = blockdiag(sparse.(Vpb_blocks)...)
-end
-
-@time "Vp2_HO" Vp2_HO =  get_jacobi_V2_matrix(Vp_of_r, basis_ho, μω_global; atol=atol, maxevals=maxevals)
-@time "Vp2" Vp2 = W_left * Vp2_HO * transpose(W_right)
-@time "Vp" Vp = Vpb + Vp2
-
-# free memory
-basis = Hb_blocks = Hb = basis_ho = V2_HO = W_right = W_left = V2 = nothing
-GC.gc()
-
-exact = ComplexF64[]
-training = ComplexF64[]
-extrapolated = ComplexF64[]
-training_vecs = Vector{ComplexF64}[]
-
-current_E = training_ref
-
-for c in training_c
-    println("Training for c = $c")
-
-    local H = H0 + c .* Vp
-    local evals, evecs = eigs(H, sigma=current_E, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
-
-    global current_E = nearest(evals, current_E)
-    push!(training, current_E)
-    push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
-end
-
-training_vecs = vcat(training_vecs, conj(training_vecs)) # CA-EC
-EC_basis = hcat(training_vecs...)
-weights_mat = spdiagm(repeat(kron(ws, ws), jmax + 1))
-N_EC = transpose(EC_basis) * weights_mat * EC_basis
-H0_EC = transpose(EC_basis) * weights_mat * H0 * EC_basis
-Vp_EC = transpose(EC_basis) * weights_mat * Vp * EC_basis
-
-for c in extrapolating_c
-    println("Extrapolating for c = $c")
-    global current_E = pop!(exact_ref)
-
-    local H = H0 + c .* Vp
-    local evals, _ = eigs(H, sigma=current_E, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
-
-    global current_E = nearest(evals, current_E)
-    push!(exact, current_E)
-
-    # extrapolation
-    H_EC = H0_EC + c .* Vp_EC
-    evals = eigvals(H_EC, N_EC)
-    push!(extrapolated, nearest(evals, current_E))
-end
-
-exportCSV("temp/Berggren_B2R.csv", (training, exact, extrapolated), ("training", "exact", "extrapolated"))
-
-scatter(real.(training),imag.(training), label="training")
-scatter!(real.(exact),imag.(exact), label="exact")
-scatter!(real.(extrapolated),imag.(extrapolated), label="extrapolated")
-savefig("temp/Berggren_B2R.pdf")

calculations/3body_Berggren_R2R_EC.jl

@@ -1,81 +1,42 @@
-using Plots
+include("../p_space.jl")
+include("../EC.jl")
+
+Λ = 0
+m = 1.0
+V_of_r(r) =  2 * exp(-(r-3)^2 / (1.5)^2)
+
+vertices = [0, 2 - 0.2im, 3, 4]
+subdivisions = [15, 10, 10]
+jmax = 4
+
+E_max = 40
+μω_global = 0.5
+
+H0, weights = get_3b_H_matrix(jacobi, V_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m)
+
+# Vp = perturbation to make the state artificially bound
+Vp_of_r(r) = -exp(-(r/3)^2)
+Vp, _ = get_3b_H_matrix(jacobi, Vp_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m, false, true)
 
 training_c = [1.1, 0.9, 0.7, 0.5]
 extrapolating_c = 0.0 : 0.2 : 1.2
 
-training_ref = reverse([1.4750633616275919 - 0.0003021770706749637im
+training_ref = [1.4750633616275919 - 0.0003021770706749637im
     1.9567078295375822 - 0.0007646829108872369im
     2.4351117758403076 - 0.001281037843108658im
-    2.9096543462392357 - 0.002962488527470604im])
+    2.9096543462392357 - 0.002962488527470604im]
 
-exact_ref = reverse([4.076662025307587-0.012709842443350328im,
+extrapolating_ref = [4.076662025307587-0.012709842443350328im,
     3.613318119833891-0.007335804709990623im,
     3.1453431847006783-0.004030580410326795im,
     2.672967129943755-0.00211498327461944im,
     2.196542557810288-0.0010719835443437104im,
     1.7164583929199813-0.0005455212208182736im,
-    1.233088227541505-0.0003070320106485624im])
+    1.233088227541505-0.0003070320106485624im]
 
-include("../p_space_3body_resonance.jl")
+EC = affine_EC(H0, Vp, weights)
-H0 = H
+train!(EC, training_c; ref_eval=training_ref, CAEC=false)
+extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)
 
-# Vp = perturbation to make the state artificially bound
+exportCSV(EC, "temp/Berggren_R2R.csv")
-Vp_of_r(r) = -exp(-(r/3)^2)
+plot(EC, "temp/Berggren_R2R.pdf")
-Vp_l(j, k, kp) = Vl_mat_elem(Vp_of_r, j, k, kp; atol=atol, maxevals=maxevals, R_cutoff=R_cutoff)
-
-@time "Vp block diagonal part" begin
-    Vpb_blocks = [kron_sum(get_V_matrix((k, kp) -> Vp_l(j1, k, kp), ks, ws), spzeros(length(ks), length(ks))) for (j1, _) in js]
-    Vpb = blockdiag(sparse.(Vpb_blocks)...)
-end
-
-@time "Vp2_HO" Vp2_HO =  get_jacobi_V2_matrix(Vp_of_r, basis_ho, μω_global; atol=atol, maxevals=maxevals)
-@time "Vp2" Vp2 = W_left * Vp2_HO * transpose(W_right)
-@time "Vp" Vp = Vpb + Vp2
-
-# free memory
-basis = Hb_blocks = Hb = basis_ho = V2_HO = W_right = W_left = V2 = nothing
-GC.gc()
-
-exact = ComplexF64[]
-training = ComplexF64[]
-extrapolated = ComplexF64[]
-training_vecs = Vector{ComplexF64}[]
-
-for c in training_c
-    println("Training for c = $c")
-    global current_E = pop!(training_ref)
-
-    local H = H0 + c .* Vp
-    local evals, evecs = eigs(H, sigma=current_E, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
-
-    global current_E = nearest(evals, current_E)
-    push!(training, current_E)
-    push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
-end
-
-EC_basis = hcat(training_vecs...)
-weights_mat = spdiagm(repeat(kron(ws, ws), jmax + 1))
-N_EC = transpose(EC_basis) * weights_mat * EC_basis
-H0_EC = transpose(EC_basis) * weights_mat * H0 * EC_basis
-Vp_EC = transpose(EC_basis) * weights_mat * Vp * EC_basis
-
-for c in extrapolating_c
-    println("Extrapolating for c = $c")
-    global current_E = pop!(exact_ref)
-
-    local H = H0 + c .* Vp
-    local evals, _ = eigs(H, sigma=current_E, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
-
-    global current_E = nearest(evals, current_E)
-    push!(exact, current_E)
-
-    # extrapolation
-    H_EC = H0_EC + c .* Vp_EC
-    evals = eigvals(H_EC, N_EC)
-    push!(extrapolated, nearest(evals, current_E))
-end
-
-scatter(real.(training),imag.(training), label="training")
-scatter!(real.(exact),imag.(exact), label="exact")
-scatter!(real.(extrapolated),imag.(extrapolated), label="extrapolated")
-savefig("temp/Berggren_R2R.pdf")

calculations/3body_CSM_B2R_EC.jl

@@ -0,0 +1,40 @@
+include("../p_space.jl")
+include("../EC.jl")
+
+Λ = 0
+m = 1.0
+V_of_r(r) =  2 * exp(-(r-3)^2 / (1.5)^2)
+
+ϕ = 0.1
+vertices = [0, 6 * exp(-1im * ϕ)]
+subdivisions = [50]
+jmax = 4
+
+E_max = 40
+μω_global = 0.5
+
+H0, weights = get_3b_H_matrix(jacobi, V_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m)
+
+# Vp = perturbation to make the state artificially bound
+Vp_of_r(r) = -exp(-(r/3)^2)
+Vp, _ = get_3b_H_matrix(jacobi, Vp_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m, false, true)
+
+training_c = [2.6, 2.4, 2.2, 2.0, 1.8]
+extrapolating_c = 0.0 : 0.2 : 1.2
+
+training_ref = -2.22 # complete list not needed because identification is simple
+
+exact_E = [4.076662025307587-0.012709842443350328im,
+    3.613318119833891-0.007335804709990623im,
+    3.1453431847006783-0.004030580410326795im,
+    2.672967129943755-0.00211498327461944im,
+    2.196542557810288-0.0010719835443437104im,
+    1.7164583929199813-0.0005455212208182736im,
+    1.233088227541505-0.0003070320106485624im]
+
+EC = affine_EC(H0, Vp, weights)
+train!(EC, training_c; ref_eval=training_ref, CAEC=true)
+extrapolate!(EC, extrapolating_c; precalculated_exact_E=exact_E)
+
+exportCSV(EC, "temp/CSM_B2R.csv")
+plot(EC, "temp/CSM_B2R.pdf")

calculations/3body_HO_B2R_ACCC.jl

@@ -0,0 +1,68 @@
+using Roots, LinearAlgebra, Plots
+
+include("../EC.jl")
+include("../common.jl")
+include("../ho_basis.jl")
+
+V_of_r(r) = 2 * exp(-(r-3)^2 / (1.5)^2)
+Λ = 0
+m = 1.0
+
+ϕ = 0.1
+μω_global = 0.5 * exp(-2im * ϕ)
+E_max = 40
+
+H0 = get_3b_H_matrix(jacobi, V_of_r, μω_global, E_max, Λ, m, true, true)
+
+# Vp = perturbation to make the state artificially bound
+Vp_of_r(r) = -exp(-(r/3)^2)
+@time "Vp" Vp = get_3b_H_matrix(jacobi, Vp_of_r, μω_global, E_max, Λ, m, false, true)
+
+training_ref = -2.22
+extrapolating_ref = [4.076662025307587-0.012709842443350328im,
+    3.613318119833891-0.007335804709990623im,
+    3.1453431847006783-0.004030580410326795im,
+    2.672967129943755-0.00211498327461944im,
+    2.196542557810288-0.0010719835443437104im,
+    1.7164583929199813-0.0005455212208182736im,
+    1.233088227541505-0.0003070320106485624im]
+
+training_c = range(2.8, 1.8, 5)
+extrapolating_c = 0.0 : 0.2 : 1.2
+
+EC = affine_EC(H0, Vp)
+train!(EC, training_c; ref_eval=training_ref, CAEC=true)
+extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)
+
+# determining c0 with EC
+approx_c0 = 1.5
+quick_extrapolate(c) = minimum(abs2, get_extrapolated_evals(EC.H0_EC, EC.H1_EC, EC.N_EC, c, 1e-14))
+c0 = find_zero(quick_extrapolate, approx_c0)
+
+order::Int = ceil((length(training_c) - 1) / 2) # order of the Pade approximant
+
+# Solve coefficients as a linear system
+training_k = alt_sqrt.(EC.training_E)
+M_left_element(c, i) = alt_sqrt(c - c0)^i
+M_left = M_left_element.(training_c, (0:order)')
+M_right = -training_k .* M_left[:, 2:end] # remove the first column
+M = hcat(M_left, M_right) # M = [M_left | M_right]
+sol = M \ training_k
+a = sol[1:order+1]
+b = [1; sol[order+2:end]]
+
+# Pade approximant
+polynomial(a, c) = sum(i -> a[i+1] * alt_sqrt(c - c0)^i, 0:order)
+pade_approx(c) = polynomial(a, c) / polynomial(b, c)
+
+# Extrapolate
+extrapolated_k = pade_approx.([extrapolating_c; training_c])
+extrapolated_E = extrapolated_k .^ 2
+
+# Plotting
+scatter(real.(EC.training_E), imag.(EC.training_E), label="training")
+scatter!(real.(EC.exact_E), imag.(EC.exact_E), label="exact")
+scatter!(real.(EC.extrapolated_E), imag.(EC.extrapolated_E), label="CAEC", m=:x)
+scatter!(real.(extrapolated_E), imag.(extrapolated_E), label="ACCC", m=:+)
+title!("3-body extrapolation with $(length(training_c)) training points")
+savefig("temp/3body_HO_B2R_ACCC-$(length(training_c)).pdf")

calculations/3body_HO_B2R_EC.jl

@@ -1,62 +1,36 @@
-using Plots
+include("../ho_basis.jl")
+include("../EC.jl")
 
-training_ref = -0.72763
+V_of_r(r) = 2 * exp(-(r-3)^2 / (1.5)^2)
-exact_ref = 4.0766890719636635 - 0.01275892774109674im
+Λ = 0
+m = 1.0
 
-training_c = [2.0, 1.9, 1.8]
+ϕ = 0.1
-extrapolating_c = 0.0 : 0.2 : 1.2
+μω_global = 0.5 * exp(-2im * ϕ)
+E_max = 40
 
-include("../ho_basis_3body_resonance.jl")
+H0 = get_3b_H_matrix(jacobi, V_of_r, μω_global, E_max, Λ, m, true, true)
-H0 = H
 
 # Vp = perturbation to make the state artificially bound
 Vp_of_r(r) = -exp(-(r/3)^2)
-@time "Vp" Vp =  get_src_V_matrix(Vp_of_r, basis, μω, μω_global)
+@time "Vp" Vp = get_3b_H_matrix(jacobi, Vp_of_r, μω_global, E_max, Λ, m, false, true)
 
-exact = ComplexF64[]
+training_ref = -2.22
-training = ComplexF64[]
-extrapolated = ComplexF64[]
-training_vecs = Vector{ComplexF64}[]
 
-current_E = training_ref
+extrapolating_ref = [4.076662025307587-0.012709842443350328im,
+    3.613318119833891-0.007335804709990623im,
+    3.1453431847006783-0.004030580410326795im,
+    2.672967129943755-0.00211498327461944im,
+    2.196542557810288-0.0010719835443437104im,
+    1.7164583929199813-0.0005455212208182736im,
+    1.233088227541505-0.0003070320106485624im]
 
-for c in training_c
+training_c = [2.6, 2.4, 2.2, 2.0, 1.8]
-    println("Training for c = $c")
+extrapolating_c = 0.0 : 0.2 : 1.2
-    local H = H0 + c .* Vp
-    local evals, evecs = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
 
-    global current_E = nearest(evals, current_E)
+EC = affine_EC(H0, Vp)
-    push!(training, current_E)
+train!(EC, training_c; ref_eval=training_ref, CAEC=true)
-    push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
+extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)
-end
 
-# CA-EC
+exportCSV(EC, "temp/HO_B2R.csv")
-training_vecs = vcat(training_vecs, conj(training_vecs))
+plot(EC, "temp/HO_B2R.pdf")
-
-EC_basis = hcat(training_vecs...)
-N_EC = transpose(EC_basis) * EC_basis
-H0_EC = transpose(EC_basis) * H0 * EC_basis
-Vp_EC = transpose(EC_basis) * Vp * EC_basis
-
-current_E = exact_ref
-
-for c in extrapolating_c
-    println("Extrapolating for c = $c")
-    local H = H0 + c .* Vp
-    local evals, _ = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
-
-    global current_E = nearest(evals, current_E)
-    push!(exact, current_E)
-
-    # extrapolation
-    H_EC = H0_EC + c .* Vp_EC
-    evals = eigvals(H_EC, N_EC)
-    push!(extrapolated, nearest(evals, current_E))
-end
-
-exportCSV("temp/HO_B2R.csv", (training, exact, extrapolated), ("training", "exact", "extrapolated"))
-
-scatter(real.(training),imag.(training), label="training")
-scatter!(real.(exact),imag.(exact), label="exact")
-scatter!(real.(extrapolated),imag.(extrapolated), label="extrapolated")
-savefig("temp/HO_B2R.pdf")

calculations/3body_HO_R2R_EC.jl

@@ -1,54 +1,24 @@
-using DelimitedFiles, Plots
+include("../ho_basis.jl")
-include("../ho_basis_3body_resonance.jl")
+include("../EC.jl")
 
-current_E = 5.9673 - 0.0006im
+V_of_r(r) = 2 * exp(-(r-3)^2 / (1.5)^2)
+Λ = 0
+m = 1.0
+
+μω_global = 0.5 * exp(-2im * pi / 9)
+E_max = 40
+
+T = get_3b_H_matrix(src, V_of_r, μω_global, E_max, Λ, m, true, false)
+V = get_3b_H_matrix(src, V_of_r, μω_global, E_max, Λ, m, false, true)
+
+ref_E = 5.9673 - 0.0006im
 
 training_c = 2.0 : -0.2 : 1.2
 extrapolating_c = 1.05 .- [0.0 : 0.1 : 0.4; 0.45 : 0.05 : 0.60]
 
-@time "H0" H0 = T1 + T2
+EC = affine_EC(T, V)
+train!(EC, training_c; ref_eval=ref_E, CAEC=false)
+extrapolate!(EC, extrapolating_c)
 
-# free memory
+exportCSV(EC, "temp/HO_R2R.csv")
-basis = T1 = T2 = V1_cache = V_relative_cache = V1 = V_relative = U = V2 = nothing
+plot(EC, "temp/HO_R2R.pdf")
-GC.gc()
-
-exact = ComplexF64[]
-training = ComplexF64[]
-extrapolated = ComplexF64[]
-training_vecs = Vector{ComplexF64}[]
-
-for c in training_c
-    println("Training for c = $c")
-    local H = H0 + c .* V
-    local evals, evecs = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
-
-    global current_E = nearest(evals, current_E)
-    push!(training, current_E)
-    push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
-end
-
-EC_basis = hcat(training_vecs...)
-N_EC = transpose(EC_basis) * EC_basis
-H0_EC = transpose(EC_basis) * H0 * EC_basis
-V_EC = transpose(EC_basis) * V * EC_basis
-
-for c in extrapolating_c
-    println("Extrapolating for c = $c")
-    local H = H0 + c .* V
-    local evals, evecs = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
-
-    global current_E = nearest(evals, current_E)
-    push!(exact, current_E)
-
-    # extrapolation
-    H_EC = H0_EC + c .* V_EC
-    evals = eigvals(H_EC, N_EC)
-    push!(extrapolated, nearest(evals, current_E))
-end
-
-exportCSV("temp/NCSM.csv", (training, exact, extrapolated), ("training", "exact", "extrapolated"))
-
-scatter(real.(training),imag.(training), label="training")
-scatter!(real.(exact),imag.(exact), label="exact")
-scatter!(real.(extrapolated),imag.(extrapolated), label="extrapolated")
-savefig("temp/NCSM.pdf")

calculations/3body_dis_CSM_EC.jl

@@ -0,0 +1,77 @@
+include("../p_space.jl")
+include("../EC.jl")
+
+Λ = 0
+m = 1.0
+
+# Distinguishable particles: V12 = bound and V13 & V23 = resonant
+Vsubsystem_of_r(r) = -2 * exp(-r^2/4)
+Vdecay_of_r(r) = -exp(-r^2 / 3) + exp(-r^2 / 10)
+
+ϕ = 0.1
+vertices = [0, 6 * exp(-1im * ϕ)]
+subdivisions = [50]
+jmax = 4
+
+E_max = 40
+μω_global = 0.4
+
+# Jacobi coordinates
+μ1ω1 = μω_global * 1/2
+μ2ω2 = μω_global * 2
+μ1 = m * 1/2
+μ2 = m * 2/3
+
+atol=10^-5; maxevals=10^5; R_cutoff=16; verbose=true;
+
+###########
+
+verbose && println("No of threads = ", Threads.nthreads())
+
+V_l(j, k, kp) = Vl_mat_elem(Vsubsystem_of_r, j, k, kp; atol=atol, maxevals=maxevals, R_cutoff=R_cutoff)
+
+ks, ws = get_mesh(vertices, subdivisions)
+weights = repeat(kron(ws, ws), jmax + 1)
+block_size = length(ks)
+tri((j1, j2)) = triangle_ineq(j1, j2, Λ)
+js = collect(Iterators.filter(tri, iter_prod(0:jmax, 0:jmax)))
+basis = iter_prod(js, zip(ks, ws), zip(ks, ws)) # basis = ((j1, j2), (k1, w1), (k2, w2))
+basis_size = length(js) * length(ks)^2
+@assert length(basis) == basis_size "Something wrong with the basis"
+verbose && println("Basis size = $basis_size")
+
+@time "Block diagonal part" begin
+    blocks = [kron_sum(get_H_matrix((k, kp) -> V_l(j1, k, kp), ks, ws, μ1), get_T_matrix(ks, μ2)) for (j1, _) in js]
+    Ha = blockdiag(sparse.(blocks)...)
+end
+
+basis_ho = ho_basis_2B(E_max, Λ)
+verbose && println("HO basis size = ", basis_ho.dim)
+
+@time "V2_HO" V2_HO =  get_jacobi_V2_matrix(Vdecay_of_r, basis_ho, μω_global)
+
+@time "W_right" W_right = get_W_matrix(basis, basis_ho, μ1ω1, μ2ω2; weights=true)
+@time "W_left" W_left = get_W_matrix(basis, basis_ho, μ1ω1, μ2ω2; weights=false)
+
+@time "V2" Vb = W_left * V2_HO * transpose(W_right)
+
+###########
+
+training_c = [-0.55, -0.7, -0.85, -1, -1.2]
+extrapolating_c = [0.2, 0.1, 0.0, -0.1, -0.2, -0.3]
+
+ref_E = -0.5173809356244544
+
+exact_ref = [-0.31360746615280954-0.07689284936870341im
+    -0.3233372403877718-0.06011323914565665im
+    -0.339615582074795-0.04239442037174759im
+    -0.36333816534241997-0.02648721825958402im
+    -0.39376650561322885-0.014382935339817332im	
+    -0.4299479825535172-0.006510710745123606im]
+
+EC = affine_EC(Ha, Vb)
+train!(EC, training_c; ref_eval=ref_E, CAEC=true)
+extrapolate!(EC, extrapolating_c; precalculated_exact_E=exact_ref)
+
+exportCSV(EC, "temp/dis_CSM_B2R.csv")
+plot(EC, "temp/dis_CSM_B2R.pdf")

calculations/3body_dis_HO_EC.jl

@@ -1,6 +1,5 @@
-using Arpack, SparseArrays, LRUCache
-using DelimitedFiles, Plots
 include("../ho_basis.jl")
+include("../EC.jl")
 
 Λ = 0
 m = 1.0
@@ -31,62 +30,23 @@ println("Basis size = ", basis.dim)
 
 @time "Ha" Ha = T1 + T2 + Va
 @time "Eigenvalues" target_evals, _ = eigs(Ha, nev=5, ncv=50, which=:SR, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
-
 display(target_evals)
 
-# free memory
+training_c = [-0.55, -0.7, -0.85, -1, -1.2]
-basis = T1 = T2 = V1_cache = V_relative_cache = V1 = V_relative = U = V2 = nothing
+extrapolating_c = [0.2, 0.1, 0.0, -0.1, -0.2, -0.3]
-GC.gc()
 
-training_c = [-0.5, -0.65, -0.8, -1, -1.2]
+ref_E = -0.5173809356244544
-extrapolating_c = [0.8, 0.6, 0.4, 0.2, 0.1, 0.0, -0.1, -0.2, -0.3]
 
-current_E = -0.5173809356244544
+exact_ref = [-0.3136074661528041-0.07689284936868852im
+    -0.323337240387771-0.06011323914564878im
+    -0.33961558207479553-0.04239442037174764im
+    -0.3633381653424224-0.026487218259589693im
+    -0.393766505613234-0.014382935339825854im
+    -0.42994798255352606-0.006510710745131777im]
 
-exact = ComplexF64[]
+EC = affine_EC(Ha, Vb)
-training = ComplexF64[]
+train!(EC, training_c; ref_eval=ref_E, CAEC=true) # try CAEC=false !!!
-extrapolated = ComplexF64[]
+extrapolate!(EC, extrapolating_c, precalculated_exact_E = exact_ref)
-training_vecs = Vector{ComplexF64}[]
 
-for c in training_c
+exportCSV(EC, "temp/dis_HO_B2R.csv")
-    print("Training for c = $c: ")
+plot(EC, "temp/dis_HO_B2R.pdf")
-    H = Ha + c .* Vb
-    evals, evecs = eigs(H, nev=3, ncv=24, which=:SR, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
-
-    global current_E = nearest(evals, current_E)
-    println(current_E)
-    push!(training, current_E)
-    push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
-end
-
-# CA-EC
-training_vecs = vcat(training_vecs, conj(training_vecs))
-
-EC_basis = hcat(training_vecs...)
-N_EC = transpose(EC_basis) * EC_basis
-Ha_EC = transpose(EC_basis) * Ha * EC_basis
-Vb_EC = transpose(EC_basis) * Vb * EC_basis
-
-current_E = -0.3005521915662689 - 0.13612069020686351im
-
-for c in extrapolating_c
-    print("Extrapolating for c = $c: ")
-    H = Ha + c .* Vb
-    evals, evecs = eigs(H, nev=3, ncv=24, which=:SR, maxiter=5000, tol=1e-5, ritzvec=true, check=1)
-
-    global current_E = nearest(evals, current_E)
-    println(current_E)
-    push!(exact, current_E)
-
-    # extrapolation
-    H_EC = Ha_EC + c .* Vb_EC
-    evals = eigvals(H_EC, N_EC)
-    push!(extrapolated, nearest(evals, current_E))
-end
-
-# exportCSV("temp/dis_HO_B2R.csv", (training, exact, extrapolated), ("training", "exact", "extrapolated"))
-
-scatter(real.(training),imag.(training), label="training")
-scatter!(real.(exact),imag.(exact), label="exact")
-scatter!(real.(extrapolated),imag.(extrapolated), label="extrapolated")
-savefig("temp/dis_HO_B2R.pdf")

calculations/ACCC.jl

@@ -0,0 +1,50 @@
+using Roots, LinearAlgebra, Plots
+
+include("../EC.jl")
+include("../common.jl")
+include("../p_space.jl")
+
+μ = 0.5
+V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q)) # ResonanceEC: Eq. (20)
+
+# determining c0 with EC
+temp_c = range(1.1, 0.9, 3)
+p, w = get_mesh([0, 8], [256])
+H0 = get_T_matrix(p, μ)
+V = get_V_matrix(V_system(1), p, w)
+EC = affine_EC(H0, V, w)
+train!(EC, temp_c; ref_eval=-0.2, CAEC=false, verbose=false)
+quick_extrapolate(c) = minimum(abs2, get_extrapolated_evals(EC.H0_EC, EC.H1_EC, EC.N_EC, c, 0))
+c0 = find_zero(quick_extrapolate, 0.85)
+
+# Calculation of training and extrapolating E
+training_c = range(1.2, 0.9, 9) # original: range(1.35, 0.9, 5)
+training_E = [quick_pole_E(V_system(c)) for c in training_c]
+training_k = alt_sqrt.(2μ .* training_E)
+
+extrapolating_c = range(0.78, 0.45, 7) # original: range(0.75, 0.40, 8)
+exact_E = [quick_pole_E(V_system(c)) for c in extrapolating_c]
+
+order::Int = ceil((length(training_c) - 1) / 2) # order of the Pade approximant
+
+# Solve coefficients as a linear system
+M_left_element(c, i) = alt_sqrt(c - c0)^i
+M_left = M_left_element.(training_c, (0:order)')
+M_right = -training_k .* M_left[:, 2:end] # remove the first column
+M = hcat(M_left, M_right) # M = [M_left | M_right]
+sol = M \ training_k
+a = sol[1:order+1]
+b = [1; sol[order+2:end]]
+
+# Pade approximant
+polynomial(a, c) = sum(i -> a[i+1] * alt_sqrt(c - c0)^i, 0:order)
+pade_approx(c) = polynomial(a, c) / polynomial(b, c)
+
+# Extrapolate
+extrapolated_k = pade_approx.([training_c; extrapolating_c])
+extrapolated_E = (extrapolated_k .^ 2) / (2μ)
+
+# Plotting
+scatter(real.(training_E), imag.(training_E), label="training")
+scatter!(real.(exact_E), imag.(exact_E), label="exact")
+scatter!(real.(extrapolated_E), imag.(extrapolated_E), label="extrapolated", m=:star5)

calculations/B2R_Berggren_poles.jl

@@ -1,12 +1,12 @@
-using Plots
+include("../EC.jl")
-include("../helper.jl")
+include("../common.jl")
 include("../p_space.jl")
 
 # contour
 p, w = get_mesh([0, 0.4 - 0.15im, 0.8, 6], [128, 128, 128])
 
-# ResonanceEC: Eq. (20)
+μ = 0.5
-V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q))
+V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q)) # ResonanceEC: Eq. (20)
 
 # generating a Berggren basis with a pole using the same system
 basis_c = 0.6
@@ -16,48 +16,18 @@ N_berg = sqrt.(diag(transpose(berg_basis .* w) * berg_basis))
 berg_basis = berg_basis ./ transpose(N_berg)
 berg_basis_w = berg_basis .* w
 
-training_points = range(1.1, 0.9, 5) # original: range(1.35, 0.9, 5)
+H0 = transpose(berg_basis_w) * get_T_matrix(p, μ) * berg_basis
+V = transpose(berg_basis_w) * get_V_matrix(V_system(1), p, w) * berg_basis
 
-training_E = Vector{ComplexF64}(undef, length(training_points))
+training_c = range(1.1, 0.9, 5) # original: range(1.35, 0.9, 5)
-EC_basis = Matrix{ComplexF64}(undef, length(p), length(training_points))
+extrapolating_c = range(0.78, 0.45, 7) # original: range(0.75, 0.40, 8)
 
-# training
+training_ref = -0.26
-for (j, c) in enumerate(training_points)
+extrapolating_ref = [quick_pole_E(V_system(c)) for c in extrapolating_c]
-    H = get_H_matrix(V_system(c), p, w)
-    H_berg = transpose(berg_basis_w) * H * berg_basis
-    evals, evecs = eigen(H_berg)
-    i = argmin(real.(evals))
-#    i = identify_pole_i(basis_p, evals)
-    training_E[j] = evals[i]
-    EC_basis[:, j] = evecs[:, i]
-end
 
-EC_basis = hcat(EC_basis, conj.(EC_basis)) # CA-EC
+EC = affine_EC(H0, V)
-N_EC = transpose(EC_basis) * EC_basis
+train!(EC, training_c; ref_eval=training_ref, CAEC=true)
+extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)
 
-extrapolate_points = range(0.78, 0.45, 7) # original: range(0.75, 0.40, 8)
+exportCSV(EC, "temp/2b_GSM_B2R.csv")
-
+plot(EC, "temp/2b_GSM_B2R.pdf"; basis_points=basis_E, xlims=(0, 0.3), ylims=(-0.120, 0.020))
-exact_E = Vector{ComplexF64}(undef, length(extrapolate_points))
-extrapolate_E = Vector{ComplexF64}(undef, length(extrapolate_points))
-
-# extrapolating
-for (j, c) in enumerate(extrapolate_points)
-    exact_E[j] = quick_pole_E(V_system(c))
-
-    H = get_H_matrix(V_system(c), p, w)
-    H_berg = transpose(berg_basis_w) * H * berg_basis
-    H_EC = transpose(EC_basis) * H_berg * EC_basis
-    evals = eigvals(H_EC, N_EC)
-    i = argmin(abs.(evals .- exact_E[j]))
-    extrapolate_E[j] = evals[i]
-end
-
-exportCSV("temp/2b_GSM_B2R.csv", (training_E, exact_E, extrapolate_E), ("training", "exact", "extrapolated"))
-
-scatter(real.(training_E), imag.(training_E), label="training")
-scatter!(real.(exact_E), imag.(exact_E), label="exact")
-scatter!(real.(extrapolate_E), imag.(extrapolate_E), label="extrapolated")
-scatter!(real.(basis_E), imag.(basis_E), m=:x, label="Berggren basis")
-xlims!(0,0.3)
-ylims!(-0.120,0.020)
-savefig("temp/2b_GSM_B2R.pdf")

calculations/B2R_comparison.jl

@@ -1,51 +1,30 @@
-using Plots
+include("../EC.jl")
+include("../common.jl")
 include("../p_space.jl")
 
 berggren_mesh = get_mesh([0, 0.4 - 0.15im, 0.8, 6], [128, 128, 128])
 csm_mesh = get_mesh([0, 8 - 3im], [512])
 
-for mesh in (berggren_mesh, csm_mesh)
+for (mesh, name) in zip((berggren_mesh, csm_mesh), ("beggren", "csm"))
     p, w = mesh
     mesh_E = p.*p ./ (2*0.5)
 
-    # ResonanceEC: Eq. (20)
+    μ = 0.5
-    V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q))
+    V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q)) # ResonanceEC: Eq. (20)
     
-    training_points = range(1.1, 0.9, 5) # original: range(1.35, 0.9, 5)
+    H0 = get_T_matrix(p, μ)
-    training_E = Vector{ComplexF64}(undef, length(training_points))
+    V = get_V_matrix(V_system(1), p, w)
-    EC_basis = Matrix{ComplexF64}(undef, length(p), length(training_points))
     
-    for (j, c) in enumerate(training_points)
+    training_c = range(1.1, 0.9, 5) # original: range(1.35, 0.9, 5)
-        evals, evecs = eigen(get_H_matrix(V_system(c), p, w))
+    extrapolating_c = range(0.78, 0.45, 7) # original: range(0.75, 0.40, 8)
-        i = identify_pole_i(p, evals)
+
-        training_E[j] = evals[i]
+    training_ref = [quick_pole_E(V_system(c)) for c in training_c]
-        EC_basis[:, j] = evecs[:, i]
+    exact_E = [quick_pole_E(V_system(c)) for c in extrapolating_c]
-    end
+
-
+    EC = affine_EC(H0, V, w)
-    EC_basis = hcat(EC_basis, conj.(EC_basis)) # CA-EC
+    train!(EC, training_c; ref_eval=training_ref, CAEC=true)
-    EC_basis_w = EC_basis .* w
+    extrapolate!(EC, extrapolating_c; precalculated_exact_E=exact_E)
-    N_EC = transpose(EC_basis_w) * EC_basis
+    
-
+    #exportCSV(EC, "temp/2b_comparison_$name.csv")
-    extrapolate_points = range(0.78, 0.45, 7) # original: range(0.75, 0.40, 8)
+    plot(EC, "temp/2b_comparison_$name.pdf"; basis_contour=mesh_E, xlims=(-0.3,0.3), ylims=(-0.120,0.020))
-
-    exact_E = Vector{ComplexF64}(undef, length(extrapolate_points))
-    extrapolate_E = Vector{ComplexF64}(undef, length(extrapolate_points))
-
-    for (j, c) in enumerate(extrapolate_points)
-        exact_E[j] = quick_pole_E(V_system(c))
-
-        H = get_H_matrix(V_system(c), p, w)
-        H_EC = transpose(EC_basis_w) * H * EC_basis
-        evals = eigvals(H_EC, N_EC)
-        i = argmin(abs.(evals .- exact_E[j]))
-        extrapolate_E[j] = evals[i]
-    end
-
-    scatter(real.(training_E), imag.(training_E), label="training")
-    scatter!(real.(exact_E), imag.(exact_E), label="exact")
-    scatter!(real.(extrapolate_E), imag.(extrapolate_E), label="extrapolated")
-    plot!(real.(mesh_E), imag.(mesh_E), label="contour")
-    xlims!(-0.3,0.3)
-    ylims!(-0.120,0.020)
-    savefig("temp/" * string(rand(UInt16)) * ".pdf")
 end

calculations/PMM.py

@@ -0,0 +1,81 @@
+#%%
+import pandas as pd
+import torch
+import numpy as np
+
+#%%
+df = pd.read_csv('../temp/2body_data.csv').sort_values(by='c')
+df['E'] = df['re_E'] + 1j * df['im_E']
+train_data = df[df['re_E'] < 0]
+target_data = df[df['re_E'] > 0]
+
+train_cs = train_data['c'].to_numpy()
+train_Es = torch.tensor(train_data['E'].to_numpy(), dtype=torch.complex128)
+
+#%%
+# hyperparameters
+N = 9
+
+# initialize random Hamiltonians
+H0 = torch.randn(N, N, dtype=torch.complex128)
+H0 = (H0 + torch.transpose(H0, 0, 1)).requires_grad_() # symmetric
+H1 = torch.randn(N, N, dtype=torch.complex128)
+H1 = (H1 + torch.transpose(H1, 0, 1)).requires_grad_() # symmetric
+
+#%%
+# training
+
+# generate a set of c values to follow by subdividing the training cs
+subdivisions = 3
+c_steps = np.concatenate([np.linspace(start, stop, subdivisions, endpoint=False) for (start, stop) in zip(train_cs, train_cs[1:])])
+c_steps = np.append(c_steps, train_cs[-1])
+
+lr = 0.05
+epochs = 100000
+for epoch in range(epochs):
+    Es = torch.empty(len(train_data), dtype=torch.complex128)
+    current_E = 0.0 # start at the threshold
+    for c in c_steps:
+        H = H0 + c * H1
+        evals = torch.linalg.eigvals(H)
+        current_E = evals[torch.argmin(torch.abs(evals - current_E))]
+        if np.any(c == train_cs):
+            index = np.where(c == train_cs)[0][0]
+            Es[index] = current_E
+
+    loss = ((Es - train_Es).abs() ** 2).sum()
+
+    if epoch % 1000 == 0:
+        print(f"Training {(epoch+1)/epochs:.1%} \t Loss: {loss}")
+
+    if H0.grad is not None:
+        H0.grad.zero_()
+    if H1.grad is not None:
+        H1.grad.zero_()
+    loss.backward()
+
+    with torch.no_grad():
+        H0 -= lr * H0.grad
+        H1 -= lr * H1.grad
+
+# %%
+# evaluate for all points
+all_c = torch.tensor(df['c'].values, dtype=torch.float64)
+exact_E = torch.tensor(df['E'].values, dtype=torch.complex128)
+pred_Es = torch.empty(len(df), dtype=torch.complex128)
+with torch.no_grad():
+    for (index, (c, E)) in enumerate(zip(all_c, exact_E)):
+        H = H0 + c * H1
+        evals = torch.linalg.eigvals(H)
+        i = torch.argmin(torch.abs(evals - E)) # TODO: more robust way to identify the eigenvector
+        pred_Es[index]= evals[i]
+
+# %%
+# plot the results
+import matplotlib.pyplot as plt
+plt.scatter(train_data['re_E'], train_data['im_E'], label='training')
+plt.scatter(target_data['re_E'], target_data['im_E'], label='target')
+plt.scatter(pred_Es.real, pred_Es.imag, marker='x', label='predicted')
+plt.legend()
+
+# %%

calculations/R2R_Berggren.jl

@@ -1,4 +1,5 @@
-using Plots
+include("../EC.jl")
+include("../common.jl")
 include("../p_space.jl")
 
 # contour
@@ -7,42 +8,21 @@ subdivisions = [128, 128, 128]
 p, w = get_mesh(vertices, subdivisions)
 mesh_E = p.*p ./ (2*0.5)
 
-# ResonanceEC: Eq. (20)
+μ = 0.5
-V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q))
+V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q)) # ResonanceEC: Eq. (20)
 
-training_points = range(0.75, 0.45, 5)
+H0 = get_T_matrix(p, μ)
-training_E = Vector{ComplexF64}(undef, length(training_points))
+V = get_V_matrix(V_system(1), p, w)
-EC_basis = Matrix{ComplexF64}(undef, length(p), length(training_points))
 
-for (j, c) in enumerate(training_points)
+training_c = range(0.75, 0.45, 5)
-    evals, evecs = eigen(get_H_matrix(V_system(c), p, w))
+extrapolating_c = range(0.40, 0.25, 5)
-    i = identify_pole_i(p, evals)
-    training_E[j] = evals[i]
-    EC_basis[:, j] = evecs[:, i]
-end
 
-extrapolate_points = range(0.40, 0.25, 5)
+training_ref = [quick_pole_E(V_system(c)) for c in training_c]
-ref_E = 0.2 - 0.1im
+exact_E = [quick_pole_E(V_system(c)) for c in extrapolating_c]
 
-exact_E = Vector{ComplexF64}(undef, length(extrapolate_points))
+EC = affine_EC(H0, V, w)
-extrapolate_E = Vector{ComplexF64}(undef, length(extrapolate_points))
+train!(EC, training_c; ref_eval=training_ref, CAEC=false)
+extrapolate!(EC, extrapolating_c; precalculated_exact_E=exact_E)
 
-EC_basis_w = EC_basis .* w
+#exportCSV(EC, "temp/2b_R2R.csv")
-N_EC = transpose(EC_basis_w) * EC_basis
+plot(EC, "temp/2b_R2R.pdf"; basis_contour=mesh_E, xlims=(0, 1))
-
-for (j, c) in enumerate(extrapolate_points)
-    exact_E[j] = quick_pole_E(V_system(c))
-
-    H = get_H_matrix(V_system(c), p, w)
-    H_EC = transpose(EC_basis_w) * H * EC_basis
-    evals = eigvals(H_EC, N_EC)
-    i = argmin(abs.(evals .- ref_E))
-    global ref_E = evals[i]
-    extrapolate_E[j] = evals[i]
-end
-
-scatter(real.(training_E), imag.(training_E), label="training")
-scatter!(real.(exact_E), imag.(exact_E), label="exact")
-scatter!(real.(extrapolate_E), imag.(extrapolate_E), label="extrapolated")
-plot!(real.(mesh_E), imag.(mesh_E), label="contour")
-xlims!(0,1)

calculations/R2R_Berggren_poles.jl

@@ -1,66 +1,33 @@
-using Plots
+include("../EC.jl")
-include("../helper.jl")
+include("../common.jl")
 include("../p_space.jl")
 
 # contour
-p, w = get_mesh([0, 0.4 - 0.08im, 0.8, 6], [128, 128, 128])
+p, w = get_mesh([0, 0.4 - 0.15im, 0.8, 6], [128, 128, 128])
-contour_E = p.^2
 
-# ResonanceEC: Eq. (20)
+μ = 0.5
-V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q))
+V_system(c) = (p, q) -> c*(-5*g0(sqrt(3), p, q) + 2*g0(sqrt(10), p, q)) # ResonanceEC: Eq. (20)
 
 # generating a Berggren basis with a pole using the same system
 basis_c = 0.6
 basis_E, berg_basis = eigen(get_H_matrix(V_system(basis_c), p, w); permute=false, scale=false)
-pole_E = quick_pole_E(V_system(basis_c)) # basis only has 1 pole
 basis_p = sqrt.(basis_E)
 N_berg = sqrt.(diag(transpose(berg_basis .* w) * berg_basis))
 berg_basis = berg_basis ./ transpose(N_berg)
 berg_basis_w = berg_basis .* w
 
-training_points = range(0.79, 0.66, 4) # original: range(1.35, 0.9, 5)
+H0 = transpose(berg_basis_w) * get_T_matrix(p, μ) * berg_basis
+V = transpose(berg_basis_w) * get_V_matrix(V_system(1), p, w) * berg_basis
 
-training_E = Vector{ComplexF64}(undef, length(training_points))
+training_c = range(0.79, 0.66, 4) # original: range(1.35, 0.9, 5)
-EC_basis = Matrix{ComplexF64}(undef, length(p), length(training_points))
+extrapolating_c = range(0.62, 0.40, 6) # original: range(0.75, 0.40, 8)
 
-# training
+training_ref = [quick_pole_E(V_system(c)) for c in training_c]
-for (j, c) in enumerate(training_points)
+extrapolating_ref = [quick_pole_E(V_system(c)) for c in extrapolating_c]
-    H = get_H_matrix(V_system(c), p, w)
-    H_berg = transpose(berg_basis_w) * H * berg_basis
-    evals, evecs = eigen(H_berg)
-    i = identify_pole_i(basis_p, evals)
-    training_E[j] = evals[i]
-    EC_basis[:, j] = evecs[:, i]
-end
 
-N_EC = transpose(EC_basis) * EC_basis
+EC = affine_EC(H0, V)
+train!(EC, training_c; ref_eval=training_ref, CAEC=false)
+extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)
 
-extrapolate_points = range(0.62, 0.40, 6) # original: range(0.75, 0.40, 8)
+exportCSV(EC, "temp/2b_GSM_R2R.csv")
-
+plot(EC, "temp/2b_GSM_R2R.pdf"; basis_points=basis_E, xlims=(0, 0.3), ylims=(-0.120, 0.020))
-exact_E = Vector{ComplexF64}(undef, length(extrapolate_points))
-extrapolate_E = Vector{ComplexF64}(undef, length(extrapolate_points))
-
-# extrapolating
-for (j, c) in enumerate(extrapolate_points)
-    exact_E[j] = quick_pole_E(V_system(c))
-
-    H = get_H_matrix(V_system(c), p, w)
-    H_berg = transpose(berg_basis_w) * H * berg_basis
-    H_EC = transpose(EC_basis) * H_berg * EC_basis
-    evals = eigvals(H_EC, N_EC)
-    i = argmin(abs.(evals .- exact_E[j]))
-    extrapolate_E[j] = evals[i]
-end
-
-exportCSV("temp/2b_GSM_R2R.csv", (training_E, exact_E, extrapolate_E, [pole_E]), ("training", "exact", "extrapolated", "basis"))
-
-contour_E_export = contour_E[real.(contour_E) .< 1] # to trim unnecessary data outside axis limits
-exportCSV("temp/2b_GSM_R2R_contour.csv", contour_E_export)
-
-scatter(real.(training_E), imag.(training_E), label="training")
-scatter!(real.(exact_E), imag.(exact_E), label="exact")
-scatter!(real.(extrapolate_E), imag.(extrapolate_E), label="extrapolated")
-scatter!(real.(basis_E), imag.(basis_E), m=:x, label="Berggren basis")
-xlims!(0,0.3)
-ylims!(-0.120,0.020)
-savefig("temp/2b_GSM_R2R.pdf")

calculations/XZ/2body_HO.jl

@@ -0,0 +1,39 @@
+using Plots
+
+include("../../EC.jl")
+include("../../ho_basis.jl")
+include("../../p_space.jl")
+
+angle = 0.25 * pi # DOESN'T WORK WITHOUT ROTATION
+μω_gen = 0.5 * exp(-2im * angle)
+μ = 0.5
+l = 0
+V1 = -5
+R1 = sqrt(3)
+V2 = 2
+R2 = sqrt(10)
+n_max = 15
+
+ns = collect(0:n_max)
+ls = fill(l, n_max + 1)
+
+T = get_sp_T_matrix(ns, ls; μω_gen=μω_gen, μ=μ)
+V = V1 .* V_Gaussian.(R1, l, ns, transpose(ns); μω_gen=μω_gen) + V2 .* V_Gaussian.(R2, l, ns, transpose(ns); μω_gen=μω_gen)
+
+n_EC = 8
+train_cs = (0.7 .+ 0.05 * randn(n_EC)) - 1im * (0.2 .+ 0.05 * randn(n_EC))
+target_cs = [0.5]
+
+near_E = 0.2 + 0.2im
+exact_E = [0.20845136860234303 - 0.07100640993695649im]
+
+EC = affine_EC(T, V; ensemble_size=32)
+train!(EC, train_cs; ref_eval=near_E, CAEC=false)
+extrapolate!(EC, target_cs; precalculated_exact_E=exact_E)
+
+plot(EC; xlims=(0,0.3), ylims=(-0.3,0.3))
+hline!([0], color=:red, label="continuum")
+xlabel!("Re(E)")
+ylabel!("Im(E)")
+plot!(legend=:bottomleft)
+savefig("temp/2b_HO_XZ.pdf")

calculations/XZ/2body_p_space.jl

@@ -0,0 +1,50 @@
+using Plots
+
+include("../../EC.jl")
+include("../../ho_basis.jl")
+include("../../p_space.jl")
+
+# paramters of the system
+
+angle = 0.0
+μ = 0.5
+l = 0
+V1 = -5
+R1 = sqrt(3)
+V2 = 2
+R2 = sqrt(10)
+
+n_EC = 8
+train_cs = (0.7 .+ 0.03 * randn(n_EC)) - 1im * (0.2 .+ 0.03 * randn(n_EC))
+near_E = 0.2 + 0.2im
+
+target_c = 0.5
+exact_E = 0.20845136860234303 - 0.07100640993695649im
+
+vertices = [0, 4 * exp(-1im * angle)]
+subdivisions = [256]
+ks, ws = get_mesh(vertices, subdivisions)
+
+V_of_r(r) = V1 * exp(-r^2 / R1^2) + V2 * exp(-r^2 / R2^2)
+V_mat_elem(k, kp) = Vl_mat_elem(V_of_r, l, k, kp; atol=10^-5, maxevals=10^5, R_cutoff=16)
+V = get_V_matrix(V_mat_elem, ks, ws)
+T = get_T_matrix(ks, μ)
+
+EC_p_space = affine_EC(T, V)
+train!(EC_p_space, train_cs; ref_eval=near_E, CAEC=false)
+extrapolate!(EC_p_space, [target_c]; precalculated_exact_E=[exact_E])
+
+# Plotting
+
+theme(:dark) # Set the global theme to dark
+
+scatter([real(exact_E)], [imag(exact_E)], label="exact", marker=:circle, markercolor=:white, bg = :black) # black background
+scatter!(real.(EC_p_space.training_E), imag.(EC_p_space.training_E), label="training", marker=:circle, color=:blue)
+scatter!(real.(EC_p_space.extrapolated_E), imag.(EC_p_space.extrapolated_E), label="extrapolated", marker=:x, color=:green)
+hline!([0], color=:red, label="continuum")
+plot!(legend=:bottomleft)
+xlabel!("Re(E)")
+ylabel!("Im(E)")
+xlims!(0, 0.3)
+ylims!(-0.3, 0.3)
+savefig("temp/2body_p_space.pdf")

calculations/XZ/3body_HO.jl

@@ -0,0 +1,36 @@
+include("../../ho_basis.jl")
+include("../../EC.jl")
+
+V_of_r(r) = 2 * exp(-(r-3)^2 / (1.5)^2)
+Λ = 0
+m = 1.0
+
+ϕ = 0.1 # DOESN'T WORK WITHOUT ROTATION
+μω_global = 0.5 * exp(-2im * ϕ)
+E_max = 40
+
+H0 = get_3b_H_matrix(jacobi, V_of_r, μω_global, E_max, Λ, m, true, true)
+
+# Vp = perturbation to make the state artificially bound
+Vp_of_r(r) = -exp(-(r/3)^2)
+@time "Vp" Vp = get_3b_H_matrix(jacobi, Vp_of_r, μω_global, E_max, Λ, m, false, true)
+
+training_ref = 2 + 0.5im
+
+exact_E = [4.076642792419057-0.012998408352259658im,
+    3.6129849325287-0.007397677539402868im,
+    3.145212908643357-0.0038660337822150753im,	
+    2.6729225739451596-0.0021090370393881063im,	
+    2.196385760253282-0.0010430088245526555im,	
+    1.7162659936896967-0.0004515351140200029,	
+    1.2329926791785895-0.00017698044022813525im]
+
+training_c = [0.6 - 0.16im] .+ 0.04 .* (randn(8) .+ 0.5im * randn(8))
+extrapolating_c = 0.0 : 0.2 : 1.2
+
+EC = affine_EC(H0, Vp)
+train!(EC, training_c; ref_eval=training_ref, CAEC=false)
+extrapolate!(EC, extrapolating_c; precalculated_exact_E=exact_E)
+
+exportCSV(EC, "temp/3b_HO_XZ.csv")
+plot(EC, "temp/3b_HO_XZ.pdf")

calculations/XZ/3body_p_space.jl

@@ -0,0 +1,45 @@
+include("../../p_space.jl")
+include("../../EC.jl")
+
+using Arpack
+
+# target = 4.0766890719636875 - 0.012758927741074495im
+
+Λ = 0
+m = 1.0
+V_of_r(r) =  2 * exp(-(r-3)^2 / (1.5)^2)
+
+vertices = [0, 2 - 0.2im, 3, 4] # TODO: real contour instead of Berggren basis
+subdivisions = [15, 10, 10]
+jmax = 4
+
+E_max = 40
+μω_global = 0.5
+
+@time "H0" H0, _ = get_3b_H_matrix(jacobi, V_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m, true, true)
+
+# Vp = perturbation to make the state artificially bound
+Vp_of_r(r) = -exp(-(r/3)^2)
+@time "Vp" Vp, _ = get_3b_H_matrix(jacobi, Vp_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m, false, true)
+
+training_ref = 2 + 0.5im
+
+exact_E = [4.076642792419057-0.012998408352259658im,
+    3.6129849325287-0.007397677539402868im,
+    3.145212908643357-0.0038660337822150753im,	
+    2.6729225739451596-0.0021090370393881063im,	
+    2.196385760253282-0.0010430088245526555im,	
+    1.7162659936896967-0.0004515351140200029,	
+    1.2329926791785895-0.00017698044022813525im]
+
+training_c = [-1.5 - 0.5im] .+ (randn(8) .+ 0.05im * randn(8))
+extrapolating_c = 0.0 : 0.2 : 1.2
+
+EC = affine_EC(H0, Vp)
+train!(EC, training_c; ref_eval=training_ref, CAEC=false)
+extrapolate!(EC, extrapolating_c; precalculated_exact_E=exact_E)
+
+exportCSV(EC, "temp/3b_p_space_XZ.csv")
+plot(EC, "temp/3b_p_space_XZ.pdf")
+
+# Results: training points are all over the place, and extrapolated values are garbage.

calculations/XZ/p_space_vs_HO.jl

@@ -0,0 +1,71 @@
+using Plots
+
+include("../../EC.jl")
+include("../../ho_basis.jl")
+include("../../p_space.jl")
+
+# paramters of the system
+
+angle = 0.0 * pi
+μ = 0.5
+l = 0
+V1 = -5
+R1 = sqrt(3)
+V2 = 2
+R2 = sqrt(10)
+
+n_EC = 8
+train_cs = (0.7 .+ 0.03 * randn(n_EC)) - 1im * (0.2 .+ 0.03 * randn(n_EC))
+near_E = 0.2 + 0.2im
+
+target_c = 0.5
+exact_E = 0.20845136860234303 - 0.07100640993695649im
+
+# HO basis
+
+global EC_HO
+begin
+    println("HO basis calculation")
+    μω_gen = 0.5 * exp(-1im * angle)
+    n_max = 40
+    ns = collect(0:n_max)
+    ls = fill(l, n_max + 1)
+
+    T = get_sp_T_matrix(ns, ls; μω_gen=μω_gen, μ=μ)
+    V = V1 .* V_Gaussian.(R1, l, ns, transpose(ns); μω_gen=μω_gen) + V2 .* V_Gaussian.(R2, l, ns, transpose(ns); μω_gen=μω_gen)
+
+    global EC_HO = affine_EC(T, V)
+    train!(EC_HO, train_cs; ref_eval=near_E, CAEC=false)
+    extrapolate!(EC_HO, [target_c]; precalculated_exact_E=[exact_E])
+end
+
+# p-space
+
+global EC_p_space
+begin
+    println("p-space calculation")
+    vertices = [0, 4 * exp(-1im * angle)]
+    subdivisions = [256]
+    ks, ws = get_mesh(vertices, subdivisions)
+
+    V_of_r(r) = V1 * exp(-r^2 / R1^2) + V2 * exp(-r^2 / R2^2)
+    V_mat_elem(k, kp) = Vl_mat_elem(V_of_r, l, k, kp; atol=10^-5, maxevals=10^5, R_cutoff=16)
+    V = get_V_matrix(V_mat_elem, ks, ws)
+    T = get_T_matrix(ks, μ)
+
+    global EC_p_space = affine_EC(T, V)
+    train!(EC_p_space, train_cs; ref_eval=near_E, CAEC=false)
+    extrapolate!(EC_p_space, [target_c]; precalculated_exact_E=[exact_E])
+end
+
+# Plotting
+
+scatter([real(exact_E)], [imag(exact_E)], label="Exact", marker=:circle, markercolor=:white)
+scatter!(real.(EC_HO.training_E), imag.(EC_HO.training_E), label="HO basis training", marker=:circle, color=:blue)
+scatter!(real.(EC_HO.extrapolated_E), imag.(EC_HO.extrapolated_E), label="HO basis extrapolated", marker=:x, color=:blue)
+scatter!(real.(EC_p_space.training_E), imag.(EC_p_space.training_E), label="p-space training", marker=:circle, color=:red)
+scatter!(real.(EC_p_space.extrapolated_E), imag.(EC_p_space.extrapolated_E), label="p-space extrapolated", marker=:x, color=:red)
+plot!(legend=:bottomleft)
+xlabel!("Re(E)")
+ylabel!("Im(E)")
+savefig("temp/2b_p_space_vs_HO.pdf")

calculations/XZ_technique.jl

@@ -1,55 +0,0 @@
-using LinearAlgebra, Plots
-include("../ho_basis.jl")
-include("../p_space.jl")
-
-μω_gen = 0.5 * exp(-1im * 0.47 * pi)
-μ = 0.5
-l = 0
-V1 = -5
-R1 = sqrt(3)
-V2 = 2
-R2 = sqrt(10)
-n_max = 15
-
-ns = collect(0:n_max)
-ls = fill(l, n_max + 1)
-
-T = get_sp_T_matrix(ns, ls; μω_gen=μω_gen, μ=μ)
-V = V1 .* V_Gaussian.(R1, l, ns, transpose(ns); μω_gen=μω_gen) + V2 .* V_Gaussian.(R2, l, ns, transpose(ns); μω_gen=μω_gen)
-
-n_EC = 8
-train_cs = (0.7 .+ 0.05 * randn(n_EC)) - 1im * (0.2 .+ 0.05 * randn(n_EC))
-target_cs = range(0.77, 0.22, 6)
-
-train_E = zeros(ComplexF64, n_EC)
-EC_basis = zeros(ComplexF64, (n_max + 1, length(train_cs)))
-exact_E = zeros(ComplexF64, length(target_cs))
-extrapolate_E = similar(exact_E)
-
-near_E = 0.2 + 0.2im
-
-for (j, c) in enumerate(train_cs)
-    H = T + c .* V
-    evals, evecs = eigen(H)
-    i = argmin(abs.(evals .- near_E))
-    train_E[j] = evals[i]
-    EC_basis[:, j] = evecs[:, i]
-end
-
-N_EC = transpose(EC_basis) * EC_basis
-
-for (j, c) in enumerate(target_cs)
-    exact_E[j] = quick_pole_E((p, q) -> c*(V1*g0(R1, p, q) + V2*g0(R2, p, q)), μ; cs_angle=0.5)
-
-    H = T + c .* V
-    H_EC = transpose(EC_basis) * H * EC_basis
-    evals = eigvals(H_EC, N_EC)
-    i = argmin(abs.(evals .- exact_E[j]))
-    extrapolate_E[j] = evals[i]
-end
-
-scatter(real.(train_E), imag.(train_E), label="training")
-scatter!(real.(exact_E), imag.(exact_E), label="exact")
-scatter!(real.(extrapolate_E), imag.(extrapolate_E), label="extrapolated")
-xlims!(-0.2,0.3)
-ylims!(-0.3,0.3)

common.jl

@@ -0,0 +1,106 @@
+using LinearAlgebra, DelimitedFiles, SparseArrays
+
+@enum coordinate_system jacobi src
+
+"Square root function with the branch cut along the postive imaginary axis"
+alt_sqrt(x::Number)::ComplexF64 = sqrt(im * x) / sqrt(im)
+
+"Sum over array while minimizing catastrophic cancellation as much as possible"
+function better_sum(arr::Array{T}) where T<:Real
+    pos_arr = arr[arr .> 0]
+    neg_arr = arr[arr .< 0]
+
+    sort!(pos_arr)
+    sort!(neg_arr, rev=true)
+
+    return sum(pos_arr) + sum(neg_arr)
+end
+
+better_sum(arr::Array{ComplexF64}) = better_sum(real.(arr)) + 1im * better_sum(imag.(arr))
+
+"The triangle inequality for angular momenta"
+triangle_ineq(l1, l2, L) = abs(l1 - l2) ≤ L && L ≤ (l1 + l2)
+
+"Index of the nearest value in a list to a given reference point"
+nearestIndex(list::Array, ref) = argmin(norm.(list .- ref))
+
+"Nearest value in a list to a given reference point"
+nearest(list::Array, ref) = list[nearestIndex(list, ref)]
+
+"Simple implementation of the Kronecker sum"
+function kron_sum(A::AbstractMatrix, B::AbstractMatrix)
+    @assert size(A, 1) == size(A, 2) && size(B, 1) == size(B, 2) "Matrices should be square"
+    return kron(A, I(size(B, 1))) + kron(I(size(A, 1)), B)
+end
+
+"Flattened vector version of Iterators.product(...) with index hierachy reversed -- leftmost index has the highest hierachy"
+iter_prod(args...) = reverse.(collect(Iterators.product(reverse(args)...))[:])
+
+"Export CSV data for a scatter plot taking in data as a list of complex vectors (x=Re, y=Im), and a list of corresponding labels, typically used for EC results"
+function exportCSV(file::String, data, labels=nothing)
+    columns = ["x" "y" "label"]
+
+    if isnothing(labels)
+        columns[end] = ""
+        labels = fill("", length(data))
+    end
+    
+    open(file, "w") do f
+        writedlm(f, columns)
+        for (d, label) in zip(data, labels)
+            l = fill(label, length(d))
+            writedlm(f, hcat(reim(d)..., l))
+        end
+    end
+end
+
+"In-place c-orthogonalization via (modified) Gram-Schmidt. Only significant vectors are returned (c-normalized).
+The number of significant vectors to return are determined by the original singular values (compared to the threshold)."
+function gram_schmidt!(vecs::Vector{Vector{T}}, ws=ones(length(vecs[1])), threshold=1e-5; verbose=false) where T<: Number
+    c_product(i, j) = sum(vecs[i] .* ws .* vecs[j])
+    norm(i) = c_product(i, i)
+    proj(i, j) = (c_product(i, j) / norm(i)) .* vecs[i] # component of vec[i] in vec[j]
+
+    # initial normalization
+    for i in eachindex(vecs)
+        vecs[i] ./= sqrt(norm(i))
+    end
+
+    verbose && println("Absolute singular values = $(round.(c_singular_values(vecs, ws); sigdigits=1))")
+    target_dim = c_rank(vecs, ws, threshold)
+    verbose && println("Target dimensionality = $target_dim")
+
+    selected_vecs_i = Integer[]
+
+    while length(selected_vecs_i) < target_dim
+        i = argmax(i -> abs(norm(i)), setdiff(eachindex(vecs), selected_vecs_i)) # find the largest vector from the remaining
+        push!(selected_vecs_i, i)
+        for j in setdiff(eachindex(vecs), selected_vecs_i)
+            vecs[j] .-= proj(i, j)
+        end
+    end
+    
+    verbose && println("Absolute norms of selected vectors = $(round.(selected_vecs_i .|> norm .|> abs; sigdigits=1))")
+    verbose && println("Absolute norms of dropped vectors = $(round.(setdiff(eachindex(vecs), selected_vecs_i) .|> norm .|> abs; sigdigits=1))")
+
+    # final normalization
+    for i in selected_vecs_i
+        vecs[i] ./= sqrt(norm(i))
+    end
+
+    return vecs[selected_vecs_i]
+end
+
+"Same as above but the basis is provided as a matrix instead of an array of vectors"
+gram_schmidt!(vecs::Matrix{T}, ws=ones(size(vecs, 1)), threshold=1e-5; verbose=false) where T<: Number = hcat(gram_schmidt!([vecs[:, i] for i in axes(vecs, 2)], ws, threshold; verbose=verbose)...)
+
+"Rank of a basis set (pre-normalized) under c-product"
+c_rank(vecs, ws, threshold=1e-8) = count(c_singular_values(vecs, ws) .> threshold)
+
+"Singular values (magnitudes) of a basis set (pre-normalized) under c-product"
+function c_singular_values(vecs, ws)
+    basis = hcat(vecs...)
+    N = transpose(basis) * spdiagm(ws) * basis
+    singular_values = eigvals(N) .|> abs .|> sqrt
+    return singular_values
+end

helper.jl

@@ -1,50 +0,0 @@
-using LinearAlgebra, DelimitedFiles
-
-"Sum over array while minimizing catastrophic cancellation as much as possible"
-function better_sum(arr::Array{T}) where T<:Real
-    pos_arr = arr[arr .> 0]
-    neg_arr = arr[arr .< 0]
-
-    sort!(pos_arr)
-    sort!(neg_arr, rev=true)
-
-    return sum(pos_arr) + sum(neg_arr)
-end
-
-better_sum(arr::Array{ComplexF64}) = better_sum(real.(arr)) + 1im * better_sum(imag.(arr))
-
-"The triangle inequality for angular momenta"
-triangle_ineq(l1, l2, L) = abs(l1 - l2) ≤ L && L ≤ (l1 + l2)
-
-"Index of the nearest value in a list to a given reference point"
-nearestIndex(list::Array, ref) = argmin(norm.(list .- ref))
-
-"Nearest value in a list to a given reference point"
-nearest(list::Array, ref) = list[nearestIndex(list, ref)]
-
-"Simple implementation of the Kronecker sum"
-function kron_sum(A::AbstractMatrix, B::AbstractMatrix)
-    @assert size(A, 1) == size(A, 2) && size(B, 1) == size(B, 2) "Matrices should be square"
-    return kron(A, I(size(B, 1))) + kron(I(size(A, 1)), B)
-end
-
-"Flattened vector version of Iterators.product(...) with index hierachy reversed -- leftmost index has the highest hierachy"
-iter_prod(args...) = reverse.(collect(Iterators.product(reverse(args)...))[:])
-
-"Export CSV data for a scatter plot taking in data as a list of complex vectors (x=Re, y=Im), and a list of corresponding labels, typically used for EC results"
-function exportCSV(file::String, data, labels=nothing)
-    columns = ["x" "y" "label"]
-
-    if isnothing(labels)
-        columns[end] = ""
-        labels = fill("", length(data))
-    end
-    
-    open(file, "w") do f
-        writedlm(f, columns)
-        for (d, label) in zip(data, labels)
-            l = fill(label, length(d))
-            writedlm(f, hcat(reim(d)..., l))
-        end
-    end
-end

ho_basis.jl

@@ -1,36 +1,10 @@
 using SparseArrays
 using QuadGK
 using LRUCache
-include("helper.jl")
+include("common.jl")
 include("math.jl")
 
-"1-body HO basis"
+"2-body HO basis (used for 3-body systems without the CM DOF)"
-struct ho_basis_1B
-    dim::Int # dimensionality of the basis
-    Es::Vector{Int}
-    ns::Vector{Int}
-    ls::Vector{Int}
-
-    function ho_basis_1B(E_max)
-        Es = Int[]
-        ns = Int[]
-        ls = Int[]
-    
-        # E = 2*n + l
-        for E in 0 : E_max
-            for n in 0 : E ÷ 2
-                l = E - 2*n
-                push!(Es, E)
-                push!(ns, n)
-                push!(ls, l)
-            end
-        end
-
-        return new(length(Es), Es, ns, ls)
-    end
-end
-
-"2-body HO basis"
 struct ho_basis_2B
     E_max::Int
     Λ::Int
@@ -181,7 +155,7 @@ function get_jacobi_V2_matrix(V_of_r, basis::ho_basis_2B, μω_global; atol=10^-
 
     V_relative = get_sp_V_matrix(V_relative_elem, basis.n1s, basis.l1s, [basis.n2s, basis.l2s]; dtype=ComplexF64, cache=V_relative_cache) + get_sp_V_matrix(V_relative_elem, basis.n2s, basis.l2s, [basis.n1s, basis.l1s]; dtype=ComplexF64, cache=V_relative_cache)
     U = Moshinsky_transform(basis)
-    V2 =  U' * V_relative * U
+    V2 =  transpose(U) * V_relative * U
 
     return V2
 end
@@ -217,7 +191,7 @@ function get_src_V12_matrix(V_of_r, basis::ho_basis_2B, μω_global; atol=10^-6,
     V_relative = get_sp_V_matrix(V_relative_elem, basis.n1s, basis.l1s, [basis.n2s, basis.l2s]; dtype=ComplexF64, E_max=basis.E_max)
 
     U = Moshinsky_transform(basis)
-    V12 =  U' * V_relative * U
+    V12 =  transpose(U) * V_relative * U
 
     return V12
 end
@@ -236,3 +210,42 @@ function get_W_matrix(basis_p, basis::ho_basis_2B, μ1ω1, μ2ω2=μ1ω1; weight
     end
     return sparse(W)
 end
+
+function get_3b_H_matrix(coord_system::coordinate_system, V_of_r, μω_global, E_max, Λ, m=1.0, kinetic_part=true, potential_part=true; atol=10^-5, maxevals=10^5, verbose=true)
+    if coord_system == jacobi
+        μ1ω1 = μω_global * 1/2
+        μ2ω2 = μω_global * 2
+        μ1 = m * 1/2
+        μ2 = m * 2/3        
+    elseif coord_system == src
+        μ1ω1 = μ2ω2 = μω = μω_global * 2
+        μ1 = μ2 = μ = m/2
+    end
+
+    verbose && println("No of threads = ", Threads.nthreads())
+
+    @time "Basis" basis = ho_basis_2B(E_max, Λ)
+    verbose && println("Basis size = ", basis.dim)
+
+    out = spzeros(basis.dim, basis.dim)
+
+    if kinetic_part
+        verbose && println("Constructing KE matrices")
+        @time "T1" out += get_sp_T_matrix(basis.n1s, basis.l1s, [basis.n2s, basis.l2s]; μω_gen=μ1ω1, μ=μ1)
+        @time "T2" out += get_sp_T_matrix(basis.n2s, basis.l2s, [basis.n1s, basis.l1s]; μω_gen=μ2ω2, μ=μ2)
+        if coord_system == src
+            @time "T_cross" out += get_2p_p1p2_matrix(basis, μ1ω1, μ2ω2; dtype=ComplexF64) ./ (2*μ)
+        end
+    end
+
+    if potential_part
+        verbose && println("Constructing PE matrices")
+        if coord_system == jacobi
+            @time "V" out += get_jacobi_V_matrix(V_of_r, basis, μ1ω1, μω_global; atol=atol, maxevals=maxevals)
+        elseif coord_system == src
+            @time "V" out += get_src_V_matrix(V_of_r, basis, μω, μω_global; atol=atol, maxevals=maxevals)
+        end
+    end
+
+    return out
+end

ho_basis_2body.jl

@@ -1,30 +0,0 @@
-using Arpack, SparseArrays
-include("ho_basis.jl")
-
-E_max = 30
-μω_gen = 0.2
-Λ = 0
-m = 1.0
-
-Va = -2
-Ra = 2
-
-μ1 = m * 1/2
-
-println("No of threads = ", Threads.nthreads())
-
-basis = ho_basis_1B(E_max)
-println("Basis size = ", basis.dim)
-
-println("Constructing KE matrices")
-@time "T1" T1 = get_sp_T_matrix(basis.ns, basis.ls; μω_gen=μω_gen, μ=μ1)
-
-println("Constructing PE matrices")
-V1_elem(l, n1, n2) = Va * V_Gaussian(Ra, l, n1, n2; μω_gen=μω_gen)
-@time "V1" V1 = get_sp_V_matrix(V1_elem, basis.ns, basis.ls; E_max=E_max)
-
-println("Calculating spectrum")
-@time "H" H = T1 + V1
-@time "Eigenvalues" evals, _ = eigs(H, nev=3, ncv=30, which=:SR, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
-
-display(evals)

ho_basis_3body.jl

@@ -1,40 +1,14 @@
-using Arpack, SparseArrays
+using Arpack
 include("ho_basis.jl")
 
 Λ = 0
 m = 1.0
-Va = -2
+V_of_r(r) = -2 * exp(-r^2 / 4)
-Ra = 2
 
 E_max = 40
 μω_global = 0.3
 
-# due to simple relative coordinates
+H = get_3b_H_matrix(src, V_of_r, μω_global, E_max, Λ, m)
-μω = μω_global * 2
-μ = m/2
 
-println("No of threads = ", Threads.nthreads())
-
-@time "Basis" basis = ho_basis_2B(E_max, Λ)
-
-println("Basis size = ", basis.dim)
-
-println("Constructing KE matrices")
-@time "T1" T1 = get_sp_T_matrix(basis.n1s, basis.l1s, [basis.n2s, basis.l2s]; μω_gen=μω, μ=μ)
-@time "T2" T2 = get_sp_T_matrix(basis.n2s, basis.l2s, [basis.n1s, basis.l1s]; μω_gen=μω, μ=μ)
-@time "T_cross" T_cross = get_2p_p1p2_matrix(basis, μω, μω) ./ (2*μ)
-
-println("Constructing PE matrices")
-V_elem(l, n1, n2) = Va * V_Gaussian(Ra, l, n1, n2; μω_gen=μω)
-V_relative_elem(l, n1, n2) = Va * V_Gaussian(Ra, l, n1, n2; μω_gen=μω_global)
-@time "V1" V1 = get_sp_V_matrix(V_elem, basis.n1s, basis.l1s, [basis.n2s, basis.l2s]; E_max=E_max)
-@time "V2" V2 = get_sp_V_matrix(V_elem, basis.n2s, basis.l2s, [basis.n1s, basis.l1s]; E_max=E_max)
-@time "V relative" V_relative = get_sp_V_matrix(V_relative_elem, basis.n1s, basis.l1s, [basis.n2s, basis.l2s]; E_max=E_max)
-@time "Moshinsky brackets" U = Moshinsky_transform(basis)
-@time "V12" V12 =  U' * V_relative * U
-
-println("Calculating spectrum")
-@time "H" H = T1 + T2 + T_cross + V1 + V2 + V12
 @time "Eigenvalues" evals, _ = eigs(H, nev=3, ncv=30, which=:SR, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
-
 display(evals)

ho_basis_3body_resonance.jl

@@ -1,35 +1,15 @@
-using Arpack, SparseArrays, LRUCache
+using Arpack
 include("ho_basis.jl")
 
+target_E = 4.07656088827514 - 0.012743522750966718im
+V_of_r(r) = 2 * exp(-(r-3)^2 / (1.5)^2)
 Λ = 0
 m = 1.0
-V_of_r(r) = 2 * exp(-(r-3)^2 / (1.5)^2)
 
-E_max = 30
 μω_global = 0.5 * exp(-2im * pi / 9)
+E_max = 30
 
-# due to simple relative coordinates
+H = get_3b_H_matrix(src, V_of_r, μω_global, E_max, Λ, m)
-μω = μω_global * 2
-μ = m/2
-
-println("No of threads = ", Threads.nthreads())
-
-@time "Basis" basis = ho_basis_2B(E_max, Λ)
-
-println("Basis size = ", basis.dim)
-
-println("Constructing KE matrices")
-
-@time "T1" T1 = get_sp_T_matrix(basis.n1s, basis.l1s, [basis.n2s, basis.l2s]; μω_gen=μω, μ=μ)
-@time "T2" T2 = get_sp_T_matrix(basis.n2s, basis.l2s, [basis.n1s, basis.l1s]; μω_gen=μω, μ=μ)
-@time "T_cross" T_cross = get_2p_p1p2_matrix(basis, μω, μω; dtype=ComplexF64) ./ (2*μ)
-
-println("Constructing PE matrices")
-
-@time "V" V = get_src_V_matrix(V_of_r, basis, μω, μω_global)
-
-println("Calculating spectrum")
-@time "H" H = T1 + T2 + T_cross + V
-@time "Eigenvalues" evals, _ = eigs(H, nev=5, ncv=50, which=:LI, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
 
+@time "Eigenvalues" evals, _ = eigs(H, nev=5, ncv=50, sigma=target_E, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
 display(evals)

ho_basis_3body_wf.jl

@@ -1,30 +0,0 @@
-using HDF5
-
-include("ho_basis_3body.jl")
-
-@time "Eigenvectors" evals, evecs = eigs(H, nev=1, ncv=10, which=:SR, maxiter=5000, tol=1e-8, ritzvec=true, check=1)
-
-idx = argmin(real.(evals))
-
-E = evals[idx]
-@assert imag(E) ≈ 0 "Energy is not real"
-E = real(E)
-println("Exporting E = $E")
-
-wf = real.(evecs[:, idx])
-@assert all(imag.(wf) .≈ 0) "Wave function is not real"
-wf = real.(wf)
-
-h5open("temp/HO_3b.h5", "w") do fid
-    write_attribute(fid, "E", E)
-    write_attribute(fid, "Λ", Λ)
-    write_attribute(fid, "μ1ω1", μ1ω1)
-    write_attribute(fid, "μ2ω2", μ2ω2)
-    write_attribute(fid, "μ1", μ1)
-    write_attribute(fid, "μ2", μ2)
-    write_dataset(fid, "n1", n1s)
-    write_dataset(fid, "l1", l1s)
-    write_dataset(fid, "n2", n2s)
-    write_dataset(fid, "l2", l2s)
-    write_dataset(fid, "wf", wf)
-end

math.jl

@@ -1,5 +1,5 @@
 using SpecialFunctions, WignerSymbols
-include("helper.jl")
+include("common.jl")
 
 # Gaussian potentials in HO space
 inv_factorial(n) = Iterators.prod(inv.(1:n))

p_space.jl

@@ -1,5 +1,6 @@
 using LinearAlgebra
 using SpecialFunctions, FastGaussQuadrature, QuadGK
+include("ho_basis.jl")
 
 function gausslegendre_shifted(a, b, n)
     scale = (b - a) / 2
@@ -59,3 +60,56 @@ function Vl_mat_elem(V_of_r, l, p, q; atol=0, maxevals=10^7, R_cutoff=Inf)
     (integral, _) = quadgk(integrand, 0, R_cutoff; atol=atol, maxevals=maxevals)
     return (2 / pi) * integral
 end
+
+"Return the Hamiltonian matrix (and the array of weights) for the given system."
+function get_3b_H_matrix(coord_system::coordinate_system, V_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m=1.0, kinetic_part=true, potential_part=true; atol=10^-5, maxevals=10^5, R_cutoff=16, verbose=true)
+    if coord_system == jacobi
+        μ1ω1 = μω_global * 1/2
+        μ2ω2 = μω_global * 2
+        μ1 = m * 1/2
+        μ2 = m * 2/3
+    else
+        error("Only Jacobi coordinates are implemented")
+    end
+
+    verbose && println("No of threads = ", Threads.nthreads())
+
+    V_l(j, k, kp) = Vl_mat_elem(V_of_r, j, k, kp; atol=atol, maxevals=maxevals, R_cutoff=R_cutoff)
+
+    ks, ws = get_mesh(vertices, subdivisions)
+    weights = repeat(kron(ws, ws), jmax + 1)
+    block_size = length(ks)
+    tri((j1, j2)) = triangle_ineq(j1, j2, Λ)
+    js = collect(Iterators.filter(tri, iter_prod(0:jmax, 0:jmax)))
+    basis = iter_prod(js, zip(ks, ws), zip(ks, ws)) # basis = ((j1, j2), (k1, w1), (k2, w2))
+    basis_size = length(js) * length(ks)^2
+    @assert length(basis) == basis_size "Something wrong with the basis"
+    verbose && println("Basis size = $basis_size")
+
+    out = spzeros(basis_size, basis_size)
+
+    @time "Block diagonal part" begin
+        if kinetic_part & potential_part
+            Hb_blocks = [kron_sum(get_H_matrix((k, kp) -> V_l(j1, k, kp), ks, ws, μ1), get_T_matrix(ks, μ2)) for (j1, _) in js]
+        elseif kinetic_part
+            Hb_blocks = [kron_sum(get_T_matrix(ks, μ1), get_T_matrix(ks, μ2)) for _ in js]
+        elseif potential_part
+            Hb_blocks = [kron_sum(get_V_matrix((k, kp) -> V_l(j1, k, kp), ks, ws), spzeros(block_size, block_size)) for (j1, _) in js]
+        end
+        out += blockdiag(sparse.(Hb_blocks)...)
+    end
+
+    if potential_part
+        basis_ho = ho_basis_2B(E_max, Λ)
+        verbose && println("HO basis size = ", basis_ho.dim)
+    
+        @time "V2_HO" V2_HO =  get_jacobi_V2_matrix(V_of_r, basis_ho, μω_global)
+
+        @time "W_right" W_right = get_W_matrix(basis, basis_ho, μ1ω1, μ2ω2; weights=true)
+        @time "W_left" W_left = get_W_matrix(basis, basis_ho, μ1ω1, μ2ω2; weights=false)
+
+        @time "V2" out += W_left * V2_HO * transpose(W_right)
+    end
+
+    return (out, weights)
+end

p_space_2body.jl

@@ -1,5 +1,5 @@
 using SparseArrays, Arpack
-include("helper.jl")
+include("common.jl")
 include("p_space.jl")
 
 E_target = -0.3919

p_space_3body.jl

@@ -1,56 +1,18 @@
-using LinearAlgebra, SparseArrays, Arpack
+using Arpack
-include("helper.jl")
 include("p_space.jl")
-include("ho_basis.jl")
-
-println("No of threads = ", Threads.nthreads())
-
-atol = 10^-5
-maxevals = 10^5
-R_cutoff = 16
 
 Λ = 0
 m = 1.0
-μ1 = m * 1/2
+V_of_r(r) = -2 * exp(-r^2 / 4)
-μ2 = m * 2/3
-
-Va = -2
-Ra = 2
-V_of_r(r) = Va * exp(-r^2 / Ra^2)
-V_l(j, k, kp) = Vl_mat_elem(V_of_r, j, k, kp; atol=atol, maxevals=maxevals, R_cutoff=R_cutoff)
 
 vertices = [0, 0.5 - 0.3im, 1, 4]
 subdivisions = [10, 10, 10]
-ks, ws = get_mesh(vertices, subdivisions)
-
 jmax = 4
-tri((j1, j2)) = triangle_ineq(j1, j2, Λ)
-js = collect(Iterators.filter(tri, iter_prod(0:jmax, 0:jmax)))
-
-basis = iter_prod(js, zip(ks, ws), zip(ks, ws)) # basis = ((j1, j2), (k1, w1), (k2, w2))
-basis_size = length(js) * length(ks)^2
-@assert length(basis) == basis_size "Something wrong with the basis"
-println("Basis size = $basis_size")
-
-@time "Block diagonal part" begin
-    Hb_blocks = [kron_sum(get_H_matrix((k, kp) -> V_l(j1, k, kp), ks, ws, μ1), get_T_matrix(ks, μ2)) for (j1,_ ) in js]
-    Hb = blockdiag(sparse.(Hb_blocks)...)
-end
 
 E_max = 30
 μω_global = 0.5
-μ1ω1 = μω_global * 1/2
-μ2ω2 = μω_global * 2
 
-basis_ho = ho_basis_2B(E_max, Λ)
+H, _ = get_3b_H_matrix(jacobi, V_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m)
-
-@time "V2_HO" V2_HO =  get_jacobi_V2_matrix(V_of_r, basis_ho, μω_global)
-
-@time "W_right" W_right = get_W_matrix(basis, basis_ho, μ1ω1, μ2ω2; weights=true)
-@time "W_left" W_left = get_W_matrix(basis, basis_ho, μ1ω1, μ2ω2; weights=false)
-
-@time "V2" V2 = W_left * V2_HO * transpose(W_right)
-@time "H" H = Hb + V2
 
 @time "Eigenvalues" evals, _ = eigs(H, nev=3, ncv=24, which=:SR, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
 display(evals)

p_space_3body_resonance.jl

@@ -1,55 +1,20 @@
-using LinearAlgebra, SparseArrays, Arpack
+using Arpack
-include("helper.jl")
 include("p_space.jl")
-include("ho_basis.jl")
-
-println("No of threads = ", Threads.nthreads())
-atol = 10^-5
-maxevals = 10^5
-R_cutoff = 16
-
-Λ = 0
-m = 1.0
-μ1 = m * 1/2
-μ2 = m * 2/3
 
 target = 4.0766890719636875 - 0.012758927741074495im
 
+Λ = 0
+m = 1.0
 V_of_r(r) =  2 * exp(-(r-3)^2 / (1.5)^2)
-V_l(j, k, kp) = Vl_mat_elem(V_of_r, j, k, kp; atol=atol, maxevals=maxevals, R_cutoff=R_cutoff)
 
 vertices = [0, 2 - 0.2im, 3, 4]
 subdivisions = [15, 10, 10]
-ks, ws = get_mesh(vertices, subdivisions)
-
 jmax = 4
-tri((j1, j2)) = triangle_ineq(j1, j2, Λ)
-js = collect(Iterators.filter(tri, iter_prod(0:jmax, 0:jmax)))
-
-basis = iter_prod(js, zip(ks, ws), zip(ks, ws)) # basis = ((j1, j2), (k1, w1), (k2, w2))
-basis_size = length(js) * length(ks)^2
-@assert length(basis) == basis_size "Something wrong with the basis"
-println("Basis size = $basis_size")
-
-@time "Block diagonal part" begin
-    Hb_blocks = [kron_sum(get_H_matrix((k, kp) -> V_l(j1, k, kp), ks, ws, μ1), get_T_matrix(ks, μ2)) for (j1,_ ) in js]
-    Hb = blockdiag(sparse.(Hb_blocks)...)
-end
 
 E_max = 40
 μω_global = 0.5
-μ1ω1 = μω_global * 1/2
-μ2ω2 = μω_global * 2
 
-basis_ho = ho_basis_2B(E_max, Λ)
+H, _ = get_3b_H_matrix(jacobi, V_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m)
-
-@time "V2_HO" V2_HO =  get_jacobi_V2_matrix(V_of_r, basis_ho, μω_global; atol=atol, maxevals=maxevals)
-
-@time "W_right" W_right = get_W_matrix(basis, basis_ho, μ1ω1, μ2ω2; weights=true)
-@time "W_left" W_left = get_W_matrix(basis, basis_ho, μ1ω1, μ2ω2; weights=false)
-
-@time "V2" V2 = W_left * V2_HO * transpose(W_right)
-@time "H" H = Hb + V2
 
 @time "Eigenvalues" evals, _ = eigs(H, sigma=target, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
 display(evals)

test/ho_basis_test.jl

@@ -1,5 +1,5 @@
 using LinearAlgebra
-include("ho_basis.jl")
+include("../ho_basis.jl")
 
 l = 0
 V0 = -10

test/misc.jl

@@ -0,0 +1,65 @@
+using SparseArrays, LinearAlgebra
+include("../common.jl")
+
+#### gram_schmidt ####
+ 
+n = 64
+N = 8
+
+vecs = [rand(n) + 1im .* rand(n) for _ in 1:N]
+
+ws = rand(n)
+ws_mat = spdiagm(ws)
+
+basis = hcat(vecs...)
+
+H = rand(n, n)
+H += transpose(H) # complex symmetric
+
+H_EC = transpose(basis) * ws_mat * H * ws_mat * basis
+N_EC = transpose(basis) * ws_mat * basis
+
+evals = eigvals(H_EC, N_EC)
+println("Eigenvalues with GEVP:")
+display(evals)
+
+ortho_basis = hcat(gram_schmidt!(vecs, ws)...)
+N_ortho = transpose(ortho_basis) * ws_mat * ortho_basis
+println("Norm matrix after Gram-Schmidt:")
+display(round.(N_ortho; digits=2)) # should be ≈I
+@assert N_ortho ≈ I(N) "Gram-Schmidt did not yield an orthogonal basis"
+
+H_EC_ortho = transpose(ortho_basis) * ws_mat * H * ws_mat * ortho_basis
+
+evals_ortho = eigvals(H_EC_ortho)
+println("Eigenvalues after Gram-Schmidt:")
+display(evals_ortho)
+
+@assert evals ≈ evals_ortho "Gram-Schmidt did not preserve the eigenvalues"
+
+############
+println("\nRepeat with a redundant basis\n")
+
+println("Original dimensionality = $(length(vecs))")
+
+for pow in 6:9
+    noise = rand(n) ./ 10^pow
+    new_vec = vecs[1] + noise
+    push!(vecs, new_vec)
+end
+
+println("Dimensionality before Gram-Schmidt = $(length(vecs))")
+
+ortho_vecs = gram_schmidt!(vecs, ws; verbose=true)
+ortho_basis = hcat(ortho_vecs...)
+println("Dimensionality after Gram-Schmidt = $(length(ortho_vecs))")
+
+H_EC_ortho = transpose(ortho_basis) * ws_mat * H * ws_mat * ortho_basis
+
+evals_ortho = eigvals(H_EC_ortho)
+println("Eigenvalues after Gram-Schmidt:")
+display(evals_ortho)
+
+@assert isapprox(evals, evals_ortho; atol=1e-3) "Gram-Schmidt did not approximately preserve the eigenvalues"
+
+######################

test/moshinsky.jl

@@ -1,4 +1,4 @@
-println("### Test: U' * U == identity")
+println("### Test: transpose(U) * U == identity")
 
 using LinearAlgebra
 
@@ -15,7 +15,7 @@ println("Basis size = ", basis.dim)
 
 @time "Moshinsky brackets" U = Moshinsky_transform(basis)
 
-check = U' * U - I
+check = transpose(U) * U - I
 
 println("Maximum = ", maximum(abs.(check)))
 println("Norm = ", sum(check .* conj(check)))

test/p_space_basic.jl

@@ -1,5 +1,5 @@
 using Plots
-include("../helper.jl")
+include("../common.jl")
 include("../p_space.jl")
 
 vertices = [0, 1 - 0.5im, 2, 6]

test/simple_berggren.jl

@@ -1,5 +1,5 @@
 using Plots
-include("../helper.jl")
+include("../common.jl")
 include("../p_space.jl")
 
 vertices = [0, 0.5 - 0.3im, 1, 6]