Rename HOperator to Hamiltonian

Hamiltonian.jl

@@ -1,10 +1,10 @@
 include("common.jl")
 using TensorOperations, KrylovKit, LinearAlgebra, CUDA, CUDA.CUTENSOR
 
-@enum HOperator_backend cpu_tensor gpu_cutensor
+@enum Hamiltonian_backend cpu_tensor gpu_cutensor
 
 "A Hamiltonian that can be applied to a vector"
-struct HOperator{T}
+struct Hamiltonian{T}
     d::Int
     n::Int
     N::Int
@@ -15,8 +15,8 @@ struct HOperator{T}
     K_mixed # CuTensor{Complex{T}} or Nothing
     Vs # Array{Complex{T}} or CuArray{Complex{T}}
     hermitian::Bool
-    mode::HOperator_backend
+    mode::Hamiltonian_backend
-    function HOperator{T}(V_twobody::Function, d::Int, n::Int, N::Int, L::T, ϕ::T, μ::T, n_image::Int, mode::HOperator_backend) where {T<:Float}
+    function Hamiltonian{T}(V_twobody::Function, d::Int, n::Int, N::Int, L::T, ϕ::T, μ::T, n_image::Int, mode::Hamiltonian_backend) where {T<:Float}
         @assert mode != gpu_cutensor || CUDA.functional() && CUDA.has_cuda() && CUDA.has_cuda_gpu() "CUDA not available"
         k = -N÷2:N÷2-1
         Vs = calculate_Vs(V_twobody, d, n, N, L, ϕ, n_image)
@@ -33,14 +33,14 @@ struct HOperator{T}
     end
 end
 
-Base.size(H::HOperator, i::Int)::Int = (i == 1 || i == 2) ? H.N^(H.d * (H.n - 1)) : throw(ArgumentError("HOperator only has 2 dimesions"))
+Base.size(H::Hamiltonian, i::Int)::Int = (i == 1 || i == 2) ? H.N^(H.d * (H.n - 1)) : throw(ArgumentError("Hamiltonian only has 2 dimesions"))
-Base.size(H::HOperator)::Dims{2} = (size(H, 1), size(H, 2))
+Base.size(H::Hamiltonian)::Dims{2} = (size(H, 1), size(H, 2))
 
 "Dimensions of a vector to which 'H' can be applied"
-vectorDims(H::HOperator)::Dims = tuple(fill(H.N, H.d * (H.n - 1))...)
+vectorDims(H::Hamiltonian)::Dims = tuple(fill(H.N, H.d * (H.n - 1))...)
 
 "Apply 'H' on 'v' and store the result in 'out' using the 'cpu_tensor' backend"
-function LinearAlgebra.mul!(out::Array{Complex{T}}, H::HOperator{T}, v::Array{Complex{T}})::Array{Complex{T}} where {T<:Float}
+function LinearAlgebra.mul!(out::Array{Complex{T}}, H::Hamiltonian{T}, v::Array{Complex{T}})::Array{Complex{T}} where {T<:Float}
     #LinearMaps.check_dim_mul(out,H,v) --- dimensions don't match
     # apply V operator
     @. out = H.Vs * v
@@ -78,7 +78,7 @@ function contract_accumulate!(C::CuTensor, A::CuTensor, B::CuTensor)::CuTensor
 end
 
 "Apply 'H' on 'v' and store the result in 'out' using the 'gpu_cutensor' backend"
-function LinearAlgebra.mul!(out::CuArray{Complex{T}}, H::HOperator{T}, v::CuArray{Complex{T}})::CuArray{Complex{T}} where {T<:Float}
+function LinearAlgebra.mul!(out::CuArray{Complex{T}}, H::Hamiltonian{T}, v::CuArray{Complex{T}})::CuArray{Complex{T}} where {T<:Float}
     #LinearMaps.check_dim_mul(out,H,v) --- dimensions don't match
     ctx = context()
     # apply V operator
@@ -123,7 +123,7 @@ function LinearAlgebra.mul!(out::CuArray{Complex{T}}, H::HOperator{T}, v::CuArra
 end
 
 "Apply 'H' on 'v' and return the result"
-function (H::HOperator)(v)
+function (H::Hamiltonian)(v)
     out = similar(v)
     return mul!(out, H, v)
 end
@@ -131,7 +131,7 @@ end
 tolerance = 1e-6
 
 "Wrapper for KrylovKit.eigsolve"
-function eig(H::HOperator{T}, levels::Int; resonances = !H.hermitian)::Tuple{Vector{Complex{T}},Any,Any} where {T<:Float}
+function eig(H::Hamiltonian{T}, levels::Int; resonances = !H.hermitian)::Tuple{Vector{Complex{T}},Any,Any} where {T<:Float}
     if H.mode == cpu_tensor
         x₀ = rand(Complex{T}, vectorDims(H)...)
     elseif H.mode == gpu_cutensor

benchmark.jl

@@ -1,4 +1,4 @@
-include("HOperator.jl")
+include("Hamiltonian.jl")
 
 GPU_mode = !("CPU" in ARGS) && CUDA.functional() && CUDA.has_cuda() && CUDA.has_cuda_gpu()
 
@@ -31,7 +31,7 @@ n_image=1
 
 for L::T in 5.0:14.0
     println("Constructing H operator...")
-    @time H=HOperator{T}(V_test,3,3,N,L,convert(T,0),convert(T,μ),n_image,mode)
+    @time H=Hamiltonian{T}(V_test,3,3,N,L,convert(T,0),convert(T,μ),n_image,mode)
     println("Applying H 1000 times...")
     if GPU_mode
         v=CUDA.rand(Complex{T},vectorDims(H)...)

example.ipynb

@@ -7,7 +7,7 @@
    "outputs": [],
    "source": [
     "# prerequisite packages: KrylovKit, TensorOperations, LinearAlgebra, CUDA#tb/cutensor, Plots\n",
-    "include(\"HOperator.jl\")\n",
+    "include(\"Hamiltonian.jl\")\n",
     "mode = cpu_tensor # using CPU mode\n",
     "T = Float32 # single-precision mode"
    ]
@@ -29,7 +29,7 @@
     "μ::T = 0.5\n",
     "n_imag = 1\n",
     "\n",
-    "H = HOperator{T}(V_gauss, d, n, N, L, ϕ, μ, n_imag, mode)\n",
+    "H = Hamiltonian{T}(V_gauss, d, n, N, L, ϕ, μ, n_imag, mode)\n",
     "@time evals, evecs, info = eig(H, 5)\n",
     "print(info.numops, \" operations : \")\n",
     "println(evals)"
@@ -54,7 +54,7 @@
     "μ::T = 0.5\n",
     "n_imag = 0\n",
     "\n",
-    "H = HOperator{T}(V_gauss, d, n, N, L, ϕ, μ, n_imag, mode)\n",
+    "H = Hamiltonian{T}(V_gauss, d, n, N, L, ϕ, μ, n_imag, mode)\n",
     "@time evals, evecs, info = eig(H, 20)\n",
     "print(info.numops, \" operations : \")\n",
     "print(evals)\n",