Add support for sum and kronecker product of COO sparse matrices

.gitignore

@@ -3,3 +3,5 @@
 *.jl.mem
 /Manifest.toml
 /docs/build/
+
+.vscode

src/coo_linalg.jl

@@ -320,6 +320,64 @@
 
 +(D::Diagonal, A::SparseMatrixCOO) = A + D
 
+function Base.:+(A::SparseMatrixCOO{T1}, B::SparseMatrixCOO{T2}) where {T1<:Number, T2<:Number}
+  A.n == B.n || throw(ArgumentError("A and B must have the same number of rows"))
+  A.m == B.m || throw(ArgumentError("A and B must have the same number of columns"))
+
+  T = promote_type(T1, T2)
+
+  rowval_colvalA = collect(zip(A.rows, A.cols))
+  rowval_colvalB = collect(zip(B.rows, B.cols))
+
+  rowval_colval = union(rowval_colvalA, rowval_colvalB)
+  rowval = first.(rowval_colval)
+  colval = last.(rowval_colval)
+
+  nzval = similar(rowval, T)
+  @inbounds for i in eachindex(rowval)
+      nzval[i] = zero(T)
+      for j in eachindex(rowval_colvalA)
+          if rowval_colvalA[j] == rowval_colval[i]
+              nzval[i] += A.vals[j]
+              break
+          end
+      end
+      for j in eachindex(rowval_colvalB)
+          if rowval_colvalB[j] == rowval_colval[i]
+              nzval[i] += B.vals[j]
+              break
+          end
+      end
+  end
+
+  return SparseMatrixCOO(A.m, A.n, rowval, colval, nzval)
+end
+
+function LinearAlgebra.kron(A::SparseMatrixCOO, B::SparseMatrixCOO)
+  mA, nA = size(A)
+  mB, nB = size(B)
+  out_shape = (mA * mB, nA * nB)
+  Annz = nnz(A)
+  Bnnz = nnz(B)
+
+  if Annz == 0 || Bnnz == 0
+      return SparseMatrixCOO(Int[], Int[], T[], out_shape...)
+  end
+
+  row = (A.rows .- 1) .* mB
+  row = repeat(row, inner = Bnnz)
+  col = (A.cols .- 1) .* nB
+  col = repeat(col, inner = Bnnz)
+  data = repeat(A.vals, inner = Bnnz)
+
+  row .+= repeat(B.rows, outer = Annz)
+  col .+= repeat(B.cols, outer = Annz)
+
+  data .*= repeat(B.vals, outer = Annz)
+
+  return SparseMatrixCOO(out_shape[1], out_shape[2], row, col, data)
+end
+
 # maximum! functions
 replace_if_minusinf(val::T, replacement::T) where {T} = (val == -T(Inf)) ? replacement : val
 function LinearAlgebra.maximum!(f::Function, v::AbstractVector{T}, A::SparseMatrixCOO{T}) where {T}

test/runtests.jl

@@ -293,6 +293,11 @@ end
   B_coo = D + A_coo
   @test norm(B_csc - B_coo) ≤ sqrt(eps()) * norm(B_csc)
   @test issorted(B_coo.cols)
+
+  B = sprand(Float64, 20, 20, 0.1)
+  C_csc = A + B
+  C_coo = A_coo + SparseMatrixCOO(B)
+  @test norm(C_csc - C_coo) ≤ sqrt(eps()) * norm(C_csc)
 end
 
 @testset "row/col reduce" begin
@@ -336,3 +341,13 @@ end
   maximum!(abs, v_coo', As_coo)
   @test norm(v - v_coo) ≤ sqrt(eps()) * norm(v)
 end
+
+@testset "Kronecker product" begin
+  A = sprand(Float64, 10, 15, 0.2)
+  B = sprand(Float64, 5, 7, 0.3)
+  A_coo = SparseMatrixCOO(A)
+  B_coo = SparseMatrixCOO(B)
+  C = kron(A, B)
+  C_coo = kron(A_coo, B_coo)
+  @test norm(C - C_coo) ≤ sqrt(eps()) * norm(C)
+end