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Use AbstractMatrix in LU. #178

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Merged
krcools merged 1 commit into from
Dec 12, 2025
Merged

Use AbstractMatrix in LU. #178

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2 changes: 0 additions & 2 deletions examples/hello.jl
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36 changes: 32 additions & 4 deletions src/solvers/lu.jl
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
mutable struct LUFactorization{T,M<:Matrix{T},X} <: LinearMap{T}
mutable struct LUFactorization{T,M<:AbstractMatrix{T},X} <: LinearMap{T}
A::M
F::Any
factorized::Bool
Expand All @@ -10,8 +10,7 @@ function LUFactorization(A)
T = eltype(A)
axs = reverse(axes(A))
X = typeof(axs)
B = Matrix(A)
LUFactorization{T,Matrix{T},X}(B, nothing, false, axs)
LUFactorization{T,M,X}(A, nothing, false, axs)
end

# function LUFactorization(A::SparseMatrixCSC)
Expand Down Expand Up @@ -41,7 +40,7 @@ end
function LinearAlgebra.mul!(y::AbstractVector, L::LUFactorization, b::AbstractVector)
fill!(y,false)
if L.factorized == false
L.F = LinearAlgebra.lu!(L.A)
L.F = LinearAlgebra.lu(L.A)
L.factorized = true
end
y[:] = L.F \ Vector(b)
Expand Down Expand Up @@ -88,4 +87,33 @@ end

@test x[1:n] ≈ -b[n+1:end]
@test x[n+1:end] ≈ b[1:n]
end


@testitem "LUFactorization Sparse/Dense" begin
using LinearAlgebra, SparseArrays
using CompScienceMeshes, BEAST

h = 0.5
M = meshsphere(1.0,h)
X = BEAST.gwpdiv(M;order=1)

G = assemble(BEAST.Identity(),X,X)
dG = Matrix(G)

b = rand(numfunctions(X))

Gi = BEAST.lu(G)
dGi = BEAST.lu(dG)

#First time computing the factorization
@time x1 = Gi*b
@time x2 = dGi*b
#Second time only substitutions
@time x1 = Gi*b
@time x2 = dGi*b

@test typeof(Gi.F) == SparseArrays.UMFPACK.UmfpackLU{Float64, Int64}
@test typeof(dGi.F) == LinearAlgebra.LU{Float64, Matrix{Float64}, Vector{Int64}}

end