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Fix Pardiso extension for the case of an AbstractSparseMatrixCSC #550

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Merged
merged 2 commits into from
Oct 20, 2024
Merged

Fix Pardiso extension for the case of an AbstractSparseMatrixCSC #550

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35d4fae
Allow to use Pardiso with AbstractSparseMatrixCSC
j-fu Oct 19, 2024
c88b634
Add a test for PardisoExt working with AbstractSparseMatrixCSC
j-fu Oct 20, 2024
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5 changes: 2 additions & 3 deletions ext/LinearSolvePardisoExt.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -134,12 +134,11 @@ function SciMLBase.solve!(cache::LinearSolve.LinearCache, alg::PardisoJL; kwargs
if cache.isfresh
phase = alg.cache_analysis ? Pardiso.NUM_FACT : Pardiso.ANALYSIS_NUM_FACT
Pardiso.set_phase!(cache.cacheval, phase)
Pardiso.pardiso(cache.cacheval, A, eltype(A)[])
Pardiso.pardiso(cache.cacheval, SparseMatrixCSC(size(A)..., getcolptr(A), rowvals(A), nonzeros(A)), eltype(A)[])
cache.isfresh = false
end
Pardiso.set_phase!(cache.cacheval, Pardiso.SOLVE_ITERATIVE_REFINE)
Pardiso.pardiso(cache.cacheval, u, A, b)

Pardiso.pardiso(cache.cacheval, u, SparseMatrixCSC(size(A)..., getcolptr(A), rowvals(A), nonzeros(A)), b)
return SciMLBase.build_linear_solution(alg, cache.u, nothing, cache)
end

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2 changes: 1 addition & 1 deletion src/extension_algs.jl
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Expand Up @@ -217,7 +217,7 @@ All values default to `nothing` and the solver internally determines the values
given the input types, and these keyword arguments are only for overriding the
default handling process. This should not be required by most users.
"""
struct PardisoJL{T1, T2} <: LinearSolve.SciMLLinearSolveAlgorithm
struct PardisoJL{T1, T2} <: AbstractSparseFactorization
nprocs::Union{Int, Nothing}
solver_type::T1
matrix_type::T2
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37 changes: 37 additions & 0 deletions test/pardiso/pardiso.jl
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Expand Up @@ -177,3 +177,40 @@ for solver in solvers
@test Pardiso.get_iparm(solver, i) == iparm[i][2]
end
end

@testset "AbstractSparseMatrixCSC" begin
struct MySparseMatrixCSC2{Tv, Ti} <: SparseArrays.AbstractSparseMatrixCSC{Tv, Ti}
csc::SparseMatrixCSC{Tv, Ti}
end

Base.size(m::MySparseMatrixCSC2) = size(m.csc)
SparseArrays.getcolptr(m::MySparseMatrixCSC2) = SparseArrays.getcolptr(m.csc)
SparseArrays.rowvals(m::MySparseMatrixCSC2) = SparseArrays.rowvals(m.csc)
SparseArrays.nonzeros(m::MySparseMatrixCSC2) = SparseArrays.nonzeros(m.csc)

for alg in algs
N = 100
u0 = ones(N)
A0 = spdiagm(1 => -ones(N - 1), 0 => fill(10.0, N), -1 => -ones(N - 1))
b0 = A0 * u0
B0 = MySparseMatrixCSC2(A0)
A1 = spdiagm(1 => -ones(N - 1), 0 => fill(100.0, N), -1 => -ones(N - 1))
b1=A1*u0
B1= MySparseMatrixCSC2(A1)


pr = LinearProblem(B0, b0)
# test default algorithn
u=solve(pr,alg)
@test norm(u - u0, Inf) < 1.0e-13

# test factorization with reinit!
pr = LinearProblem(B0, b0)
cache=init(pr,alg)
u=solve!(cache)
@test norm(u - u0, Inf) < 1.0e-13
reinit!(cache; A=B1, b=b1)
u=solve!(cache)
@test norm(u - u0, Inf) < 1.0e-13
end
end
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