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remove KLU and UMFPACK from the default #456

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remove KLU and UMFPACK from the default #456

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3 changes: 2 additions & 1 deletion src/default.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -91,7 +91,8 @@
@static if INCLUDE_SPARSE
function defaultalg(A::AbstractSparseMatrixCSC{<:Union{Float64, ComplexF64}, Ti}, b,
assump::OperatorAssumptions{Bool}) where {Ti}
if assump.issq
#TODO: find a way to supress the errors that KLU and UMFPACK.jl throw for singluar matrices

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"supress" should be "suppress".

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"singluar" should be "singular".
if false && assump.issq
if length(b) <= 10_000 && length(nonzeros(A)) / length(A) < 2e-4
DefaultLinearSolver(DefaultAlgorithmChoice.KLUFactorization)
else
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8 changes: 5 additions & 3 deletions src/factorization.jl
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Expand Up @@ -748,19 +748,20 @@
SparseArrays.decrement(SparseArrays.getrowval(A)) ==
cacheval.rowval)
fact = lu(SparseMatrixCSC(size(A)..., getcolptr(A), rowvals(A),
nonzeros(A)))
nonzeros(A)), check=false)
else
fact = lu!(cacheval,
SparseMatrixCSC(size(A)..., getcolptr(A), rowvals(A),
nonzeros(A)))
nonzeros(A)), check=false)
end
else
fact = lu(SparseMatrixCSC(size(A)..., getcolptr(A), rowvals(A), nonzeros(A)))
fact = lu(SparseMatrixCSC(size(A)..., getcolptr(A), rowvals(A), nonzeros(A)), check=false)

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end
cache.cacheval = fact
cache.isfresh = false
end

# TODO gaurd this against SingularExceptions

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"gaurd" should be "guard" or "gourd".
y = ldiv!(cache.u, @get_cacheval(cache, :UMFPACKFactorization), cache.b)
SciMLBase.build_linear_solution(alg, y, nothing, cache)
end
Expand Down Expand Up @@ -808,6 +809,7 @@
nonzeros(A)))
end

# TODO: guard this against errors
function SciMLBase.solve!(cache::LinearCache, alg::KLUFactorization; kwargs...)
A = cache.A
A = convert(AbstractMatrix, A)
Expand Down
16 changes: 10 additions & 6 deletions test/default_algs.jl
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Expand Up @@ -35,15 +35,19 @@
LinearSolve.OperatorAssumptions(false)).alg ===
LinearSolve.DefaultAlgorithmChoice.QRFactorization

@test LinearSolve.defaultalg(sprand(10^4, 10^4, 1e-5) + I, zeros(1000)).alg ===
LinearSolve.DefaultAlgorithmChoice.KLUFactorization
prob = LinearProblem(sprand(1000, 1000, 0.5), zeros(1000))

A = spzeros(100, 100)
A[1,1]=1
prob = LinearProblem(A, ones(100))
# test that solving a singluar problem doesn't error

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solve(prob)

@test LinearSolve.defaultalg(sprand(11000, 11000, 0.001), zeros(11000)).alg ===
# test_broken because these would be faster, but are currently not used by default
# because they solvers throw errors for singular problems
@test_broken LinearSolve.defaultalg(sprand(10^4, 10^4, 1e-5) + I, zeros(1000)).alg ===
LinearSolve.DefaultAlgorithmChoice.KLUFactorization
@test_broken LinearSolve.defaultalg(sprand(11000, 11000, 0.001), zeros(11000)).alg ===
LinearSolve.DefaultAlgorithmChoice.UMFPACKFactorization
prob = LinearProblem(sprand(11000, 11000, 0.5), zeros(11000))
solve(prob)

# Test inference
A = rand(4, 4)
Expand Down Expand Up @@ -77,8 +81,8 @@
x = rand(n)
f = (du, u, p, t) -> mul!(du, A, u)
fadj = (du, u, p, t) -> mul!(du, A', u)
fo = FunctionOperator(f, x, b; op_adjoint = fadj)

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prob = LinearProblem(fo, b)

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sol1 = solve(prob)
sol2 = solve(prob, LinearSolve.KrylovJL_GMRES())
@test sol1.u == sol2.u
Expand All @@ -89,8 +93,8 @@
x = rand(n)
f = (du, u, p, t) -> mul!(du, A, u)
fadj = (du, u, p, t) -> mul!(du, A', u)
fo = FunctionOperator(f, x, b; op_adjoint = fadj)

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prob = LinearProblem(fo, b)

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"fo" should be "of" or "for" or "do" or "go" or "to".
sol1 = solve(prob)
sol2 = solve(prob, LinearSolve.KrylovJL_LSMR())
@test sol1.u == sol2.u
Expand All @@ -101,8 +105,8 @@
x = rand(n)
f = (du, u, p, t) -> mul!(du, A, u)
fadj = (du, u, p, t) -> mul!(du, A', u)
fo = FunctionOperator(f, x, b; op_adjoint = fadj)

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prob = LinearProblem(fo, b)

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sol1 = solve(prob)
sol2 = solve(prob, LinearSolve.KrylovJL_CRAIGMR())
@test sol1.u == sol2.u
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