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Fix type instabilities on complex AddedOperators and add new methods #310

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Sep 15, 2025
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Fix type instabilities on complex AddedOperators and add new methods #310

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6 changes: 5 additions & 1 deletion src/basic.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -429,7 +429,7 @@ struct AddedOperator{T,
function AddedOperator(ops)
@assert !isempty(ops)
_check_AddedOperator_sizes(ops)
T = promote_type(eltype.(ops)...)
T = mapreduce(eltype, promote_type, ops)
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@ChrisRackauckas ChrisRackauckas Sep 14, 2025

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make sure this doesn't allocate? Usually mapreduce is expensive.

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@albertomercurio albertomercurio Sep 14, 2025

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I had to change it because the broadcasting was actually making type instabilities.

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@ChrisRackauckas ChrisRackauckas Sep 15, 2025

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oh it's on a tuple, that works.

new{T, typeof(ops)}(ops)
end
end
Expand Down Expand Up @@ -476,9 +476,13 @@ function Base.:+(Z::NullOperator, A::AddedOperator)
A
end

Base.:-(A::AddedOperator) = AddedOperator(map(-, A.ops))
Base.:-(A::AbstractSciMLOperator, B::AbstractSciMLOperator) = AddedOperator(A, -B)
Base.:-(A::AbstractSciMLOperator, B::AbstractMatrix) = A - MatrixOperator(B)
Base.:-(A::AbstractMatrix, B::AbstractSciMLOperator) = MatrixOperator(A) - B
Base.:-(A::AddedOperator, B::AbstractSciMLOperator) = AddedOperator(A.ops..., -B)
Base.:-(A::AbstractSciMLOperator, B::AddedOperator) = AddedOperator(A, (-B).ops...)
Base.:-(A::AddedOperator, B::AddedOperator) = AddedOperator(A.ops..., (-B).ops...)

for op in (:+, :-)
for T in SCALINGNUMBERTYPES
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43 changes: 18 additions & 25 deletions test/basic.jl
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Expand Up @@ -278,33 +278,26 @@ end
end

## Time-Dependent Coefficients

for T in (Float32, Float64, ComplexF32, ComplexF64)
N = 100
A1_sparse = MatrixOperator(sprand(T, N, N, 5 / N))
A2_sparse = MatrixOperator(sprand(T, N, N, 5 / N))
A3_sparse = MatrixOperator(sprand(T, N, N, 5 / N))

A1_dense = MatrixOperator(rand(T, N, N))
A2_dense = MatrixOperator(rand(T, N, N))
A3_dense = MatrixOperator(rand(T, N, N))

coeff1(a, u, p, t) = sin(p.ω * t)
coeff2(a, u, p, t) = cos(p.ω * t)
coeff3(a, u, p, t) = sin(p.ω * t) * cos(p.ω * t)

c1 = ScalarOperator(rand(T), coeff1)
c2 = ScalarOperator(rand(T), coeff2)
c3 = ScalarOperator(rand(T), coeff3)

H_sparse = c1 * A1_sparse + c2 * A2_sparse + c3 * A3_sparse
H_dense = c1 * A1_dense + c2 * A2_dense + c3 * A3_dense

u = rand(T, N)
v = rand(T, N)
du = similar(u)
p = (ω = 0.1,)
t = 0.1
A = sprand(T, N, N, 2 / N)

func1(a, u, p, t) = t
func2(a, u, p, t) = t^2
func3(a, u, p, t) = t^3
func4(a, u, p, t) = t^4
func5(a, u, p, t) = t^5

O1 = MatrixOperator(A) + ScalarOperator(0.0, func1) * MatrixOperator(A) + ScalarOperator(0.0, func2) * MatrixOperator(A)

O2 = MatrixOperator(A) + ScalarOperator(0.0, func3) * MatrixOperator(A) + ScalarOperator(0.0, func4) * MatrixOperator(A)

O3 = MatrixOperator(A) + ScalarOperator(0.0, func5) * MatrixOperator(A)

Op = -1im * (O1 - O2)

@test length(Op.ops) == length(O1.ops) + length(O2.ops)
@inferred Op + O3
end
end

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