Fix type instabilities on complex AddedOperators and add new methods

Author: albertomercurio

src/basic.jl

@@ -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)
         new{T, typeof(ops)}(ops)
     end
 end
@@ -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

test/basic.jl

@@ -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