Support sparse broadcast with transposes and adjoints (#26331)

Fixes #25331

Author: mbauman

stdlib/SparseArrays/src/higherorderfns.jl

@@ -973,7 +973,7 @@ broadcast(f::Tf, A::SparseMatrixCSC, ::Type{T}) where {Tf,T} = broadcast(x -> f(
 # and rebroadcast. otherwise, divert to generic AbstractArray broadcast code.
 
 struct PromoteToSparse <: Broadcast.AbstractArrayStyle{2} end
-StructuredMatrix = Union{Diagonal,Bidiagonal,Tridiagonal,SymTridiagonal}
+const StructuredMatrix = Union{Diagonal,Bidiagonal,Tridiagonal,SymTridiagonal}
 Broadcast.BroadcastStyle(::Type{<:StructuredMatrix}) = PromoteToSparse()
 
 PromoteToSparse(::Val{0}) = PromoteToSparse()
@@ -988,8 +988,10 @@ Broadcast.BroadcastStyle(::PromoteToSparse, ::Broadcast.Style{Tuple}) = Broadcas
 # Broadcast.BroadcastStyle(::SPVM, ::Broadcast.DefaultArrayStyle{0}) = PromoteToSparse()
 # Broadcast.BroadcastStyle(::SPVM, ::Broadcast.DefaultArrayStyle{1}) = PromoteToSparse()
 # Broadcast.BroadcastStyle(::SPVM, ::Broadcast.DefaultArrayStyle{2}) = PromoteToSparse()
-BroadcastStyle(::Type{<:Adjoint{T,<:Vector}}) where T = Broadcast.MatrixStyle() # Adjoint not yet defined when broadcast.jl loaded
-BroadcastStyle(::Type{<:Transpose{T,<:Vector}}) where T = Broadcast.MatrixStyle() # Transpose not yet defined when broadcast.jl loaded
+Broadcast.BroadcastStyle(::Type{<:Adjoint{T,<:Vector} where T}) = Broadcast.MatrixStyle() # Adjoint not yet defined when broadcast.jl loaded
+Broadcast.BroadcastStyle(::Type{<:Transpose{T,<:Vector} where T}) = Broadcast.MatrixStyle() # Transpose not yet defined when broadcast.jl loaded
+Broadcast.BroadcastStyle(::Type{<:Adjoint{T,<:Union{SparseVector,SparseMatrixCSC}} where T}) = PromoteToSparse()
+Broadcast.BroadcastStyle(::Type{<:Transpose{T,<:Union{SparseVector,SparseMatrixCSC}} where T}) = PromoteToSparse()
 Broadcast.BroadcastStyle(::SPVM, ::Broadcast.VectorStyle) = PromoteToSparse()
 Broadcast.BroadcastStyle(::SPVM, ::Broadcast.MatrixStyle) = PromoteToSparse()
 Broadcast.BroadcastStyle(::SparseVecStyle, ::Broadcast.DefaultArrayStyle{N}) where N =

stdlib/SparseArrays/test/higherorderfns.jl

@@ -299,10 +299,12 @@ end
     elT = Float64
     s = Float32(2.0)
     V = sprand(elT, N, p)
+    Vᵀ = transpose(sprand(elT, 1, N, p))
     A = sprand(elT, N, M, p)
-    fV, fA = Array(V), Array(A)
+    Aᵀ = transpose(sprand(elT, M, N, p))
+    fV, fA, fVᵀ, fAᵀ = Array(V), Array(A), Array(Vᵀ), Array(Aᵀ)
     # test combinations involving one to three scalars and one to five sparse vectors/matrices
-    spargseq, dargseq = Iterators.cycle((A, V)), Iterators.cycle((fA, fV))
+    spargseq, dargseq = Iterators.cycle((A, V, Aᵀ, Vᵀ)), Iterators.cycle((fA, fV, fAᵀ, fVᵀ))
     for nargs in 1:5 # number of tensor arguments
         nargsl = cld(nargs, 2) # number in "left half" of tensor arguments
         nargsr = fld(nargs, 2) # number in "right half" of tensor arguments