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add specific Adjoint{StridedMatrix} * SparseVector method #32403

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Merged
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Jun 26, 2019
Merged

add specific Adjoint{StridedMatrix} * SparseVector method #32403

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72 changes: 56 additions & 16 deletions stdlib/SparseArrays/src/sparsevector.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -1494,7 +1494,7 @@ function (*)(A::StridedMatrix{Ta}, x::AbstractSparseVector{Tx}) where {Ta,Tx}
require_one_based_indexing(A, x)
m, n = size(A)
length(x) == n || throw(DimensionMismatch())
Ty = promote_type(Ta, Tx)
Ty = promote_op(matprod, Ta, Tx)
y = Vector{Ty}(undef, m)
mul!(y, A, x)
end
Expand Down Expand Up @@ -1531,23 +1531,62 @@ end

function *(transA::Transpose{<:Any,<:StridedMatrix{Ta}}, x::AbstractSparseVector{Tx}) where {Ta,Tx}
require_one_based_indexing(transA, x)
A = transA.parent
m, n = size(A)
length(x) == m || throw(DimensionMismatch())
Ty = promote_type(Ta, Tx)
y = Vector{Ty}(undef, n)
mul!(y, transpose(A), x)
m, n = size(transA)
length(x) == n || throw(DimensionMismatch())
Ty = promote_op(matprod, Ta, Tx)
y = Vector{Ty}(undef, m)
mul!(y, transA, x)
end

mul!(y::AbstractVector{Ty}, transA::Transpose{<:Any,<:StridedMatrix}, x::AbstractSparseVector{Tx}) where {Tx,Ty} =
(A = transA.parent; mul!(y, transpose(A), x, one(Tx), zero(Ty)))
mul!(y, transA, x, one(Tx), zero(Ty))

function mul!(y::AbstractVector, transA::Transpose{<:Any,<:StridedMatrix}, x::AbstractSparseVector, α::Number, β::Number)
require_one_based_indexing(y, transA, x)
m, n = size(transA)
length(x) == n && length(y) == m || throw(DimensionMismatch())
m == 0 && return y
if β != one(β)
β == zero(β) ? fill!(y, zero(eltype(y))) : rmul!(y, β)
end
α == zero(α) && return y

xnzind = nonzeroinds(x)
xnzval = nonzeros(x)
_nnz = length(xnzind)
_nnz == 0 && return y

A = transA.parent
require_one_based_indexing(y, A, x)
m, n = size(A)
length(x) == m && length(y) == n || throw(DimensionMismatch())
n == 0 && return y
Ty = promote_op(matprod, eltype(A), eltype(x))
@inbounds for j = 1:m
s = zero(Ty)
for i = 1:_nnz
s += transpose(A[xnzind[i], j]) * xnzval[i]
end
y[j] += s * α
end
return y
end

# * and mul!(C, adjoint(A), B)

function *(adjA::Adjoint{<:Any,<:StridedMatrix{Ta}}, x::AbstractSparseVector{Tx}) where {Ta,Tx}
require_one_based_indexing(adjA, x)
m, n = size(adjA)
length(x) == n || throw(DimensionMismatch())
Ty = promote_op(matprod, Ta, Tx)
y = Vector{Ty}(undef, m)
mul!(y, adjA, x)
end

mul!(y::AbstractVector{Ty}, adjA::Adjoint{<:Any,<:StridedMatrix}, x::AbstractSparseVector{Tx}) where {Tx,Ty} =
mul!(y, adjA, x, one(Tx), zero(Ty))

function mul!(y::AbstractVector, adjA::Adjoint{<:Any,<:StridedMatrix}, x::AbstractSparseVector, α::Number, β::Number)
require_one_based_indexing(y, adjA, x)
m, n = size(adjA)
length(x) == n && length(y) == m || throw(DimensionMismatch())
m == 0 && return y
if β != one(β)
β == zero(β) ? fill!(y, zero(eltype(y))) : rmul!(y, β)
end
Expand All @@ -1558,11 +1597,12 @@ function mul!(y::AbstractVector, transA::Transpose{<:Any,<:StridedMatrix}, x::Ab
_nnz = length(xnzind)
_nnz == 0 && return y

s0 = zero(eltype(A)) * zero(eltype(x))
@inbounds for j = 1:n
s = zero(s0)
A = adjA.parent
Ty = promote_op(matprod, eltype(A), eltype(x))
@inbounds for j = 1:m
s = zero(Ty)
for i = 1:_nnz
s += A[xnzind[i], j] * xnzval[i]
s += adjoint(A[xnzind[i], j]) * xnzval[i]
end
y[j] += s * α
end
Expand Down
58 changes: 37 additions & 21 deletions stdlib/SparseArrays/test/sparsevector.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -841,30 +841,46 @@ end

@testset "BLAS Level-2" begin
@testset "dense A * sparse x -> dense y" begin
let A = randn(9, 16), x = sprand(16, 0.7)
xf = Array(x)
for α in [0.0, 1.0, 2.0], β in [0.0, 0.5, 1.0]
y = rand(9)
rr = α*A*xf + β*y
@test mul!(y, A, x, α, β) === y
@test y ≈ rr
for TA in (Float64, ComplexF64), Tx in (Float64, ComplexF64)
T = Base.promote_op(LinearAlgebra.matprod, TA, Tx)
let A = randn(TA, 9, 16), x = sprand(Tx, 16, 0.7)
xf = Array(x)
for α in [0.0, 1.0, 2.0], β in [0.0, 0.5, 1.0]
y = rand(T, 9)
rr = α*A*xf + β*y
@test mul!(y, A, x, α, β) === y
@test y ≈ rr
end
y = A*x
@test isa(y, Vector{T})
@test A*x ≈ A*xf
end
y = A*x
@test isa(y, Vector{Float64})
@test A*x ≈ A*xf
end

let A = randn(16, 9), x = sprand(16, 0.7)
xf = Array(x)
for α in [0.0, 1.0, 2.0], β in [0.0, 0.5, 1.0]
y = rand(9)
rr = α*A'xf + β*y
@test mul!(y, transpose(A), x, α, β) === y
@test y ≈ rr
let A = randn(TA, 16, 9), x = sprand(Tx, 16, 0.7)
xf = Array(x)
for α in [0.0, 1.0, 2.0], β in [0.0, 0.5, 1.0]
y = rand(T, 9)
rr = α*transpose(A)*xf + β*y
@test mul!(y, transpose(A), x, α, β) === y
@test y ≈ rr
end
y = *(transpose(A), x)
@test isa(y, Vector{T})
@test y ≈ *(transpose(A), xf)
end

let A = randn(TA, 16, 9), x = sprand(Tx, 16, 0.7)
xf = Array(x)
for α in [0.0, 1.0, 2.0], β in [0.0, 0.5, 1.0]
y = rand(T, 9)
rr = α*A'xf + β*y
@test mul!(y, adjoint(A), x, α, β) === y
@test y ≈ rr
end
y = *(adjoint(A), x)
@test isa(y, Vector{T})
@test y ≈ *(adjoint(A), xf)
end
y = *(transpose(A), x)
@test isa(y, Vector{Float64})
@test y ≈ *(transpose(A), xf)
end
end
@testset "sparse A * sparse x -> dense y" begin
Expand Down