Revert "Support ⊙ and ⊗ as elementwise- and tensor-product operators (#35150)"

This reverts commit f36036c.

This was viewed by some as premature, and there seems to be willingness to develop
it in a package first with an eye towards future inclsion.

Author: timholy

NEWS.md

@@ -182,8 +182,6 @@ Standard library changes
 * The BLAS submodule now supports the level-2 BLAS subroutine `spmv!` ([#34320]).
 * The BLAS submodule now supports the level-1 BLAS subroutine `rot!` ([#35124]).
 * New generic `rotate!(x, y, c, s)` and `reflect!(x, y, c, s)` functions ([#35124]).
-* `hadamard` or `⊙` (`\odotTAB`) can be used as an elementwise multiplication operator,
-  and `tensor` or `⊗` (`\otimesTAB`) as the tensor product operator ([#35150]).
 
 #### Markdown
 

stdlib/LinearAlgebra/docs/src/index.md

@@ -321,8 +321,6 @@ LinearAlgebra.PosDefException
 LinearAlgebra.ZeroPivotException
 LinearAlgebra.dot
 LinearAlgebra.cross
-LinearAlgebra.hadamard
-LinearAlgebra.tensor
 LinearAlgebra.factorize
 LinearAlgebra.Diagonal
 LinearAlgebra.Bidiagonal
@@ -476,8 +474,6 @@ LinearAlgebra.lmul!
 LinearAlgebra.rmul!
 LinearAlgebra.ldiv!
 LinearAlgebra.rdiv!
-LinearAlgebra.hadamard!
-LinearAlgebra.tensor!
 ```
 
 ## BLAS functions

stdlib/LinearAlgebra/src/LinearAlgebra.jl

@@ -388,143 +388,6 @@ const ⋅ = dot
 const × = cross
 export ⋅, ×
 
-"""
-    hadamard(a, b)
-    a ⊙ b
-
-For arrays `a` and `b`, perform elementwise multiplication.
-`a` and `b` must have identical `axes`.
-
-`⊙` can be passed as an operator to higher-order functions.
-
-```jldoctest
-julia> a = [2, 3]; b = [5, 7];
-
-julia> a ⊙ b
-2-element Array{$Int,1}:
- 10
- 21
-
-julia> a ⊙ [5]
-ERROR: DimensionMismatch("Axes of `A` and `B` must match, got (Base.OneTo(2),) and (Base.OneTo(1),)")
-[...]
-```
-
-!!! compat "Julia 1.5"
-    This function requires at least Julia 1.5. In Julia 1.0-1.4 it is available from
-    the `Compat` package.
-"""
-function hadamard(A::AbstractArray, B::AbstractArray)
-    @noinline throw_dmm(axA, axB) = throw(DimensionMismatch("Axes of `A` and `B` must match, got $axA and $axB"))
-
-    axA, axB = axes(A), axes(B)
-    axA == axB || throw_dmm(axA, axB)
-    return map(*, A, B)
-end
-const ⊙ = hadamard
-
-"""
-    hadamard!(dest, A, B)
-
-Similar to `hadamard(A, B)` (which can also be written `A ⊙ B`), but stores its results in
-the pre-allocated array `dest`.
-
-!!! compat "Julia 1.5"
-    This function requires at least Julia 1.5. In Julia 1.0-1.4 it is available from
-    the `Compat` package.
-"""
-function hadamard!(dest::AbstractArray, A::AbstractArray, B::AbstractArray)
-    @noinline function throw_dmm(axA, axB, axdest)
-        throw(DimensionMismatch("`axes(dest) = $axdest` must be equal to `axes(A) = $axA` and `axes(B) = $axB`"))
-    end
-
-    axA, axB, axdest = axes(A), axes(B), axes(dest)
-    ((axdest == axA) & (axdest == axB)) || throw_dmm(axA, axB, axdest)
-    @simd for I in eachindex(dest, A, B)
-        @inbounds dest[I] = A[I] * B[I]
-    end
-    return dest
-end
-
-export ⊙, hadamard, hadamard!
-
-"""
-    tensor(A, B)
-    A ⊗ B
-
-Compute the tensor product of `A` and `B`.
-If `C = A ⊗ B`, then `C[i1, ..., im, j1, ..., jn] = A[i1, ... im] * B[j1, ..., jn]`.
-
-```jldoctest
-julia> a = [2, 3]; b = [5, 7, 11];
-
-julia> a ⊗ b
-2×3 Array{$Int,2}:
- 10  14  22
- 15  21  33
-```
-
-See also: [`kron`](@ref).
-
-!!! compat "Julia 1.5"
-    This function requires at least Julia 1.5. In Julia 1.0-1.4 it is available from
-    the `Compat` package.
-"""
-tensor(A::AbstractArray, B::AbstractArray) = [a*b for a in A, b in B]
-const ⊗ = tensor
-
-const CovectorLike{T} = Union{Adjoint{T,<:AbstractVector},Transpose{T,<:AbstractVector}}
-function tensor(u::AbstractArray, v::CovectorLike)
-    # If `v` is thought of as a covector, you might want this to be two-dimensional,
-    # but thought of as a matrix it should be three-dimensional.
-    # The safest is to avoid supporting it at all. See discussion in #35150.
-    error("`tensor` is not defined for co-vectors, perhaps you meant `*`?")
-end
-function tensor(u::CovectorLike, v::AbstractArray)
-    error("`tensor` is not defined for co-vectors, perhaps you meant `*`?")
-end
-function tensor(u::CovectorLike, v::CovectorLike)
-    error("`tensor` is not defined for co-vectors, perhaps you meant `*`?")
-end
-
-"""
-    tensor!(dest, A, B)
-
-Similar to `tensor(A, B)` (which can also be written `A ⊗ B`), but stores its results in
-the pre-allocated array `dest`.
-
-!!! compat "Julia 1.5"
-    This function requires at least Julia 1.5. In Julia 1.0-1.4 it is available from
-    the `Compat` package.
-"""
-function tensor!(dest::AbstractArray, A::AbstractArray, B::AbstractArray)
-    @noinline function throw_dmm(axA, axB, axdest)
-        throw(DimensionMismatch("`axes(dest) = $axdest` must concatenate `axes(A) = $axA` and `axes(B) = $axB`"))
-    end
-
-    axA, axB, axdest = axes(A), axes(B), axes(dest)
-    axes(dest) == (axA..., axB...) || throw_dmm(axA, axB, axdest)
-    if IndexStyle(dest) === IndexCartesian()
-        for IB in CartesianIndices(axB)
-            @inbounds b = B[IB]
-            @simd for IA in CartesianIndices(axA)
-                @inbounds dest[IA,IB] = A[IA]*b
-            end
-        end
-    else
-        i = firstindex(dest)
-        @inbounds for b in B
-            @simd for a in A
-                dest[i] = a*b
-                i += 1
-            end
-        end
-    end
-    return dest
-end
-
-export ⊗, tensor, tensor!
-
 """
     LinearAlgebra.peakflops(n::Integer=2000; parallel::Bool=false)
 

stdlib/LinearAlgebra/src/dense.jl

@@ -341,9 +341,9 @@ end
 
 Kronecker tensor product of two vectors or two matrices.
 
-For vectors v and w, the Kronecker product is related to the tensor product [`tensor`](@ref), or `⊗`, by
-`kron(v,w) == vec(w ⊗ v)` or
-`w ⊗ v == reshape(kron(v,w), (length(w), length(v)))`.
+For vectors v and w, the Kronecker product is related to the outer product by
+`kron(v,w) == vec(w*transpose(v))` or
+`w*transpose(v) == reshape(kron(v,w), (length(w), length(v)))`.
 Note how the ordering of `v` and `w` differs on the left and right
 of these expressions (due to column-major storage).
 

stdlib/LinearAlgebra/test/addmul.jl

@@ -131,66 +131,6 @@ for cmat in mattypes,
     push!(testdata, (cmat{celt}, amat{aelt}, bmat{belt}))
 end
 
-@testset "Alternative multiplication operators" begin
-    for T in (Int, Float32, Float64, BigFloat)
-        a = [T[1, 2], T[-3, 7]]
-        b = [T[5, 11], T[-13, 17]]
-        @test map(⋅, a, b) == map(dot, a, b) == [27, 158]
-        @test map(⊙, a, b) == map(hadamard, a, b) == [a[1].*b[1], a[2].*b[2]]
-        @test map(⊗, a, b) == map(tensor, a, b) == [a[1]*transpose(b[1]), a[2]*transpose(b[2])]
-        @test hadamard!(fill(typemax(Int), 2), T[1, 2], T[-3, 7]) == [-3, 14]
-        @test tensor!(fill(typemax(Int), 2, 2), T[1, 2], T[-3, 7]) == [-3 7; -6 14]
-    end
-
-    @test_throws DimensionMismatch [1,2] ⊙ [3]
-    @test_throws DimensionMismatch hadamard!([0, 0, 0], [1,2], [-3,7])
-    @test_throws DimensionMismatch hadamard!([0, 0], [1,2], [-3])
-    @test_throws DimensionMismatch hadamard!([0, 0], [1], [-3,7])
-    @test_throws DimensionMismatch tensor!(Matrix{Int}(undef, 2, 2), [1], [-3,7])
-    @test_throws DimensionMismatch tensor!(Matrix{Int}(undef, 2, 2), [1,2], [-3])
-
-    u, v = [2+2im, 3+5im], [1-3im, 7+3im]
-    @test u ⋅ v == conj(u[1])*v[1] + conj(u[2])*v[2]
-    @test u ⊙ v == [u[1]*v[1], u[2]*v[2]]
-    @test u ⊗ v == [u[1]*v[1] u[1]*v[2]; u[2]*v[1] u[2]*v[2]]
-    @test hadamard(u, v) == u ⊙ v
-    @test tensor(u, v)   == u ⊗ v
-    dest = similar(u)
-    @test hadamard!(dest, u, v) == u ⊙ v
-    dest = Matrix{Complex{Int}}(undef, 2, 2)
-    @test tensor!(dest, u, v) == u ⊗ v
-
-    for (A, B, b) in (([1 2; 3 4], [5 6; 7 8], [5,6]),
-                      ([1+0.8im 2+0.7im; 3+0.6im 4+0.5im],
-                       [5+0.4im 6+0.3im; 7+0.2im 8+0.1im],
-                       [5+0.6im,6+0.3im]))
-        @test A ⊗ b == cat(A*b[1], A*b[2]; dims=3)
-        @test A ⊗ B == cat(cat(A*B[1,1], A*B[2,1]; dims=3),
-                           cat(A*B[1,2], A*B[2,2]; dims=3); dims=4)
-    end
-
-    A, B = reshape(1:27, 3, 3, 3), reshape(1:4, 2, 2)
-    @test A ⊗ B == [a*b for a in A, b in B]
-
-    # Adjoint/transpose is a dual vector, not an AbstractMatrix
-    v = [1,2]
-    @test_throws ErrorException v ⊗ v'
-    @test_throws ErrorException v ⊗ transpose(v)
-    @test_throws ErrorException v' ⊗ v
-    @test_throws ErrorException transpose(v) ⊗ v
-    @test_throws ErrorException v' ⊗ v'
-    @test_throws ErrorException transpose(v) ⊗ transpose(v)
-    @test_throws ErrorException v' ⊗ transpose(v)
-    @test_throws ErrorException transpose(v) ⊗ v'
-    @test_throws ErrorException A ⊗ v'
-    @test_throws ErrorException A ⊗ transpose(v)
-
-    # Docs comparison to `kron`
-    v, w = [1,2,3], [5,7]
-    @test kron(v,w) == vec(w ⊗ v)
-    @test w ⊗ v == reshape(kron(v,w), (length(w), length(v)))
-end
-
 @testset "mul!(::$TC, ::$TA, ::$TB, α, β)" for (TC, TA, TB) in testdata
     if needsquare(TA)
         na1 = na2 = rand(sizecandidates)