Deprecate adjoint(v::AbstractVector)/transpose(v::AbstractVector) to …

…Adjoint(v)/Transpose(v).

base/deprecated.jl

@@ -2540,6 +2540,27 @@ end
     @deprecate RowVector{T}(n::Tuple{Int,Int}) where {T}    RowVector{T}(uninitialized, n)
 end
 
+# deprecate adjoint(v)/tranpose(v) for v<:AbstractVector in favor of Adjoint/Transpose
+@eval Base.LinAlg begin
+    @deprecate adjoint(sv::AbstractVector)      Adjoint(sv)
+    @deprecate transpose(sv::AbstractVector)    Transpose(sv)
+end
+@eval Base.SparseArrays begin
+    @deprecate adjoint(sv::SparseVector)    Adjoint(sv)
+    @deprecate transpose(sv::SparseVector)  Transpose(sv)
+end
+@eval Base.LinAlg begin
+    @deprecate adjoint(B::BitVector) Adjoint(B)
+end
+@eval Base.LinAlg begin
+    function adjoint(A::AbstractVector{<:Real})
+        Base.depwarn(string("`adjoint(A::AbstractVector{<:Real})` yielding a ",
+            "`RowVector{eltype(A),typeof(A)}` has been deprecated in favor of Transpose(A) ",
+            "(or `Adjoint(A)`, depending on the existing code's intent)."), :adjoint)
+        return Transpose(A)
+    end
+end
+
 # A[ct]_(mul|ldiv|rdiv)_B[ct][!] methods from base/operators.jl, to deprecate
 @deprecate Ac_ldiv_Bt(a,b)  (\)(Adjoint(a), Transpose(b))
 @deprecate At_ldiv_Bt(a,b)  (\)(Transpose(a), Transpose(b))

base/linalg/adjtrans.jl

@@ -64,10 +64,6 @@ transpose(A::Transpose) = copy(A.parent)
 adjoint(A::Transpose) = conj!(copy(A.parent))
 transpose(A::Adjoint) = conj!(copy(A.parent))
 
-# lowercase quasi-constructors for vectors, TODO: deprecate
-adjoint(sv::AbstractVector) = Adjoint(sv)
-transpose(sv::AbstractVector) = Transpose(sv)
-
 
 # some aliases for internal convenience use
 const AdjOrTrans{T,S} = Union{Adjoint{T,S},Transpose{T,S}} where {T,S}

base/linalg/bitarray.jl

@@ -284,4 +284,4 @@ function transpose(B::BitMatrix)
     return Bt
 end
 
-adjoint(B::Union{BitMatrix,BitVector}) = transpose(B)
+adjoint(B::BitMatrix) = transpose(B)

base/linalg/transpose.jl

@@ -180,7 +180,6 @@ function adjoint(A::AbstractMatrix)
     adjoint!(B, A)
 end
 
-@inline adjoint(A::AbstractVector{<:Real}) = transpose(A)
 @inline adjoint(A::AbstractMatrix{<:Real}) = transpose(A)
 
 function copy_transpose!(B::AbstractVecOrMat, ir_dest::AbstractRange{Int}, jr_dest::AbstractRange{Int},

base/sparse/sparsevector.jl

@@ -1435,11 +1435,6 @@ vecnorm(x::SparseVectorUnion, p::Real=2) = vecnorm(nonzeros(x), p)
 
 ### linalg.jl
 
-# Transpose
-# (The only sparse matrix structure in base is CSC, so a one-row sparse matrix is worse than dense)
-transpose(sv::SparseVector) = Transpose(sv)
-adjoint(sv::SparseVector) = Adjoint(sv)
-
 ### BLAS Level-1
 
 # axpy

test/sparse/higherorderfns.jl

@@ -415,8 +415,10 @@ end
         @test broadcast(*, s, V, A, X)::SparseMatrixCSC == sparse(broadcast(*, s, fV, fA, X))
         @test broadcast!(*, Z, s, V, A, X) == sparse(broadcast(*, s, fV, fA, X))
         # Issue #20954 combinations of sparse arrays and Adjoint/Transpose vectors
-        @test broadcast(+, A, adjoint(X))::SparseMatrixCSC == sparse(broadcast(+, fA, adjoint(X)))
-        @test broadcast(*, V, adjoint(X))::SparseMatrixCSC == sparse(broadcast(*, fV, adjoint(X)))
+        if X isa AbstractVector
+            @test broadcast(+, A, Adjoint(X))::SparseMatrixCSC == sparse(broadcast(+, fA, Adjoint(X)))
+            @test broadcast(*, V, Adjoint(X))::SparseMatrixCSC == sparse(broadcast(*, fV, Adjoint(X)))
+        end
     end
     @test V .+ ntuple(identity, N) isa Vector
     @test A .+ ntuple(identity, N) isa Matrix

test/sparse/sparse.jl

@@ -163,7 +163,7 @@ end
         am = sprand(1, 20, 0.2)
         av = squeeze(am, 1)
         @test ndims(av) == 1
-        @test all(transpose(av) .== am)
+        @test all(Transpose(av) .== am)
     end
 end
 
@@ -1313,7 +1313,7 @@ end
 end
 
 @testset "issue #9917" begin
-    @test sparse(adjoint([])) == reshape(sparse([]), 1, 0)
+    @test sparse(Adjoint([])) == reshape(sparse([]), 1, 0)
     @test Array(sparse([])) == zeros(0)
     @test_throws BoundsError sparse([])[1]
     @test_throws BoundsError sparse([])[1] = 1

test/sparse/sparsevector.jl

@@ -385,14 +385,14 @@ end
     @test float(af) == af
     @test isa(af, SparseVector{Float64,Int})
     @test exact_equal(af, SparseVector(8, [2, 5, 6], [12., 35., 72.]))
-    @test sparsevec(transpose(transpose(af))) == af
+    @test sparsevec(Transpose(Transpose(af))) == af
 
     # complex
     acp = complex(af)
     @test complex(acp) == acp
     @test isa(acp, SparseVector{ComplexF64,Int})
     @test exact_equal(acp, SparseVector(8, [2, 5, 6], complex([12., 35., 72.])))
-    @test sparsevec(adjoint(adjoint(acp))) == acp
+    @test sparsevec(Adjoint(Adjoint(acp))) == acp
 end
 
 @testset "Type conversion" begin