Merge pull request #439 from JuliaSparse/backports-release-1.10

Backports for Julia v1.10

docs/Project.toml

@@ -1,2 +1,5 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+
+[compat]
+Documenter = "1"

docs/make.jl

@@ -10,8 +10,7 @@ makedocs(
         "SparseArrays" => "index.md",
         "Sparse Linear Algebra" => "solvers.md",
         ];
-    # strict = true,
-    strict = Symbol[:doctest],
+    warnonly = [:missing_docs, :cross_references],
     )
 
 deploydocs(repo = "github.com/JuliaSparse/SparseArrays.jl.git")

docs/src/solvers.md

@@ -14,11 +14,14 @@ Sparse matrix solvers call functions from [SuiteSparse](http://suitesparse.com).
 
 Other solvers such as [Pardiso.jl](https://github.com/JuliaSparse/Pardiso.jl/) are available as external packages. [Arpack.jl](https://julialinearalgebra.github.io/Arpack.jl/stable/) provides `eigs` and `svds` for iterative solution of eigensystems and singular value decompositions.
 
-These factorizations are described in more detail in [`Linear Algebra`](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/) section of the manual:
-1. [`cholesky`](@ref)
-2. [`ldlt`](@ref)
-3. [`lu`](@ref)
-4. [`qr`](@ref)
+These factorizations are described in more detail in the
+[`Linear Algebra`](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/)
+section of the manual:
+
+1. [`cholesky`](@ref SparseArrays.CHOLMOD.cholesky)
+2. [`ldlt`](@ref SparseArrays.CHOLMOD.ldlt)
+3. [`lu`](@ref SparseArrays.UMFPACK.lu)
+4. [`qr`](@ref SparseArrays.SPQR.qr)
 
 ```@docs
 SparseArrays.CHOLMOD.cholesky

src/linalg.jl

@@ -560,7 +560,7 @@ function LinearAlgebra.generic_trimatmul!(C::StridedVecOrMat, uploc, isunitc, tf
     end
     return C
 end
-function LinearAlgebra.generic_trimatmul!(C::StridedVecOrMat, uploc, isunitc, _, xA::AdjOrTrans{<:Any,<:SparseMatrixCSCUnion}, B::AbstractVecOrMat)
+function LinearAlgebra.generic_trimatmul!(C::StridedVecOrMat, uploc, isunitc, ::Function, xA::AdjOrTrans{<:Any,<:SparseMatrixCSCUnion}, B::AbstractVecOrMat)
     A = parent(xA)
     nrowC = size(C, 1)
     ncol = checksquare(A)

src/solvers/cholmod.jl

@@ -18,7 +18,8 @@ using LinearAlgebra
 using LinearAlgebra: RealHermSymComplexHerm, AdjOrTrans
 import LinearAlgebra: (\), AdjointFactorization,
                  cholesky, cholesky!, det, diag, ishermitian, isposdef,
-                 issuccess, issymmetric, ldlt, ldlt!, logdet, lowrankdowndate!
+                 issuccess, issymmetric, ldlt, ldlt!, logdet,
+                 lowrankdowndate, lowrankdowndate!, lowrankupdate, lowrankupdate!
 
 using SparseArrays
 using SparseArrays: getcolptr, AbstractSparseVecOrMat
@@ -1549,7 +1550,7 @@ factor will be `L*L' == P*A*P' + C'*C`
 
 `update`: `Cint(1)` for `A + CC'`, `Cint(0)` for `A - CC'`
 """
-lowrankdowndate!
+lowrankupdowndate!
 
 #Helper functions for rank updates
 lowrank_reorder(V::AbstractArray,p) = Sparse(sparse(V[p,:]))
@@ -1598,7 +1599,7 @@ lowrankupdate(F::Factor{Tv}, V::AbstractArray{Tv}) where {Tv<:VTypes} =
     lowrankupdate!(copy(F), V)
 
 """
-    lowrankupdate(F::CHOLMOD.Factor, C::AbstractArray) -> FF::CHOLMOD.Factor
+    lowrankdowndate(F::CHOLMOD.Factor, C::AbstractArray) -> FF::CHOLMOD.Factor
 
 Get an `LDLt` Factorization of `A + C*C'` given an `LDLt` or `LLt` factorization `F` of `A`.
 

src/sparsematrix.jl

@@ -344,6 +344,26 @@ function Base.print_array(io::IO, S::AbstractSparseMatrixCSCInclAdjointAndTransp
     end
 end
 
+"""
+    ColumnIndices(S::AbstractSparseMatrixCSC)
+
+Return the column indices of the stored values in `S`.
+This is an internal type that is used in displaying sparse matrices,
+and is not a part of the public interface.
+"""
+struct ColumnIndices{Ti,S<:AbstractSparseMatrixCSC{<:Any,Ti}} <: AbstractVector{Ti}
+    arr :: S
+end
+
+size(C::ColumnIndices) = (nnz(C.arr),)
+# returns the column index of the n-th non-zero value from the column pointer
+@inline function getindex(C::ColumnIndices, i::Int)
+    @boundscheck checkbounds(C, i)
+    colptr = getcolptr(C.arr)
+    ind = searchsortedlast(colptr, i)
+    eltype(C)(ind)
+end
+
 # always show matrices as `sparse(I, J, K)`
 function Base.show(io::IO, _S::AbstractSparseMatrixCSCInclAdjointAndTranspose)
     _checkbuffers(_S)
@@ -358,21 +378,7 @@ function Base.show(io::IO, _S::AbstractSparseMatrixCSCInclAdjointAndTranspose)
         print(io, "transpose(")
     end
     print(io, "sparse(", I, ", ")
-    if length(I) == 0
-        print(io, eltype(getcolptr(S)), "[]")
-    else
-        print(io, "[")
-        il = nnz(S) - 1
-        for col in 1:size(S, 2),
-            k in getcolptr(S)[col] : (getcolptr(S)[col+1]-1)
-            print(io, col)
-            if il > 0
-                print(io, ", ")
-                il -= 1
-            end
-        end
-        print(io, "]")
-    end
+    show(io, ColumnIndices(S))
     print(io, ", ", K, ", ", m, ", ", n, ")")
     if _S isa Adjoint || _S isa Transpose
         print(io, ")")

src/sparsevector.jl

@@ -1108,7 +1108,9 @@ function hcat(Xin::AbstractSparseVector...)
     X = map(_unsafe_unfix, Xin)
     Tv = promote_type(map(eltype, X)...)
     Ti = promote_type(map(indtype, X)...)
-    r = _absspvec_hcat(map(x -> convert(SparseVector{Tv,Ti}, x), X)...)
+    r = (function (::Type{SV}) where SV
+        _absspvec_hcat(map(x -> convert(SV, x), X)...)
+    end)(SparseVector{Tv,Ti})
     return @if_move_fixed Xin... r
 end
 function _absspvec_hcat(X::AbstractSparseVector{Tv,Ti}...) where {Tv,Ti}
@@ -1144,7 +1146,9 @@ function vcat(Xin::AbstractSparseVector...)
     X = map(_unsafe_unfix, Xin)
     Tv = promote_type(map(eltype, X)...)
     Ti = promote_type(map(indtype, X)...)
-    r = _absspvec_vcat(map(x -> convert(SparseVector{Tv,Ti}, x), X)...)
+    r = (function (::Type{SV}) where SV
+            _absspvec_vcat(map(x -> convert(SV, x), X)...)
+        end)(SparseVector{Tv,Ti})
     return @if_move_fixed Xin... r
 end
 function _absspvec_vcat(X::AbstractSparseVector{Tv,Ti}...) where {Tv,Ti}
@@ -1193,14 +1197,16 @@ _makesparse(x::AbstractMatrix) = convert(SparseMatrixCSC, issparse(x) ? x : spar
 anysparse() = false
 anysparse(X) = X isa AbstractArray && issparse(X)
 anysparse(X, Xs...) = anysparse(X) || anysparse(Xs...)
+anysparse(X::T, Xs::T...) where {T} = anysparse(X)
 
-function hcat(X::Union{Vector, AbstractSparseVector}...)
+const _SparseVecConcatGroup = Union{Vector, AbstractSparseVector}
+function hcat(X::_SparseVecConcatGroup...)
     if anysparse(X...)
         X = map(sparse, X)
     end
     return cat(X...; dims=Val(2))
 end
-function vcat(X::Union{Vector, AbstractSparseVector}...)
+function vcat(X::_SparseVecConcatGroup...)
     if anysparse(X...)
         X = map(sparse, X)
     end
@@ -1213,30 +1219,30 @@ end
 const _SparseConcatGroup = Union{AbstractVecOrMat{<:Number},Number}
 
 # `@constprop :aggressive` allows `dims` to be propagated as constant improving return type inference
-Base.@constprop :aggressive function Base._cat(dims, X::_SparseConcatGroup...)
-    T = promote_eltype(X...)
-    if anysparse(X...)
-        X = (_sparse(first(X)), map(_makesparse, Base.tail(X))...)
+Base.@constprop :aggressive function Base._cat(dims, X1::_SparseConcatGroup, X::_SparseConcatGroup...)
+    T = promote_eltype(X1, X...)
+    if anysparse(X1) || anysparse(X...)
+        X1, X = _sparse(X1), map(_makesparse, X)
     end
-    return Base._cat_t(dims, T, X...)
+    return Base._cat_t(dims, T, X1, X...)
 end
-function hcat(X::_SparseConcatGroup...)
-    if anysparse(X...)
-        X = (_sparse(first(X)), map(_makesparse, Base.tail(X))...)
+function hcat(X1::_SparseConcatGroup, X::_SparseConcatGroup...)
+    if anysparse(X1) || anysparse(X...)
+        X1, X = _sparse(X1), map(_makesparse, X)
     end
-    return cat(X..., dims=Val(2))
+    return Base.typed_hcat(Base.promote_eltype(X1, X...), X1, X...)
 end
-function vcat(X::_SparseConcatGroup...)
-    if anysparse(X...)
-        X = (_sparse(first(X)), map(_makesparse, Base.tail(X))...)
+function vcat(X1::_SparseConcatGroup, X::_SparseConcatGroup...)
+    if anysparse(X1) || anysparse(X...)
+        X1, X = _sparse(X1), map(_makesparse, X)
     end
-    return cat(X..., dims=Val(1))
+    return Base.typed_vcat(Base.promote_eltype(X1, X...), X1, X...)
 end
-function hvcat(rows::Tuple{Vararg{Int}}, X::_SparseConcatGroup...)
-    if anysparse(X...)
-        vcat(_hvcat_rows(rows, X...)...)
+function hvcat(rows::Tuple{Vararg{Int}}, X1::_SparseConcatGroup, X::_SparseConcatGroup...)
+    if anysparse(X1) || anysparse(X...)
+        vcat(_hvcat_rows(rows, X1, X...)...)
     else
-        Base.typed_hvcat(Base.promote_eltypeof(X...), rows, X...)
+        Base.typed_hvcat(Base.promote_eltypeof(X1, X...), rows, X1, X...)
     end
 end
 function _hvcat_rows((row1, rows...)::Tuple{Vararg{Int}}, X::_SparseConcatGroup...)
@@ -1254,6 +1260,15 @@ function _hvcat_rows((row1, rows...)::Tuple{Vararg{Int}}, X::_SparseConcatGroup.
 end
 _hvcat_rows(::Tuple{}, X::_SparseConcatGroup...) = ()
 
+# disambiguation for type-piracy problems created above
+hcat(n1::Number, ns::Vararg{Number}) = invoke(hcat, Tuple{Vararg{Number}}, n1, ns...)
+vcat(n1::Number, ns::Vararg{Number}) = invoke(vcat, Tuple{Vararg{Number}}, n1, ns...)
+hcat(n1::Type{N}, ns::Vararg{N}) where {N<:Number} = invoke(hcat, Tuple{Vararg{Number}}, n1, ns...)
+vcat(n1::Type{N}, ns::Vararg{N}) where {N<:Number} = invoke(vcat, Tuple{Vararg{Number}}, n1, ns...)
+hvcat(rows::Tuple{Vararg{Int}}, n1::Number, ns::Vararg{Number}) = invoke(hvcat, Tuple{typeof(rows), Vararg{Number}}, rows, n1, ns...)
+hvcat(rows::Tuple{Vararg{Int}}, n1::N, ns::Vararg{N}) where {N<:Number} = invoke(hvcat, Tuple{typeof(rows), Vararg{N}}, rows, n1, ns...)
+
+
 # make sure UniformScaling objects are converted to sparse matrices for concatenation
 promote_to_array_type(A::Tuple{Vararg{Union{_SparseConcatGroup,UniformScaling}}}) = anysparse(A...) ? SparseMatrixCSC : Matrix
 promote_to_arrays_(n::Int, ::Type{SparseMatrixCSC}, J::UniformScaling) = sparse(J, n, n)

test/ambiguous.jl

@@ -25,13 +25,13 @@ using Test, LinearAlgebra, SparseArrays, Aqua
 
 @testset "code quality" begin
     @testset "Method ambiguity" begin
-        # Aqua.test_ambiguities([SparseArrays, Base, Core])
+        Aqua.test_ambiguities([SparseArrays, Base, Core])
     end
     @testset "Unbound type parameters" begin
         @test_broken Aqua.detect_unbound_args_recursively(SparseArrays) == []
     end
     @testset "Undefined exports" begin
-        Aqua.test_undefined_exports(SparseArrays) == []
+        Aqua.test_undefined_exports(SparseArrays)
     end
     @testset "Compare Project.toml and test/Project.toml" begin
         Aqua.test_project_extras(SparseArrays)
@@ -50,8 +50,23 @@ using Test, LinearAlgebra, SparseArrays, Aqua
     end
 end
 
-@testset "detect_ambiguities" begin
-    @test_nowarn detect_ambiguities(SparseArrays; recursive=true, ambiguous_bottom=false)
+let ambig = detect_ambiguities(SparseArrays; recursive=true)
+    @test isempty(ambig)
+    ambig = Set{Any}(((m1.sig, m2.sig) for (m1, m2) in ambig))
+    expect = []
+    good = true
+    while !isempty(ambig)
+        sigs = pop!(ambig)
+        i = findfirst(==(sigs), expect)
+        if i === nothing
+            println(stderr, "push!(expect, (", sigs[1], ", ", sigs[2], "))")
+            good = false
+            continue
+        end
+        deleteat!(expect, i)
+    end
+    @test isempty(expect)
+    @test good
 end
 
 ## This was the older version that was disabled

test/linalg.jl

@@ -162,6 +162,18 @@ begin
         vMAW = tr(wr(view(MA, :, 1:n)))
         @test vAW * B ≈ vMAW * B
     end
+    a = sprand(rng, ComplexF64, n, n, 0.01)
+    ma = Matrix(a)
+    @testset "triangular multiply with conjugate matrices" for tr in (x -> adjoint(transpose(x)), x -> transpose(adjoint(x))),
+        wr in (UpperTriangular, LowerTriangular, UnitUpperTriangular, UnitLowerTriangular)
+        AW = tr(wr(a))
+        MAW = tr(wr(ma))
+        @test AW * B ≈ MAW * B
+        # and for SparseMatrixCSCView - a view of all rows and unit range of cols
+        vAW = tr(wr(view(a, :, 1:n)))
+        vMAW = tr(wr(view(ma, :, 1:n)))
+        @test vAW * B ≈ vMAW * B
+    end
     A = A - Diagonal(diag(A)) + 2I # avoid rounding errors by division
     MA = Matrix(A)
     @testset "triangular solver for $tr($wr)" for tr in (identity, adjoint, transpose),

test/sparsematrix_constructors_indexing.jl

@@ -1574,6 +1574,15 @@ end
     _show_with_braille_patterns(ioc, _filled_sparse(8, 16))
     @test String(take!(io)) == "⎡⣿⣿⎤\n" *
                                "⎣⣿⣿⎦"
+
+    # respect IOContext while displaying J
+    I, J, V = shuffle(1:50), shuffle(1:50), [1:50;]
+    S = sparse(I, J, V)
+    I, J, V = I[sortperm(J)], sort(J), V[sortperm(J)]
+    @test repr(S) == "sparse($I, $J, $V, $(size(S,1)), $(size(S,2)))"
+    limctxt(x) = repr(x, context=:limit=>true)
+    expstr = "sparse($(limctxt(I)), $(limctxt(J)), $(limctxt(V)), $(size(S,1)), $(size(S,2)))"
+    @test limctxt(S) == expstr
 end
 
 @testset "issparse for specialized matrix types" begin