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Backports release 1.12 (#1411)
[x] #1365 [x] #1373 [x] #1385 [x] #1397 [x] #1402 [x] #1413 [x] #1417 [x] #1418
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‎.buildkite/runtests.yml

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gid: 1000
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# Julia installation inside the sandbox
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- JuliaCI/julia#v1:
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version: "nightly"
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version: "1.12"
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arch: "i686"
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command: |
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julia --color=yes --code-coverage=@ .ci/run_tests.jl
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gid: 1000
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# Julia installation inside the sandbox
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- JuliaCI/julia#v1:
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version: "nightly"
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version: "1.12"
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command: |
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julia --color=yes --code-coverage=@ .ci/run_tests.jl
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agents:
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- label: ":macos: macos-aarch64"
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plugins:
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- JuliaCI/julia#v1:
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version: "nightly"
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version: "1.12"
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- JuliaCI/julia-coverage#v1:
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codecov: true
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command: |
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- label: ":windows: windows-x86_64"
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plugins:
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- JuliaCI/julia#v1:
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version: "nightly"
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version: "1.12"
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- JuliaCI/julia-coverage#v1:
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codecov: true
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command: |

‎docs/src/index.md

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@@ -745,7 +745,7 @@ LinearAlgebra.BLAS.trsv
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```@docs
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LinearAlgebra.BLAS.ger!
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# xGERU
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LinearAlgebra.BLAS.geru!
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# xGERC
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LinearAlgebra.BLAS.her!
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# xHPR

‎src/adjtrans.jl

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@@ -536,3 +536,27 @@ conj(A::Adjoint) = transpose(A.parent)
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function Base.replace_in_print_matrix(A::AdjOrTrans,i::Integer,j::Integer,s::AbstractString)
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Base.replace_in_print_matrix(parent(A), j, i, s)
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end
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# Special adjoint/transpose methods for Adjoint/Transpose that have been reshaped as a vector
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# these are used in transposing banded matrices by forwarding the operation to the bands
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"""
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_vectranspose(A::AbstractVector)::AbstractVector
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Compute `vec(transpose(A))`, but avoid an allocating reshape if possible
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"""
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_vectranspose(A::AbstractVector) = vec(transpose(A))
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_vectranspose(A::Base.ReshapedArray{<:Any,1,<:TransposeAbsVec}) = transpose(parent(A))
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"""
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_vecadjoint(A::AbstractVector)::AbstractVector
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Compute `vec(adjoint(A))`, but avoid an allocating reshape if possible
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"""
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_vecadjoint(A::AbstractVector) = vec(adjoint(A))
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_vecadjoint(A::Base.ReshapedArray{<:Any,1,<:AdjointAbsVec}) = adjoint(parent(A))
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diagview(A::Transpose, k::Integer = 0) = _vectranspose(diagview(parent(A), -k))
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diagview(A::Adjoint, k::Integer = 0) = _vecadjoint(diagview(parent(A), -k))
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# triu and tril
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triu!(A::AdjOrTransAbsMat, k::Integer = 0) = wrapperop(A)(tril!(parent(A), -k))
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tril!(A::AdjOrTransAbsMat, k::Integer = 0) = wrapperop(A)(triu!(parent(A), -k))

‎src/factorization.jl

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@@ -110,7 +110,7 @@ Factorization{T}(A::AdjointFactorization) where {T} =
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adjoint(Factorization{T}(parent(A)))
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Factorization{T}(A::TransposeFactorization) where {T} =
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transpose(Factorization{T}(parent(A)))
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inv(F::Factorization{T}) where {T} = (n = size(F, 1); ldiv!(F, Matrix{T}(I, n, n)))
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inv(F::Factorization{T}) where {T} = (n = checksquare(F); ldiv!(F, Matrix{T}(I, n, n)))
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Base.hash(F::Factorization, h::UInt) = mapreduce(f -> hash(getfield(F, f)), hash, 1:nfields(F); init=h)
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Base.:(==)( F::T, G::T) where {T<:Factorization} = all(f -> getfield(F, f) == getfield(G, f), 1:nfields(F))

‎src/generic.jl

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@@ -1821,7 +1821,7 @@ julia> det(BigInt[1 0; 2 2]) # exact integer determinant
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function det(A::AbstractMatrix{T}) where {T}
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if istriu(A) || istril(A)
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S = promote_type(T, typeof((one(T)*zero(T) + zero(T))/one(T)))
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return convert(S, det(UpperTriangular(A)))
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return prod(Base.Fix1(convert, S), @view A[diagind(A)]; init=one(S))
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end
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return det(lu(A; check = false))
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end

‎src/matmul.jl

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@@ -968,11 +968,13 @@ function __generic_matvecmul!(::typeof(identity), C::AbstractVector, A::Abstract
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C[i] = zero(A[i]*B[1] + A[i]*B[1])
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end
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end
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for k = eachindex(B)
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aoffs = (k-1)*Astride
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b = @stable_muladdmul MulAddMul(alpha,false)(B[k])
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for i = eachindex(C)
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C[i] += A[aoffs + i] * b
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if !iszero(alpha)
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for k = eachindex(B)
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aoffs = (k-1)*Astride
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b = @stable_muladdmul MulAddMul(alpha,false)(B[k])
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for i = eachindex(C)
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C[i] += A[aoffs + i] * b
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end
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end
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end
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end

‎src/qr.jl

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@@ -535,6 +535,28 @@ function ldiv!(A::QRCompactWY{T}, B::AbstractMatrix{T}) where {T}
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return B
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end
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"""
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rank(A::QRPivoted{<:Any, T}; atol::Real=0, rtol::Real=min(n,m)*ϵ) where {T}
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Compute the numerical rank of the QR factorization `A` by counting how many diagonal entries of
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`A.factors` are greater than `max(atol, rtol*Δ₁)` where `Δ₁` is the largest calculated such entry.
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This is similar to the [`rank(::AbstractMatrix)`](@ref) method insofar as it counts the number of
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(numerically) nonzero coefficients from a matrix factorization, although the default method uses an
545+
SVD instead of a QR factorization. Like [`rank(::SVD)`](@ref), this method also re-uses an existing
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matrix factorization.
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Using a QR factorization to compute rank should typically produce the same result as using SVD,
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although it may be more prone to overestimating the rank in pathological cases where the matrix is
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ill-conditioned. It is also worth noting that it is generally faster to compute a QR factorization
551+
than it is to compute an SVD, so this method may be preferred when performance is a concern.
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`atol` and `rtol` are the absolute and relative tolerances, respectively.
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The default relative tolerance is `n*ϵ`, where `n` is the size of the smallest dimension of `A`
555+
and `ϵ` is the [`eps`](@ref) of the element type of `A`.
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!!! compat "Julia 1.12"
558+
The `rank(::QRPivoted)` method requires at least Julia 1.12.
559+
"""
538560
function rank(A::QRPivoted; atol::Real=0, rtol::Real=min(size(A)...) * eps(real(float(eltype(A)))) * iszero(atol))
539561
m = min(size(A)...)
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m == 0 && return 0

‎src/svd.jl

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@@ -272,6 +272,7 @@ function ldiv!(A::SVD{T}, B::AbstractVecOrMat) where T
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end
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function inv(F::SVD{T}) where T
275+
checksquare(F)
275276
@inbounds for i in eachindex(F.S)
276277
iszero(F.S[i]) && throw(SingularException(i))
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end

‎src/triangular.jl

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@@ -248,16 +248,15 @@ Base.@constprop :aggressive @propagate_inbounds function getindex(A::Union{Lower
248248
_shouldforwardindex(A, b) ? A.data[b] : diagzero(A.data, b)
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end
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251-
_zero_triangular_half_str(::Type{<:UpperOrUnitUpperTriangular}) = "lower"
252-
_zero_triangular_half_str(::Type{<:LowerOrUnitLowerTriangular}) = "upper"
251+
_zero_triangular_half_str(T::Type) = T <: UpperOrUnitUpperTriangular ? "lower" : "upper"
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254-
@noinline function throw_nonzeroerror(T, @nospecialize(x), i, j)
253+
@noinline function throw_nonzeroerror(T::DataType, @nospecialize(x), i, j)
255254
Ts = _zero_triangular_half_str(T)
256255
Tn = nameof(T)
257256
throw(ArgumentError(
258257
lazy"cannot set index in the $Ts triangular part ($i, $j) of an $Tn matrix to a nonzero value ($x)"))
259258
end
260-
@noinline function throw_nononeerror(T, @nospecialize(x), i, j)
259+
@noinline function throw_nononeerror(T::DataType, @nospecialize(x), i, j)
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Tn = nameof(T)
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throw(ArgumentError(
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lazy"cannot set index on the diagonal ($i, $j) of an $Tn matrix to a non-unit value ($x)"))
@@ -303,7 +302,7 @@ end
303302
return A
304303
end
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306-
@noinline function throw_setindex_structuralzero_error(T, @nospecialize(x))
305+
@noinline function throw_setindex_structuralzero_error(T::DataType, @nospecialize(x))
307306
Ts = _zero_triangular_half_str(T)
308307
Tn = nameof(T)
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throw(ArgumentError(

‎test/adjtrans.jl

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end
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end
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752+
@testset "diagview" begin
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for A in (rand(4, 4), rand(ComplexF64,4,4),
754+
fill([1 2; 3 4], 4, 4))
755+
for k in -3:3
756+
@test diagview(A', k) == diag(A', k)
757+
@test diagview(transpose(A), k) == diag(transpose(A), k)
758+
end
759+
@test IndexStyle(diagview(A')) == IndexLinear()
760+
end
761+
end
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763+
@testset "triu!/tril!" begin
764+
@testset for sz in ((4,4), (3,4), (4,3))
765+
A = rand(sz...)
766+
B = similar(A)
767+
@testset for f in (adjoint, transpose), k in -3:3
768+
@test triu!(f(copy!(B, A)), k) == triu(f(A), k)
769+
@test tril!(f(copy!(B, A)), k) == tril!(f(A), k)
770+
end
771+
end
772+
end
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end # module TestAdjointTranspose

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