diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 17f6aeca..bd4c83fe 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -23,33 +23,26 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - 'nightly'
+          - '1.11'
         os:
           - ubuntu-latest
-          - macOS-latest
           - windows-latest
         arch:
           - x64
-          - x86
-        exclude:
+        include:
           - os: macOS-latest
+            arch: aarch64
+            version: '1.11'
+          - os: ubuntu-latest
             arch: x86
+            version: '1.11'
     steps:
       - uses: actions/checkout@v4
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@latest
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: actions/cache@v4
-        env:
-          cache-name: cache-artifacts
-        with:
-          path: ~/.julia/artifacts
-          key: ${{ runner.os }}-test-${{ env.cache-name }}-${{ hashFiles('**/Project.toml') }}
-          restore-keys: |
-            ${{ runner.os }}-test-${{ env.cache-name }}-
-            ${{ runner.os }}-test-${{ matrix.os }}
-            ${{ runner.os }}-
+      - uses: julia-actions/cache@v2
       - run: julia --color=yes .ci/test_and_change_uuid.jl
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
@@ -67,8 +60,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@latest
         with:
-          # version: '1.6'
-          version: 'nightly'
+          version: '1.11'
       - name: Generate docs
         run: |
           julia --color=yes -e 'write("Project.toml", replace(read("Project.toml", String), r"uuid = .*?\n" =>"uuid = \"3f01184e-e22b-5df5-ae63-d93ebab69eaf\"\n"));'
diff --git a/docs/make.jl b/docs/make.jl
index 53843712..df9e506c 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -8,7 +8,6 @@ makedocs(
     sitename = "SparseArrays",
     pages = Any[
         "SparseArrays" => "index.md",
-        "Sparse Linear Algebra" => "solvers.md",
         ];
     warnonly = [:missing_docs, :cross_references],
     )
diff --git a/docs/src/index.md b/docs/src/index.md
index 3868d041..dcfc729f 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -206,6 +206,27 @@ section of the standard library reference.
 | [`sprandn(m,n,d)`](@ref)   | [`randn(m,n)`](@ref)   | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements distributed according to the standard normal (Gaussian) distribution.                  |
 | [`sprandn(rng,m,n,d)`](@ref) | [`randn(rng,m,n)`](@ref) | Creates a *m*-by-*n* random matrix (of density *d*) with iid non-zero elements generated with the `rng` random number generator                                   |
 
+## [Sparse Linear Algebra](@id stdlib-sparse-linalg)
+
+Sparse matrix solvers call functions from [SuiteSparse](http://suitesparse.com). The following factorizations are available:
+
+| Type                  | Description                                   |
+|:----------------------|:--------------------------------------------- |
+| `CHOLMOD.Factor`      | Cholesky and LDLt factorizations              |
+| `UMFPACK.UmfpackLU`   | LU factorization                              |
+| `SPQR.QRSparse`       | QR factorization                              |
+
+These factorizations are described in more detail in the [Sparse Linear Algebra API section](@ref stdlib-sparse-linalg-api):
+
+1. [`cholesky`](@ref SparseArrays.CHOLMOD.cholesky)
+2. [`ldlt`](@ref SparseArrays.CHOLMOD.ldlt)
+3. [`lu`](@ref SparseArrays.UMFPACK.lu)
+4. [`qr`](@ref SparseArrays.SPQR.qr)
+
+```@meta
+DocTestSetup = nothing
+```
+
 # [SparseArrays API](@id stdlib-sparse-arrays)
 
 ```@docs
@@ -245,6 +266,26 @@ SparseArrays.ftranspose!
 ```@meta
 DocTestSetup = nothing
 ```
+
+# [Sparse Linear Algebra API](@id stdlib-sparse-linalg-api)
+
+```@docs
+SparseArrays.CHOLMOD.cholesky
+SparseArrays.CHOLMOD.cholesky!
+SparseArrays.CHOLMOD.lowrankupdate
+SparseArrays.CHOLMOD.lowrankupdate!
+SparseArrays.CHOLMOD.lowrankdowndate
+SparseArrays.CHOLMOD.lowrankdowndate!
+SparseArrays.CHOLMOD.lowrankupdowndate!
+SparseArrays.CHOLMOD.ldlt
+SparseArrays.UMFPACK.lu
+SparseArrays.SPQR.qr
+```
+
+```@meta
+DocTestSetup = nothing
+```
+
 # Noteworthy External Sparse Packages
 
 Several other Julia packages provide sparse matrix implementations that should be mentioned:
@@ -264,3 +305,15 @@ Several other Julia packages provide sparse matrix implementations that should b
 7. [ExtendableSparse.jl](https://github.com/j-fu/ExtendableSparse.jl) enables fast insertion into sparse matrices using a lazy approach to new stored indices.
 
 8. [Finch.jl](https://github.com/willow-ahrens/Finch.jl) supports extensive multidimensional sparse array formats and operations through a mini tensor language and compiler, all in native Julia. Support for COO, CSF, CSR, CSC and more, as well as operations like broadcast, reduce, etc. and custom operations.
+
+External packages providing sparse direct solvers:
+1. [KLU.jl](https://github.com/JuliaSparse/KLU.jl)
+2. [Pardiso.jl](https://github.com/JuliaSparse/Pardiso.jl/)
+
+External packages providing solvers for iterative solution of eigensystems and singular value decompositions:
+1. [ArnoldiMethods.jl](https://github.com/JuliaLinearAlgebra/ArnoldiMethod.jl)
+2. [KrylovKit](https://github.com/Jutho/KrylovKit.jl)
+3. [Arpack.jl](https://github.com/JuliaLinearAlgebra/Arpack.jl)
+
+External packages for working with graphs:
+1. [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl)
diff --git a/docs/src/solvers.md b/docs/src/solvers.md
deleted file mode 100644
index e633be9d..00000000
--- a/docs/src/solvers.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# Sparse Linear Algebra
-
-```@meta
-DocTestSetup = :(using LinearAlgebra, SparseArrays)
-```
-
-Sparse matrix solvers call functions from [SuiteSparse](http://suitesparse.com). The following factorizations are available:
-
-| Type                              | Description                                   |
-|:--------------------------------- |:--------------------------------------------- |
-| `CHOLMOD.Factor`      | Cholesky factorization                        |
-| `UMFPACK.UmfpackLU`   | LU factorization                              |
-| `SPQR.QRSparse`       | QR factorization                              |
-
-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 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
-SparseArrays.CHOLMOD.cholesky!
-SparseArrays.CHOLMOD.ldlt
-SparseArrays.SPQR.qr
-SparseArrays.UMFPACK.lu
-```
-
-```@docs
-SparseArrays.CHOLMOD.lowrankupdate
-SparseArrays.CHOLMOD.lowrankupdate!
-SparseArrays.CHOLMOD.lowrankdowndate
-SparseArrays.CHOLMOD.lowrankdowndate!
-SparseArrays.CHOLMOD.lowrankupdowndate!
-```
-
-```@meta
-DocTestSetup = nothing
-```
diff --git a/src/linalg.jl b/src/linalg.jl
index ca70ac4c..c4d03f97 100644
--- a/src/linalg.jl
+++ b/src/linalg.jl
@@ -47,11 +47,11 @@ for op ∈ (:+, :-)
     end
 end
 
-generic_matmatmul!(C::StridedMatrix, tA, tB, A::SparseMatrixCSCUnion2, B::DenseMatrixUnion, _add::MulAddMul) =
+@inline generic_matmatmul!(C::StridedMatrix, tA, tB, A::SparseMatrixCSCUnion2, B::DenseMatrixUnion, _add::MulAddMul) =
     spdensemul!(C, tA, tB, A, B, _add)
-generic_matmatmul!(C::StridedMatrix, tA, tB, A::SparseMatrixCSCUnion2, B::AbstractTriangular, _add::MulAddMul) =
+@inline generic_matmatmul!(C::StridedMatrix, tA, tB, A::SparseMatrixCSCUnion2, B::AbstractTriangular, _add::MulAddMul) =
     spdensemul!(C, tA, tB, A, B, _add)
-generic_matvecmul!(C::StridedVecOrMat, tA, A::SparseMatrixCSCUnion2, B::DenseInputVector, _add::MulAddMul) =
+@inline generic_matvecmul!(C::StridedVecOrMat, tA, A::SparseMatrixCSCUnion2, B::DenseInputVector, _add::MulAddMul) =
     spdensemul!(C, tA, 'N', A, B, _add)
 
 Base.@constprop :aggressive function spdensemul!(C, tA, tB, A, B, _add)
diff --git a/src/solvers/cholmod.jl b/src/solvers/cholmod.jl
index 117d801b..e155e72f 100644
--- a/src/solvers/cholmod.jl
+++ b/src/solvers/cholmod.jl
@@ -795,7 +795,7 @@ function ssmult(A::Sparse{Tv1, Ti1}, B::Sparse{Tv2, Ti2}, stype::Integer,
     A, B = convert.(Sparse{promote_type(Tv1, Tv2), promote_type(Ti1, Ti2)}, (A, B))
     return ssmult(A, B, stype, values, sorted)
 end
-function horzcat(A::Sparse{Tv1, Ti1}, B::Sparse{Tv2, Ti2}, values::Bool) where 
+function horzcat(A::Sparse{Tv1, Ti1}, B::Sparse{Tv2, Ti2}, values::Bool) where
         {Tv1<:VRealTypes, Tv2<:VRealTypes, Ti1, Ti2}
     A, B = convert.(Sparse{promote_type(Tv1, Tv2), promote_type(Ti1, Ti2)}, (A, B))
     return horzcat(A, B, values)
@@ -809,7 +809,7 @@ function sdmult!(A::Sparse{Tv1, Ti}, transpose::Bool,
     A, X = convert(Sparse{Tv3, Ti}, A), convert(Dense{Tv3}, X)
     return sdmult!(A, transpose, α, β, X, Y)
 end
-function vertcat(A::Sparse{Tv1, Ti1}, B::Sparse{Tv2, Ti2}, values::Bool) where 
+function vertcat(A::Sparse{Tv1, Ti1}, B::Sparse{Tv2, Ti2}, values::Bool) where
         {Tv1<:VRealTypes, Ti1, Tv2<:VRealTypes, Ti2}
     A, B = convert.(Sparse{promote_type(Tv1, Tv2), promote_type(Ti1, Ti2)}, (A, B))
     return vertcat(A, B, values)
@@ -895,7 +895,7 @@ function Dense(A::StridedVecOrMatInclAdjAndTrans)
     return Dense{T}(A)
 end
 # Don't always promote to Float64 now that we have Float32 support.
-Dense(A::StridedVecOrMatInclAdjAndTrans{T}) where 
+Dense(A::StridedVecOrMatInclAdjAndTrans{T}) where
     {T<:Union{Float16, ComplexF16, Float32, ComplexF32}} = Dense{promote_type(T, Float32)}(A)
 
 
@@ -1055,8 +1055,8 @@ Sparse(A::Hermitian{Tv, SparseMatrixCSC{Tv,Ti}}) where {Tv, Ti} =
     Sparse{promote_type(Tv, Float64), Ti <: ITypes ? Ti : promote_type(Ti, Int)}(
         A.data, A.uplo == 'L' ? -1 : 1
     )
-Sparse(A::Hermitian{Tv, SparseMatrixCSC{Tv,Ti}}) where 
-    {Tv<:Union{Float16, Float32, ComplexF32, ComplexF16}, Ti} = 
+Sparse(A::Hermitian{Tv, SparseMatrixCSC{Tv,Ti}}) where
+    {Tv<:Union{Float16, Float32, ComplexF32, ComplexF16}, Ti} =
     Sparse{promote_type(Float32, Tv), Ti <: ITypes ? Ti : promote_type(Ti, Int)}(
         A.data, A.uplo == 'L' ? -1 : 1
     )
@@ -1076,7 +1076,7 @@ function Base.convert(::Type{Sparse{Tnew, Inew}}, A::Sparse{Tv, Ti}) where {Tnew
         a = unsafe_load(typedpointer(A))
         S = allocate_sparse(a.nrow, a.ncol, a.nzmax, Bool(a.sorted), Bool(a.packed), a.stype, Tnew, Inew)
         s = unsafe_load(typedpointer(S))
-        
+
         ap = unsafe_wrap(Array, a.p, (a.ncol + 1,), own = false)
         sp = unsafe_wrap(Array, s.p, (s.ncol + 1,), own = false)
         copyto!(sp, ap)
@@ -1376,7 +1376,7 @@ end
 
 ## Multiplication
 (*)(A::Sparse, B::Sparse) = ssmult(A, B, 0, true, true)
-(*)(A::Sparse, B::Dense) = sdmult!(A, false, 1., 0., B, 
+(*)(A::Sparse, B::Dense) = sdmult!(A, false, 1., 0., B,
     zeros(size(A, 1), size(B, 2), promote_type(eltype(A), eltype(B)))
 )
 (*)(A::Sparse, B::VecOrMat) = (*)(A, Dense(B))
@@ -1413,7 +1413,7 @@ function *(adjA::Adjoint{<:Any,<:Sparse}, B::Sparse)
 end
 
 *(adjA::Adjoint{<:Any,<:Sparse}, B::Dense) = (
-    A = parent(adjA); sdmult!(A, true, 1., 0., B, 
+    A = parent(adjA); sdmult!(A, true, 1., 0., B,
     zeros(size(A, 2), size(B, 2), promote_type(eltype(A), eltype(B))))
 )
 *(adjA::Adjoint{<:Any,<:Sparse}, B::VecOrMat) = adjA * Dense(B)
@@ -1423,25 +1423,33 @@ end
 
 ## Compute that symbolic factorization only
 function symbolic(A::Sparse{<:VTypes, Ti};
-    perm::Union{Nothing,AbstractVector{<:Integer}}=nothing,
-    postorder::Bool=isnothing(perm)||isempty(perm), userperm_only::Bool=true) where Ti
+                  perm::Union{Nothing,AbstractVector{<:Integer}}=nothing,
+                  postorder::Bool=isnothing(perm)||isempty(perm),
+                  userperm_only::Bool=true,
+                  nested_dissection::Bool=false) where Ti
 
     sA = unsafe_load(pointer(A))
     sA.stype == 0 && throw(ArgumentError("sparse matrix is not symmetric/Hermitian"))
 
-    @cholmod_param postorder = postorder begin
-        if perm === nothing || isempty(perm) # TODO: deprecate empty perm
-            return analyze(A)
-        else # user permutation provided
-            if userperm_only # use perm even if it is worse than AMD
-                @cholmod_param nmethods = 1 begin
+    # The default is to just use AMD. Use nested dissection only if explicitly asked for.
+    # https://github.com/JuliaSparse/SparseArrays.jl/issues/548
+    # https://github.com/DrTimothyAldenDavis/SuiteSparse/blob/26ababc7f3af725c5fb9168a1b94850eab74b666/CHOLMOD/Include/cholmod.h#L555-L574
+    @cholmod_param nmethods = (nested_dissection ? 0 : 2) begin
+        @cholmod_param postorder = postorder begin
+            if perm === nothing || isempty(perm) # TODO: deprecate empty perm
+                return analyze(A)
+            else # user permutation provided
+                if userperm_only # use perm even if it is worse than AMD
+                    @cholmod_param nmethods = 1 begin
+                        return analyze_p(A, Ti[p-1 for p in perm])
+                    end
+                else
                     return analyze_p(A, Ti[p-1 for p in perm])
                 end
-            else
-                return analyze_p(A, Ti[p-1 for p in perm])
             end
         end
     end
+
 end
 
 function cholesky!(F::Factor{Tv}, A::Sparse{Tv};
@@ -1467,7 +1475,7 @@ See also [`cholesky`](@ref).
 
 !!! note
     This method uses the CHOLMOD library from SuiteSparse, which only supports
-    real or complex types in single or double precision. 
+    real or complex types in single or double precision.
     Input matrices not of those element types will
     be converted to these types as appropriate.
 """
@@ -1587,8 +1595,8 @@ true
 
 !!! note
     This method uses the CHOLMOD[^ACM887][^DavisHager2009] library from [SuiteSparse](https://github.com/DrTimothyAldenDavis/SuiteSparse).
-    CHOLMOD only supports real or complex types in single or double precision. 
-    Input matrices not of those element types will be 
+    CHOLMOD only supports real or complex types in single or double precision.
+    Input matrices not of those element types will be
     converted to these types as appropriate.
 
     Many other functions from CHOLMOD are wrapped but not exported from the
@@ -1633,8 +1641,8 @@ have the type tag, it must still be symmetric or Hermitian.
 See also [`ldlt`](@ref).
 
 !!! note
-    This method uses the CHOLMOD library from [SuiteSparse](https://github.com/DrTimothyAldenDavis/SuiteSparse), 
-    which only supports real or complex types in single or double precision. 
+    This method uses the CHOLMOD library from [SuiteSparse](https://github.com/DrTimothyAldenDavis/SuiteSparse),
+    which only supports real or complex types in single or double precision.
     Input matrices not of those element types will
     be converted to these types as appropriate.
 """
@@ -1695,7 +1703,7 @@ it should be a permutation of `1:size(A,1)` giving the ordering to use
 
 !!! note
     This method uses the CHOLMOD[^ACM887][^DavisHager2009] library from [SuiteSparse](https://github.com/DrTimothyAldenDavis/SuiteSparse).
-    CHOLMOD only supports real or complex types in single or double precision. 
+    CHOLMOD only supports real or complex types in single or double precision.
     Input matrices not of those element types will
     be converted to these types as appropriate.
 
@@ -1767,7 +1775,7 @@ See also [`lowrankupdate!`](@ref), [`lowrankdowndate`](@ref), [`lowrankdowndate!
 """
 lowrankupdate(F::Factor{Tv}, V::AbstractArray{Tv2}) where {Tv, Tv2} =
     lowrankupdate!(
-        change_xdtype(F, promote_type(Tv, Tv2)), 
+        change_xdtype(F, promote_type(Tv, Tv2)),
         convert(AbstractArray{promote_type(Tv, Tv2)}, V)
     )
 
@@ -1782,7 +1790,7 @@ See also [`lowrankdowndate!`](@ref), [`lowrankupdate`](@ref), [`lowrankupdate!`]
 """
 lowrankdowndate(F::Factor{Tv}, V::AbstractArray{Tv2}) where {Tv, Tv2} =
 lowrankdowndate!(
-    change_xdtype(F, promote_type(Tv, Tv2)), 
+    change_xdtype(F, promote_type(Tv, Tv2)),
     convert(AbstractArray{promote_type(Tv, Tv2)}, V)
 )
 
diff --git a/src/solvers/spqr.jl b/src/solvers/spqr.jl
index f7c8189a..1654646e 100644
--- a/src/solvers/spqr.jl
+++ b/src/solvers/spqr.jl
@@ -146,7 +146,7 @@ Matrix{T}(Q::QRSparseQ) where {T} = lmul!(Q, Matrix{T}(I, size(Q, 1), min(size(Q
 
 # From SPQR manual p. 6
 _default_tol(A::AbstractSparseMatrixCSC) =
-    20*sum(size(A))*eps(real(eltype(A)))*maximum(norm(view(A, :, i)) for i in 1:size(A, 2))
+    20*sum(size(A))*eps()*maximum(norm(view(A, :, i)) for i in 1:size(A, 2))
 
 """
     qr(A::SparseMatrixCSC; tol=_default_tol(A), ordering=ORDERING_DEFAULT) -> QRSparse
diff --git a/src/sparsematrix.jl b/src/sparsematrix.jl
index 1559cd86..b9637eda 100644
--- a/src/sparsematrix.jl
+++ b/src/sparsematrix.jl
@@ -49,10 +49,12 @@ SparseMatrixCSC(m, n, colptr::ReadOnly, rowval::ReadOnly, nzval::Vector) =
 
 """
     SparseMatrixCSC{Tv,Ti}(::UndefInitializer, m::Integer, n::Integer)
+    SparseMatrixCSC{Tv,Ti}(::UndefInitializer, (m,n)::NTuple{2,Integer})
 
 Creates an empty sparse matrix with element type `Tv` and integer type `Ti` of size `m × n`.
 """
 SparseMatrixCSC{Tv,Ti}(::UndefInitializer, m::Integer, n::Integer) where {Tv, Ti} = spzeros(Tv, Ti, m, n)
+SparseMatrixCSC{Tv,Ti}(::UndefInitializer, mn::NTuple{2,Integer}) where {Tv, Ti} = spzeros(Tv, Ti, mn...)
 
 """
     FixedSparseCSC{Tv,Ti<:Integer} <: AbstractSparseMatrixCSC{Tv,Ti}
diff --git a/src/sparsevector.jl b/src/sparsevector.jl
index 206a1e06..b80fbe6c 100644
--- a/src/sparsevector.jl
+++ b/src/sparsevector.jl
@@ -50,6 +50,7 @@ SparseVector(n::Integer, nzind::Vector{Ti}, nzval::Vector{Tv}) where {Tv,Ti} =
     SparseVector{Tv,Ti}(n, nzind, nzval)
 
 SparseVector{Tv, Ti}(::UndefInitializer, n::Integer) where {Tv, Ti}  = SparseVector{Tv, Ti}(n, Ti[], Tv[])
+SparseVector{Tv, Ti}(::UndefInitializer, (n,)::Tuple{Integer}) where {Tv, Ti}  = SparseVector{Tv, Ti}(n, Ti[], Tv[])
 
 """
     FixedSparseVector{Tv,Ti<:Integer} <: AbstractCompressedVector{Tv,Ti}
@@ -1276,13 +1277,16 @@ function vcat(X1::_SparseConcatGroup, X::_SparseConcatGroup...)
     end
     return Base.typed_vcat(Base.promote_eltype(X1, X...), X1, X...)
 end
-function hvcat(rows::Tuple{Vararg{Int}}, X1::_SparseConcatGroup, X::_SparseConcatGroup...)
+function hvcat_internal(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(X1, X...), rows, X1, X...)
     end
 end
+function hvcat(rows::Tuple{Vararg{Int}}, X1::_SparseConcatGroup, X::_SparseConcatGroup...)
+    return hvcat_internal(rows, X1, X...)
+end
 function _hvcat_rows((row1, rows...)::Tuple{Vararg{Int}}, X::_SparseConcatGroup...)
     if row1 ≤ 0
         throw(ArgumentError("length of block row must be positive, got $row1"))
@@ -1303,9 +1307,8 @@ hcat(n1::Number, ns::Vararg{Number}) = invoke(hcat, Tuple{Vararg{Number}}, n1, n
 vcat(n1::Number, ns::Vararg{Number}) = invoke(vcat, Tuple{Vararg{Number}}, n1, ns...)
 hcat(n1::N, ns::Vararg{N}) where {N<:Number} = invoke(hcat, Tuple{Vararg{N}}, n1, ns...)
 vcat(n1::N, ns::Vararg{N}) where {N<:Number} = invoke(vcat, Tuple{Vararg{N}}, 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...)
-
+hvcat(rows::Tuple{Vararg{Int}}, n1::Number, ns::Vararg{Number}) = hvcat_internal(rows, n1, ns...)
+hvcat(rows::Tuple{Vararg{Int}}, n1::N, ns::Vararg{N}) where {N<:Number} = hvcat_internal(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
diff --git a/test/cholmod.jl b/test/cholmod.jl
index e025119a..108b0ff8 100644
--- a/test/cholmod.jl
+++ b/test/cholmod.jl
@@ -1,8 +1,12 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 module CHOLMODTests
-
 using Test
+
+@static if !Base.USE_GPL_LIBS
+    @info "This Julia build excludes the use of SuiteSparse GPL libraries. Skipping CHOLMOD tests"
+else
+
 using SparseArrays.CHOLMOD
 using SparseArrays.CHOLMOD: getcommon
 using Random
@@ -16,8 +20,6 @@ using SparseArrays: getcolptr
 using SparseArrays.LibSuiteSparse
 using SparseArrays.LibSuiteSparse: cholmod_l_allocate_sparse, cholmod_allocate_sparse
 
-if Base.USE_GPL_LIBS
-
 # CHOLMOD tests
 itypes = sizeof(Int) == 4 ? (Int32,) : (Int32, Int64)
 for Ti ∈ itypes, Tv ∈ (Float32, Float64)
diff --git a/test/linalg_solvers.jl b/test/linalg_solvers.jl
index f8a758c7..b59659b4 100644
--- a/test/linalg_solvers.jl
+++ b/test/linalg_solvers.jl
@@ -1,14 +1,16 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 module SparseLinalgSolversTests
-
 using Test
+
+@static if !Base.USE_GPL_LIBS
+    @info "This Julia build excludes the use of SuiteSparse GPL libraries. Skipping SparseLinalgSolvers Tests"
+else
+
 using SparseArrays
 using Random
 using LinearAlgebra
 
-if Base.USE_GPL_LIBS
-
 @testset "explicit zeros" begin
     a = SparseMatrixCSC(2, 2, [1, 3, 5], [1, 2, 1, 2], [1.0, 0.0, 0.0, 1.0])
     @test lu(a)\[2.0, 3.0] ≈ [2.0, 3.0]
diff --git a/test/sparsematrix_constructors_indexing.jl b/test/sparsematrix_constructors_indexing.jl
index 5824d887..b1b08dbf 100644
--- a/test/sparsematrix_constructors_indexing.jl
+++ b/test/sparsematrix_constructors_indexing.jl
@@ -57,10 +57,13 @@ end
     @test sparse([1, 1, 2, 2, 2], [1, 2, 1, 2, 2], -1.0, 2, 2, *) == sparse([1, 1, 2, 2], [1, 2, 1, 2], [-1.0, -1.0, -1.0, 1.0], 2, 2)
     @test sparse(sparse(Int32.(1:5), Int32.(1:5), trues(5))') isa SparseMatrixCSC{Bool,Int32}
     # undef initializer
-    m = SparseMatrixCSC{Float32, Int16}(undef, 3, 4)
-    @test size(m) == (3, 4)
-    @test eltype(m) === Float32
-    @test m == spzeros(3, 4)
+    sz = (3, 4)
+    for m in (SparseMatrixCSC{Float32, Int16}(undef, sz...), SparseMatrixCSC{Float32, Int16}(undef, sz),
+                 similar(SparseMatrixCSC{Float32, Int16}, sz))
+        @test size(m) == sz
+        @test eltype(m) === Float32
+        @test m == spzeros(sz...)
+    end
 end
 
 @testset "spzeros for pattern creation (structural zeros)" begin
diff --git a/test/sparsevector.jl b/test/sparsevector.jl
index 513028e5..15499e12 100644
--- a/test/sparsevector.jl
+++ b/test/sparsevector.jl
@@ -219,10 +219,14 @@ end
     end
 
     @testset "Undef initializer" begin
-        v = SparseVector{Float32, Int16}(undef, 4)
-        @test size(v) == (4, )
-        @test eltype(v) === Float32
-        @test v == spzeros(Float32, 4)
+        sz = (4,)
+        for v in (SparseVector{Float32, Int16}(undef, sz),
+                    SparseVector{Float32, Int16}(undef, sz...),
+                    similar(SparseVector{Float32, Int16}, sz))
+            @test size(v) == sz
+            @test eltype(v) === Float32
+            @test v == spzeros(Float32, sz...)
+        end
     end
 end
 ### Element access
diff --git a/test/spqr.jl b/test/spqr.jl
index 16a72eef..b3d895c3 100644
--- a/test/spqr.jl
+++ b/test/spqr.jl
@@ -1,16 +1,19 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 module SPQRTests
-
 using Test
+
+@static if !Base.USE_GPL_LIBS
+    @info "This Julia build excludes the use of SuiteSparse GPL libraries. Skipping SPQR Tests"
+else
+
 using SparseArrays.SPQR
 using SparseArrays.CHOLMOD
 using LinearAlgebra: I, istriu, norm, qr, rank, rmul!, lmul!, Adjoint, Transpose, ColumnNorm, RowMaximum, NoPivot
 using SparseArrays: SparseArrays, sparse, sprandn, spzeros, SparseMatrixCSC
 using Random: seed!
 
-# TODO REMOVE SECOND PREDICATE WITH SS7.1
-if Base.USE_GPL_LIBS
+
 @testset "Sparse QR" begin
 m, n = 100, 10
 nn = 100
diff --git a/test/umfpack.jl b/test/umfpack.jl
index ba65e1b2..b4bdd588 100644
--- a/test/umfpack.jl
+++ b/test/umfpack.jl
@@ -1,15 +1,19 @@
 # This file is a part of Julia. License is MIT: https://julialang.org/license
 
 module UMFPACKTests
-
 using Test
+
+@static if !Base.USE_GPL_LIBS
+    @info "This Julia build excludes the use of SuiteSparse GPL libraries. Skipping UMFPACK Tests"
+else
+
 using Random
 using SparseArrays
 using Serialization
 using LinearAlgebra:
     LinearAlgebra, I, det, issuccess, ldiv!, lu, lu!, Transpose, SingularException, Diagonal, logabsdet
 using SparseArrays: nnz, sparse, sprand, sprandn, SparseMatrixCSC, UMFPACK, increment!
-if Base.USE_GPL_LIBS
+
 function umfpack_report(l::UMFPACK.UmfpackLU)
     UMFPACK.umfpack_report_numeric(l, 0)
     UMFPACK.umfpack_report_symbolic(l, 0)