Update and fix the docs

docs/Project.toml

@@ -4,5 +4,5 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Quaternions = "94ee1d12-ae83-5a48-8b1c-48b8ff168ae0"
 
 [compat]
-Documenter = "0.26"
-Quaternions = "0.4"
+Documenter = "1"
+Quaternions = "0.7"

docs/src/index.md

@@ -7,6 +7,9 @@ CurrentModule = GenericLinearAlgebra
 ```
 
 ```@docs
+ldlt
+ldlt!
+numnegevals
 svd!
 svdvals!
 ```

src/ldlt.jl

@@ -147,7 +147,7 @@ end
 
 See [`ldlt`](@ref)
 """
-function LinearAlgebra.ldlt!(A::Hermitian{T}, blocksize::Int = 32 ÷ sizeof(T)) where T
+function LinearAlgebra.ldlt!(A::Hermitian{T}, blocksize::Int = max(1, 128 ÷ sizeof(T))) where T
     if A.uplo === 'U'
         _ldlt_upper_blocked!(A.data, blocksize)
     else
@@ -157,7 +157,7 @@ function LinearAlgebra.ldlt!(A::Hermitian{T}, blocksize::Int = 32 ÷ sizeof(T))
 end
 
 """
-    ldlt(A::Hermitian)::LTLt
+    ldlt(A::Hermitian, blocksize::Int)::LTLt
 
 A Hermitian LDL factorization of `A` such that `A = L*D*L'` if `A.uplo == 'L'`
 and `A = U'*D*U` if `A.uplo == 'U'. Hence, the `t` is a bit of a misnomer,
@@ -171,35 +171,37 @@ The factorization has three properties: `d`, `D`, and `L` which is respectively
 a vector of the diagonal elements of `D`, the `Diagonal` matrix `D` and the `L`
 matrix when `A.uplo == 'L'` or the `adjoint` of the `U` matrix when `A.uplo == 'U'`.
 
-The factorization is blocked. Currently, the blocking size is set to `32 ÷ sizeof(eltype(A))`
-based on very rudimentary benchmarking on my laptop.
+The `blocksize` argument controls the block size in the blocked algorithm.
+Currently, the blocking size is set to `128 ÷ sizeof(eltype(A))`
+based on very rudimentary benchmarking on my laptop. Most users won't need
+adjust this argument.
 
 # Examples
 ```jldoctest
-julia> ldlt(Hermitian([1 1; 1 -1]))
-LDLt{Int64, Hermitian{Int64, Matrix{Int64}}}
+julia> ldlt(Hermitian([1//1 1; 1 -1]))
+LDLt{Rational{Int64}, Hermitian{Rational{Int64}, Matrix{Rational{Int64}}}}
 L factor:
-2×2 UnitLowerTriangular{Int64, Adjoint{Int64, Matrix{Int64}}}:
+2×2 UnitLowerTriangular{Rational{Int64}, Adjoint{Rational{Int64}, Matrix{Rational{Int64}}}}:
  1  ⋅
  1  1
 D factor:
-2×2 Diagonal{Int64, SubArray{Int64, 1, Base.ReshapedArray{Int64, 1, Hermitian{Int64, Matrix{Int64}}, Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}}, Tuple{StepRange{Int64, Int64}}, false}}:
+2×2 Diagonal{Rational{Int64}, SubArray{Rational{Int64}, 1, Base.ReshapedArray{Rational{Int64}, 1, Hermitian{Rational{Int64}, Matrix{Rational{Int64}}}, Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}}, Tuple{StepRange{Int64, Int64}}, false}}:
  1   ⋅
  ⋅  -2
 
-julia> ldlt(Hermitian([1 1; 1 1], :L))
-LDLt{Int64, Hermitian{Int64, Matrix{Int64}}}
+julia> ldlt(Hermitian([1//1 1; 1 1], :L))
+LDLt{Rational{Int64}, Hermitian{Rational{Int64}, Matrix{Rational{Int64}}}}
 L factor:
-2×2 UnitLowerTriangular{Int64, Matrix{Int64}}:
+2×2 UnitLowerTriangular{Rational{Int64}, Matrix{Rational{Int64}}}:
  1  ⋅
  1  1
 D factor:
-2×2 Diagonal{Int64, SubArray{Int64, 1, Base.ReshapedArray{Int64, 1, Hermitian{Int64, Matrix{Int64}}, Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}}, Tuple{StepRange{Int64, Int64}}, false}}:
+2×2 Diagonal{Rational{Int64}, SubArray{Rational{Int64}, 1, Base.ReshapedArray{Rational{Int64}, 1, Hermitian{Rational{Int64}, Matrix{Rational{Int64}}}, Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}}, Tuple{StepRange{Int64, Int64}}, false}}:
  1  ⋅
  ⋅  0
 ```
 """
-LinearAlgebra.ldlt(A::Hermitian{T}, blocksize::Int = 32 ÷ sizeof(T)) where T = ldlt!(LinearAlgebra.copy_oftype(A, typeof(oneunit(T) / one(T))))
+LinearAlgebra.ldlt(A::Hermitian{T}, blocksize::Int = max(1, 128 ÷ sizeof(T))) where T = ldlt!(LinearAlgebra.copy_oftype(A, typeof(oneunit(T) / one(T))))
 
 function Base.getproperty(F::LDLt{<:Any, <:Hermitian}, d::Symbol)
     Fdata = getfield(F, :data)

src/svd.jl

@@ -567,19 +567,21 @@ LinearAlgebra.svdvals!(B::Bidiagonal{T}; tol = eps(T)) where {T<:Real} =
     svdvals!(A [, tol])
 
 Generic computation of singular values.
+
+# Examples
 ```jldoctest
 julia> using LinearAlgebra, GenericLinearAlgebra, Quaternions
 
 julia> n = 20;
 
 julia> H = [big(1)/(i + j - 1) for i in 1:n, j in 1:n]; # The Hilbert matrix
 
-julia> Float64(svdvals(H)[end]/svdvals(Float64.(H))[end] - 1) # The relative error of the LAPACK based solution in 64 bit floating point.
--0.9999999999447275
+julia> round(svdvals(H)[end]/svdvals(Float64.(H))[end] - 1, sigdigits=8) # The relative error of the LAPACK based solution rounded to eight significant digits.
+-1.0
 
 julia> A = qr([Quaternion(randn(4)...) for i in 1:3, j in 1:3]).Q *
            Diagonal([3, 2, 1]) *
-           qr([Quaternion(randn(4)...) for i in 1:3, j in 1:3]).Q'; # A quaternion matrix with the singular value 1, 2, and 3.
+           qr([Quaternion(randn(4)...) for i in 1:3, j in 1:3]).Q';
 
 julia> svdvals(A) ≈ [3, 2, 1]
 true
@@ -617,7 +619,7 @@ A generic singular value decomposition (SVD). The implementation only uses Julia
 
 ```jldoctest
 julia> svd(big.([1 2; 3 4]))
-SVD{BigFloat, BigFloat, Matrix{BigFloat}}
+SVD{BigFloat, BigFloat, Matrix{BigFloat}, Vector{BigFloat}}
 U factor:
 2×2 Matrix{BigFloat}:
  -0.404554   0.914514