add support for BigFloats via a new extension

Project.toml

@@ -10,18 +10,24 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+GenericLinearAlgebra = "14197337-ba66-59df-a3e3-ca00e7dcff7a"
+GenericSchur = "c145ed77-6b09-5dd9-b285-bf645a82121e"
 
 [extensions]
 MatrixAlgebraKitChainRulesCoreExt = "ChainRulesCore"
 MatrixAlgebraKitAMDGPUExt = "AMDGPU"
 MatrixAlgebraKitCUDAExt = "CUDA"
+MatrixAlgebraKitGenericLinearAlgebraExt = "GenericLinearAlgebra"
+MatrixAlgebraKitGenericSchurExt = "GenericSchur"
 
 [compat]
 AMDGPU = "2"
 Aqua = "0.6, 0.7, 0.8"
 ChainRulesCore = "1"
 ChainRulesTestUtils = "1"
 CUDA = "5"
+GenericLinearAlgebra = "0.3.19"
+GenericSchur = "0.5.6"
 JET = "0.9, 0.10"
 LinearAlgebra = "1"
 SafeTestsets = "0.1"
@@ -42,4 +48,4 @@ TestExtras = "5ed8adda-3752-4e41-b88a-e8b09835ee3a"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Aqua", "JET", "SafeTestsets", "Test", "TestExtras","ChainRulesCore", "ChainRulesTestUtils", "StableRNGs", "Zygote", "CUDA", "AMDGPU"]
+test = ["Aqua", "JET", "SafeTestsets", "Test", "TestExtras", "ChainRulesCore", "ChainRulesTestUtils", "StableRNGs", "Zygote", "CUDA", "AMDGPU", "GenericLinearAlgebra", "GenericSchur"]

ext/MatrixAlgebraKitGenericLinearAlgebraExt.jl

@@ -0,0 +1,108 @@
+module MatrixAlgebraKitGenericLinearAlgebraExt
+
+using MatrixAlgebraKit
+using MatrixAlgebraKit: sign_safe, check_input, diagview
+using GenericLinearAlgebra: svd!, svdvals!, eigen!, eigvals!, Hermitian, qr!
+using LinearAlgebra: I, Diagonal, rmul!, lmul!, transpose!, dot
+
+function MatrixAlgebraKit.default_svd_algorithm(::Type{T}; kwargs...) where {T <: StridedMatrix{<:Union{BigFloat, Complex{BigFloat}}}}
+    return GLA_QRIteration()
+end
+
+for f! in (:svd_compact!, :svd_full!, :svd_vals!)
+    @eval MatrixAlgebraKit.initialize_output(::typeof($f!), A::AbstractMatrix, ::GLA_QRIteration) = nothing
+end
+
+function MatrixAlgebraKit.svd_compact!(A::AbstractMatrix, USVᴴ, ::GLA_QRIteration)
+    F = svd!(A)
+    U, S, Vᴴ = F.U, Diagonal(F.S), F.Vt
+    return MatrixAlgebraKit.gaugefix!(svd_compact!, U, S, Vᴴ, size(A)...)
+end
+
+function MatrixAlgebraKit.svd_full!(A::AbstractMatrix, USVᴴ, ::GLA_QRIteration)
+    F = svd!(A; full = true)
+    U, Vᴴ = F.U, F.Vt
+    S = MatrixAlgebraKit.zero!(similar(F.S, (size(U, 2), size(Vᴴ, 1))))
+    diagview(S) .= F.S
+    return MatrixAlgebraKit.gaugefix!(svd_full!, U, S, Vᴴ, size(A)...)
+end
+
+function MatrixAlgebraKit.svd_vals!(A::AbstractMatrix, S, ::GLA_QRIteration)
+    return svdvals!(A)
+end
+
+function MatrixAlgebraKit.default_eigh_algorithm(::Type{T}; kwargs...) where {T <: StridedMatrix{<:Union{BigFloat, Complex{BigFloat}}}}
+    return GLA_QRIteration(; kwargs...)
+end
+
+for f! in (:eigh_full!, :eigh_vals!)
+    @eval MatrixAlgebraKit.initialize_output(::typeof($f!), A::AbstractMatrix, ::GLA_QRIteration) = nothing
+end
+
+function MatrixAlgebraKit.eigh_full!(A::AbstractMatrix, DV, ::GLA_QRIteration)
+    eigval, eigvec = eigen!(Hermitian(A); sortby = real)
+    return Diagonal(eigval::AbstractVector{real(eltype(A))}), eigvec::AbstractMatrix{eltype(A)}
+end
+
+function MatrixAlgebraKit.eigh_vals!(A::AbstractMatrix, D, ::GLA_QRIteration)
+    return eigvals!(Hermitian(A); sortby = real)
+end
+
+function MatrixAlgebraKit.default_qr_algorithm(::Type{T}; kwargs...) where {T <: StridedMatrix{<:Union{BigFloat, Complex{BigFloat}}}}
+    return GLA_HouseholderQR(; kwargs...)
+end
+
+function MatrixAlgebraKit.qr_full!(A::AbstractMatrix, QR, alg::GLA_HouseholderQR)
+    check_input(qr_full!, A, QR, alg)
+    Q, R = QR
+    return _gla_householder_qr!(A, Q, R; alg.kwargs...)
+end
+
+function MatrixAlgebraKit.qr_compact!(A::AbstractMatrix, QR, alg::GLA_HouseholderQR)
+    check_input(qr_compact!, A, QR, alg)
+    Q, R = QR
+    return _gla_householder_qr!(A, Q, R; alg.kwargs...)
+end
+
+function _gla_householder_qr!(A::AbstractMatrix, Q, R; positive = false, blocksize = 1, pivoted = false)
+    pivoted && throw(ArgumentError("Only pivoted = false implemented for GLA_HouseholderQR."))
+    (blocksize == 1) || throw(ArgumentError("Only blocksize = 1 implemented for GLA_HouseholderQR."))
+
+    m, n = size(A)
+    k = min(m, n)
+    computeR = length(R) > 0
+    compact = k < m
+    Q̃, R̃ = qr!(A)
+
+    Q = compact ? copyto!(Q, Q̃) : rmul!(MatrixAlgebraKit.one!(Q), Q̃)
+    if positive
+        @inbounds for j in 1:k
+            s = sign_safe(R̃[j, j])
+            @simd for i in 1:m
+                Q[i, j] *= s
+            end
+        end
+    end
+    if computeR
+        if positive
+            @inbounds for j in n:-1:1
+                @simd for i in 1:min(k, j)
+                    R[i, j] = R̃[i, j] * conj(sign_safe(R̃[i, i]))
+                end
+                @simd for i in (min(k, j) + 1):size(R, 1)
+                    R[i, j] = zero(eltype(R))
+                end
+            end
+        else
+            R[1:k, :] .= R̃
+            MatrixAlgebraKit.zero!(@view(R[(k + 1):end, :]))
+        end
+    end
+    return Q, R
+end
+
+function MatrixAlgebraKit.default_lq_algorithm(::Type{T}; kwargs...) where {T <: StridedMatrix{<:Union{BigFloat, Complex{BigFloat}}}}
+    return MatrixAlgebraKit.LQViaTransposedQR(GLA_HouseholderQR(; kwargs...))
+end
+
+end

ext/MatrixAlgebraKitGenericSchurExt.jl

@@ -0,0 +1,25 @@
+module MatrixAlgebraKitGenericSchurExt
+
+using MatrixAlgebraKit
+using MatrixAlgebraKit: check_input
+using LinearAlgebra: Diagonal
+using GenericSchur
+
+function MatrixAlgebraKit.default_eig_algorithm(::Type{T}; kwargs...) where {T <: StridedMatrix{<:Union{BigFloat, Complex{BigFloat}}}}
+    return GS_QRIteration(; kwargs...)
+end
+
+for f! in (:eig_full!, :eig_vals!)
+    @eval MatrixAlgebraKit.initialize_output(::typeof($f!), A::AbstractMatrix, ::GS_QRIteration) = nothing
+end
+
+function MatrixAlgebraKit.eig_full!(A::AbstractMatrix, DV, ::GS_QRIteration)
+    D, V = GenericSchur.eigen!(A)
+    return Diagonal(D), V
+end
+
+function MatrixAlgebraKit.eig_vals!(A::AbstractMatrix, D, ::GS_QRIteration)
+    return GenericSchur.eigvals!(A)
+end
+
+end

src/MatrixAlgebraKit.jl

@@ -33,6 +33,7 @@ export left_orth!, right_orth!, left_null!, right_null!
 export LAPACK_HouseholderQR, LAPACK_HouseholderLQ, LAPACK_Simple, LAPACK_Expert,
     LAPACK_QRIteration, LAPACK_Bisection, LAPACK_MultipleRelativelyRobustRepresentations,
     LAPACK_DivideAndConquer, LAPACK_Jacobi
+export GLA_HouseholderQR, GLA_QRIteration, GS_QRIteration
 export LQViaTransposedQR
 export PolarViaSVD, PolarNewton
 export DiagonalAlgorithm

src/interface/decompositions.jl

@@ -32,6 +32,16 @@ elements of `L` are non-negative.
 """
 @algdef LAPACK_HouseholderLQ
 
+"""
+    GLA_HouseholderQR(; positive = false)
+
+Algorithm type to denote the GenericLinearAlgebra.jl implementation for computing the QR decomposition 
+of a matrix using Householder reflectors. Currenlty, only `blocksize = 1` and `pivoted == false` 
+are supported. The keyword `positive=true` can be used to ensure that the diagonal elements 
+of `R` are non-negative.
+"""
+@algdef GLA_HouseholderQR
+
 # TODO:
 @algdef LAPACK_HouseholderQL
 @algdef LAPACK_HouseholderRQ
@@ -56,6 +66,14 @@ eigenvalue decomposition of a matrix.
 
 const LAPACK_EigAlgorithm = Union{LAPACK_Simple, LAPACK_Expert}
 
+"""
+    GS_QRIteration()
+
+Algorithm type to denote the GenericSchur.jl implementation for computing the 
+eigenvalue decomposition of a non-Hermitian matrix.
+"""
+@algdef GS_QRIteration
+
 # Hermitian Eigenvalue Decomposition
 # ----------------------------------
 """
@@ -100,6 +118,15 @@ const LAPACK_EighAlgorithm = Union{
     LAPACK_MultipleRelativelyRobustRepresentations,
 }
 
+"""
+    GLA_QRIteration()
+
+Algorithm type to denote the GenericLinearAlgebra.jl implementation for computing the 
+eigenvalue decomposition of a Hermitian matrix, or the singular value decomposition of 
+a general matrix.
+"""
+@algdef GLA_QRIteration
+
 # Singular Value Decomposition
 # ----------------------------
 """

test/genericlinearalgebra/eigh.jl

@@ -0,0 +1,118 @@
+using MatrixAlgebraKit
+using Test
+using TestExtras
+using StableRNGs
+using LinearAlgebra: LinearAlgebra, Diagonal, I
+using MatrixAlgebraKit: TruncatedAlgorithm, diagview, norm
+using GenericLinearAlgebra
+
+const eltypes = (BigFloat, Complex{BigFloat})
+
+@testset "eigh_full! for T = $T" for T in eltypes
+    rng = StableRNG(123)
+    m = 54
+    alg = GLA_QRIteration()
+
+    A = randn(rng, T, m, m)
+    A = (A + A') / 2
+
+    D, V = @constinferred eigh_full(A; alg)
+    @test A * V ≈ V * D
+    @test isunitary(V)
+    @test all(isreal, D)
+
+    D2, V2 = eigh_full!(copy(A), (D, V), alg)
+    @test D2 ≈ D
+    @test V2 ≈ V
+
+    D3 = @constinferred eigh_vals(A, alg)
+    @test D ≈ Diagonal(D3)
+end
+
+@testset "eigh_trunc! for T = $T" for T in eltypes
+    rng = StableRNG(123)
+    m = 54
+    alg = GLA_QRIteration()
+    A = randn(rng, T, m, m)
+    A = A * A'
+    A = (A + A') / 2
+    Ac = similar(A)
+    D₀ = reverse(eigh_vals(A))
+
+    r = m - 2
+    s = 1 + sqrt(eps(real(T)))
+    atol = sqrt(eps(real(T)))
+
+    D1, V1, ϵ1 = @constinferred eigh_trunc(A; alg, trunc = truncrank(r))
+    Dfull, Vfull = eigh_full(A; alg)
+    @test length(diagview(D1)) == r
+    @test isisometric(V1)
+    @test A * V1 ≈ V1 * D1
+    @test LinearAlgebra.opnorm(A - V1 * D1 * V1') ≈ D₀[r + 1]
+    @test ϵ1 ≈ norm(view(D₀, (r + 1):m)) atol = atol
+
+    trunc = trunctol(; atol = s * D₀[r + 1])
+    D2, V2, ϵ2 = @constinferred eigh_trunc(A; alg, trunc)
+    @test length(diagview(D2)) == r
+    @test isisometric(V2)
+    @test A * V2 ≈ V2 * D2
+    @test ϵ2 ≈ norm(view(D₀, (r + 1):m)) atol = atol
+
+    s = 1 - sqrt(eps(real(T)))
+    trunc = truncerror(; atol = s * norm(@view(D₀[r:end]), 1), p = 1)
+    D3, V3, ϵ3 = @constinferred eigh_trunc(A; alg, trunc)
+    @test length(diagview(D3)) == r
+    @test A * V3 ≈ V3 * D3
+    @test ϵ3 ≈ norm(view(D₀, (r + 1):m)) atol = atol
+
+    # test for same subspace
+    @test V1 * (V1' * V2) ≈ V2
+    @test V2 * (V2' * V1) ≈ V1
+    @test V1 * (V1' * V3) ≈ V3
+    @test V3 * (V3' * V1) ≈ V1
+end
+
+@testset "eigh_trunc! specify truncation algorithm T = $T" for T in eltypes
+    rng = StableRNG(123)
+    m = 4
+    atol = sqrt(eps(real(T)))
+    V = qr_compact(randn(rng, T, m, m))[1]
+    D = Diagonal(real(T)[0.9, 0.3, 0.1, 0.01])
+    A = V * D * V'
+    A = (A + A') / 2
+    alg = TruncatedAlgorithm(GLA_QRIteration(), truncrank(2))
+    D2, V2, ϵ2 = @constinferred eigh_trunc(A; alg)
+    @test diagview(D2) ≈ diagview(D)[1:2]
+    @test_throws ArgumentError eigh_trunc(A; alg, trunc = (; maxrank = 2))
+    @test ϵ2 ≈ norm(diagview(D)[3:4]) atol = atol
+
+    alg = TruncatedAlgorithm(GLA_QRIteration(), truncerror(; atol = 0.2))
+    D3, V3, ϵ3 = @constinferred eigh_trunc(A; alg)
+    @test diagview(D3) ≈ diagview(D)[1:2]
+    @test ϵ3 ≈ norm(diagview(D)[3:4]) atol = atol
+end
+
+@testset "eigh for Diagonal{$T}" for T in eltypes
+    rng = StableRNG(123)
+    m = 54
+    Ad = randn(rng, T, m)
+    Ad .+= conj.(Ad)
+    A = Diagonal(Ad)
+    atol = sqrt(eps(real(T)))
+
+    D, V = @constinferred eigh_full(A)
+    @test D isa Diagonal{real(T)} && size(D) == size(A)
+    @test V isa Diagonal{T} && size(V) == size(A)
+    @test A * V ≈ V * D
+
+    D2 = @constinferred eigh_vals(A)
+    @test D2 isa AbstractVector{real(T)} && length(D2) == m
+    @test diagview(D) ≈ D2
+
+    A2 = Diagonal(T[0.9, 0.3, 0.1, 0.01])
+    alg = TruncatedAlgorithm(DiagonalAlgorithm(), truncrank(2))
+    D2, V2, ϵ2 = @constinferred eigh_trunc(A2; alg)
+    @test diagview(D2) ≈ diagview(A2)[1:2]
+    @test ϵ2 ≈ norm(diagview(A2)[3:4]) atol = atol
+
+end