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Add reusable testsuite for sector-dependent tests #473

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7d352d2
implement testsuite
borisdevos Jul 6, 2026
d9cc856
apply testsuite to current tests
borisdevos Jul 6, 2026
f469892
use TKS's testsuite for sector utility
borisdevos Jul 6, 2026
bad9878
homogenise eval_show
borisdevos Jul 6, 2026
5f5e313
Merge branch 'main' of https://github.com/Jutho/TensorKit.jl into bd/…
borisdevos Jul 6, 2026
91127f9
avoid testsuite being recognised as tests
borisdevos Jul 7, 2026
cbaf351
fix x ambiguity
borisdevos Jul 7, 2026
10f8a4e
move testsuite file
borisdevos Jul 7, 2026
eff5ab6
make separate entry points for single and double fusion tree tests
borisdevos Jul 7, 2026
77c2061
split up tensor tests
borisdevos Jul 8, 2026
e1d0f33
make diagonal tensor tests also an entry point
borisdevos Jul 8, 2026
cb8d067
convert braiding tensor properties testset to testsuite
borisdevos Jul 8, 2026
df01132
have single access function to run testsuites + changes related to mo…
borisdevos Jul 8, 2026
a7e4b4a
add factorizations to the testsuite
borisdevos Jul 8, 2026
622c960
Merge branches 'bd/testsuite' and 'main' of https://github.com/Quantu…
borisdevos Jul 8, 2026
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89 changes: 1 addition & 88 deletions test/factorizations/eig.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -3,102 +3,15 @@ using TensorKit
using LinearAlgebra: LinearAlgebra
using MatrixAlgebraKit: DefaultAlgorithm, diagview


spacelist = factorization_spacelist(fast_tests)

eltypes = (Float32, ComplexF64)

for V in spacelist
I = sectortype(first(V))
Istr = TensorKit.type_repr(I)
println("---------------------------------------")
println("Eigenvalue decompositions with symmetry: $Istr")
println("---------------------------------------")
@timedtestset "Eigenvalue decompositions with symmetry: $Istr" verbose = true begin
V1, V2, V3, V4, V5 = V
W = V1 ⊗ V2 ⊗ V3
Vd = fuse(V1 ⊗ V2)

@testset "Eigenvalue decomposition" begin
for T in eltypes,
t in (
rand(T, V1, V1), rand(T, W, W), rand(T, W, W)',
DiagonalTensorMap(rand(T, reduceddim(Vd)), Vd),
)

d, v = @constinferred eig_full(t)
@test t * v ≈ v * d

d, v = @constinferred eig_full(t, DefaultAlgorithm())
@test t * v ≈ v * d

d′ = @constinferred eig_vals(t)
@test d′ ≈ diagview(d)
@test d′ isa TensorKit.SectorVector

d′ = @constinferred eig_vals(t, DefaultAlgorithm())
@test d′ ≈ diagview(d)
@test d′ isa TensorKit.SectorVector

d2 = @constinferred DiagonalTensorMap(d′)
@test d2 ≈ d

vdv = project_hermitian!(v' * v)
@test @constinferred isposdef(vdv)
t isa DiagonalTensorMap || @test !isposdef(t) # unlikely for non-hermitian map

nvals = round(Int, dim(domain(t)) / 2)
d, v = @constinferred eig_trunc(t; trunc = truncrank(nvals))
@test t * v ≈ v * d
@test abs(dim(domain(d)) - nvals) ≤ maximum(c -> blockdim(domain(t), c), blocksectors(t); init = 1)

d, v = @constinferred eig_trunc(t, DefaultAlgorithm(; trunc = truncrank(nvals)))
@test t * v ≈ v * d
@test abs(dim(domain(d)) - nvals) ≤ maximum(c -> blockdim(domain(t), c), blocksectors(t); init = 1)

t2 = @constinferred project_hermitian(t)
D, V = eigen(t2)
@test isisometric(V)
D̃, Ṽ = @constinferred eigh_full(t2)
@test D ≈ D̃
@test V ≈ Ṽ

D̃, Ṽ = @constinferred eigh_full(t2, DefaultAlgorithm())
@test D ≈ D̃
@test V ≈ Ṽ

λ = minimum(real, diagview(D))
@test cond(Ṽ) ≈ one(real(T))
@test isposdef(t2) == isposdef(λ)
@test isposdef(t2 - λ * one(t2) + 0.1 * one(t2))
@test !isposdef(t2 - λ * one(t2) - 0.1 * one(t2))

d, v = @constinferred eigh_full(t2)
@test t2 * v ≈ v * d
@test isunitary(v)

d′ = @constinferred eigh_vals(t2)
@test d′ ≈ diagview(d)
@test d′ isa TensorKit.SectorVector

d′ = @constinferred eigh_vals(t2, DefaultAlgorithm())
@test d′ ≈ diagview(d)
@test d′ isa TensorKit.SectorVector

λ = minimum(real, diagview(d))
@test cond(v) ≈ one(real(T))
@test isposdef(t2) == isposdef(λ)
@test isposdef(t2 - λ * one(t) + 0.1 * one(t2))
@test !isposdef(t2 - λ * one(t) - 0.1 * one(t2))

d, v = @constinferred eigh_trunc(t2; trunc = truncrank(nvals))
@test t2 * v ≈ v * d
@test abs(dim(domain(d)) - nvals) ≤ maximum(c -> blockdim(domain(t), c), blocksectors(t); init = 1)

d, v = @constinferred eigh_trunc(t2, DefaultAlgorithm(; trunc = truncrank(nvals)))
@test t2 * v ≈ v * d
@test abs(dim(domain(d)) - nvals) ≤ maximum(c -> blockdim(domain(t), c), blocksectors(t); init = 1)
end
end
TensorKitTestSuite.run_testsuite(:factorizations, "eigenvalue decomposition", V)
end
end
188 changes: 2 additions & 186 deletions test/factorizations/ortho.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -3,200 +3,16 @@ using TensorKit
using LinearAlgebra: LinearAlgebra
using MatrixAlgebraKit: DefaultAlgorithm, diagview


spacelist = factorization_spacelist(fast_tests)

eltypes = (Float32, ComplexF64)

for V in spacelist
I = sectortype(first(V))
Istr = TensorKit.type_repr(I)
println("---------------------------------------")
println("QR and LQ decompositions with symmetry: $Istr")
println("---------------------------------------")
@timedtestset "QR and LQ decompositions with symmetry: $Istr" verbose = true begin
V1, V2, V3, V4, V5 = V
W = V1 ⊗ V2 ⊗ V3
Vd = fuse(V1 ⊗ V2)

@testset "QR decomposition" begin
for T in eltypes,
t in (
rand(T, W, W), rand(T, W, W)',
rand(T, (V1 ⊗ V2 ⊗ V3), (V4 ⊗ V5)'), rand(T, (V1 ⊗ V2 ⊗ V3), (V4 ⊗ V5)')',
rand(T, (V1 ⊗ V2)', (V3 ⊗ V4 ⊗ V5)), rand(T, (V1 ⊗ V2)', (V3 ⊗ V4 ⊗ V5))',
DiagonalTensorMap(rand(T, reduceddim(Vd)), Vd),
)

Q, R = @constinferred qr_full(t)
@test Q * R ≈ t
@test isunitary(Q)

Q, R = @constinferred qr_full(t, DefaultAlgorithm())
@test Q * R ≈ t
@test isunitary(Q)

Q, R = @constinferred qr_compact(t)
@test Q * R ≈ t
@test isisometric(Q)

Q, R = @constinferred qr_compact(t, DefaultAlgorithm())
@test Q * R ≈ t
@test isisometric(Q)

Q, R = @constinferred left_orth(t)
@test Q * R ≈ t
@test isisometric(Q)

Q, R = @constinferred left_orth(t, DefaultAlgorithm())
@test Q * R ≈ t
@test isisometric(Q)

N = @constinferred qr_null(t)
@test isisometric(N)
@test norm(N' * t) ≈ 0 atol = 100 * eps(norm(t))

N = @constinferred qr_null(t, DefaultAlgorithm())
@test isisometric(N)
@test norm(N' * t) ≈ 0 atol = 100 * eps(norm(t))

N = @constinferred left_null(t)
@test isisometric(N)
@test norm(N' * t) ≈ 0 atol = 100 * eps(norm(t))

N = @constinferred left_null(t, DefaultAlgorithm())
@test isisometric(N)
@test norm(N' * t) ≈ 0 atol = 100 * eps(norm(t))
end

# empty tensor
for T in eltypes
t = rand(T, V1 ⊗ V2, zerospace(V1))

Q, R = @constinferred qr_full(t)
@test Q * R ≈ t
@test isunitary(Q)
@test dim(R) == dim(t) == 0

Q, R = @constinferred qr_full(t, DefaultAlgorithm())
@test Q * R ≈ t
@test isunitary(Q)
@test dim(R) == dim(t) == 0

Q, R = @constinferred qr_compact(t)
@test Q * R ≈ t
@test isisometric(Q)
@test dim(Q) == dim(R) == dim(t)

Q, R = @constinferred qr_compact(t, DefaultAlgorithm())
@test Q * R ≈ t
@test isisometric(Q)
@test dim(Q) == dim(R) == dim(t)

Q, R = @constinferred left_orth(t)
@test Q * R ≈ t
@test isisometric(Q)
@test dim(Q) == dim(R) == dim(t)

Q, R = @constinferred left_orth(t, DefaultAlgorithm())
@test Q * R ≈ t
@test isisometric(Q)
@test dim(Q) == dim(R) == dim(t)

N = @constinferred qr_null(t)
@test isunitary(N)
@test norm(N' * t) ≈ 0 atol = 100 * eps(norm(t))

N = @constinferred qr_null(t, DefaultAlgorithm())
@test isunitary(N)
@test norm(N' * t) ≈ 0 atol = 100 * eps(norm(t))
end
end

@testset "LQ decomposition" begin
for T in eltypes,
t in (
rand(T, W, W), rand(T, W, W)',
rand(T, (V1 ⊗ V2), (V3 ⊗ V4 ⊗ V5)'), rand(T, (V1 ⊗ V2), (V3 ⊗ V4 ⊗ V5)')',
rand(T, (V1 ⊗ V2 ⊗ V3)', (V4 ⊗ V5)), rand(T, (V1 ⊗ V2 ⊗ V3)', (V4 ⊗ V5))',
DiagonalTensorMap(rand(T, reduceddim(Vd)), Vd),
)

L, Q = @constinferred lq_full(t)
@test L * Q ≈ t
@test isunitary(Q)

L, Q = @constinferred lq_full(t, DefaultAlgorithm())
@test L * Q ≈ t
@test isunitary(Q)

L, Q = @constinferred lq_compact(t)
@test L * Q ≈ t
@test isisometric(Q; side = :right)

L, Q = @constinferred lq_compact(t, DefaultAlgorithm())
@test L * Q ≈ t
@test isisometric(Q; side = :right)

L, Q = @constinferred right_orth(t)
@test L * Q ≈ t
@test isisometric(Q; side = :right)

L, Q = @constinferred right_orth(t, DefaultAlgorithm())
@test L * Q ≈ t
@test isisometric(Q; side = :right)

Nᴴ = @constinferred lq_null(t)
@test isisometric(Nᴴ; side = :right)
@test norm(t * Nᴴ') ≈ 0 atol = 100 * eps(norm(t))

Nᴴ = @constinferred lq_null(t, DefaultAlgorithm())
@test isisometric(Nᴴ; side = :right)
@test norm(t * Nᴴ') ≈ 0 atol = 100 * eps(norm(t))
end

for T in eltypes
# empty tensor
t = rand(T, zerospace(V1), V1 ⊗ V2)

L, Q = @constinferred lq_full(t)
@test L * Q ≈ t
@test isunitary(Q)
@test dim(L) == dim(t) == 0

L, Q = @constinferred lq_full(t, DefaultAlgorithm())
@test L * Q ≈ t
@test isunitary(Q)
@test dim(L) == dim(t) == 0

L, Q = @constinferred lq_compact(t)
@test L * Q ≈ t
@test isisometric(Q; side = :right)
@test dim(Q) == dim(L) == dim(t)

L, Q = @constinferred lq_compact(t, DefaultAlgorithm())
@test L * Q ≈ t
@test isisometric(Q; side = :right)
@test dim(Q) == dim(L) == dim(t)

L, Q = @constinferred right_orth(t)
@test L * Q ≈ t
@test isisometric(Q; side = :right)
@test dim(Q) == dim(L) == dim(t)

L, Q = @constinferred right_orth(t, DefaultAlgorithm())
@test L * Q ≈ t
@test isisometric(Q; side = :right)
@test dim(Q) == dim(L) == dim(t)

Nᴴ = @constinferred lq_null(t)
@test isunitary(Nᴴ)
@test norm(t * Nᴴ') ≈ 0 atol = 100 * eps(norm(t))

Nᴴ = @constinferred lq_null(t, DefaultAlgorithm())
@test isunitary(Nᴴ)
@test norm(t * Nᴴ') ≈ 0 atol = 100 * eps(norm(t))
end
end
TensorKitTestSuite.run_testsuite(:factorizations, "QR decomposition", V)
TensorKitTestSuite.run_testsuite(:factorizations, "LQ decomposition", V)
end
end
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