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Add KnownJacobianSparsityDetector and KnownHessianSparsityDetector (#81)
* Add `KnownSparsityDetector` * Split Jacobians and Hessians * Add tests * More tests * Update docs * Throw `ArgumentError` * Add more testsets
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‎docs/src/index.md

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@@ -69,6 +69,8 @@ ADTypes.AbstractSparsityDetector
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ADTypes.jacobian_sparsity
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ADTypes.hessian_sparsity
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ADTypes.NoSparsityDetector
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ADTypes.KnownJacobianSparsityDetector
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ADTypes.KnownHessianSparsityDetector
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```
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### Coloring algorithm

‎src/sparse.jl

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@@ -42,6 +42,67 @@ jacobian_sparsity(f, x, ::NoSparsityDetector) = trues(length(f(x)), length(x))
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jacobian_sparsity(f!, y, x, ::NoSparsityDetector) = trues(length(y), length(x))
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hessian_sparsity(f, x, ::NoSparsityDetector) = trues(length(x), length(x))
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"""
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KnownJacobianSparsityDetector(jacobian_sparsity::AbstractMatrix) <: AbstractSparsityDetector
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Trivial sparsity detector used to return a known Jacobian sparsity pattern.
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# See also
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- [`AbstractSparsityDetector`](@ref)
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- [`KnownHessianSparsityDetector`](@ref)
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"""
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struct KnownJacobianSparsityDetector{J <: AbstractMatrix} <: AbstractSparsityDetector
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jacobian_sparsity::J
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end
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function jacobian_sparsity(f, x, sd::KnownJacobianSparsityDetector)
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sz = size(sd.jacobian_sparsity)
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sz_expected = (length(f(x)), length(x))
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sz != sz_expected &&
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throw(DimensionMismatch("Jacobian size $sz of KnownJacobianSparsityDetector doesn't match expected size $sz_expected."))
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return sd.jacobian_sparsity
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end
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function jacobian_sparsity(f!, y, x, sd::KnownJacobianSparsityDetector)
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sz = size(sd.jacobian_sparsity)
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sz_expected = (length(y), length(x))
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sz != sz_expected &&
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throw(DimensionMismatch("Jacobian size $sz of KnownJacobianSparsityDetector doesn't match expected size $sz_expected."))
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return sd.jacobian_sparsity
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end
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function hessian_sparsity(f, x, sd::KnownJacobianSparsityDetector)
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throw(ArgumentError("KnownJacobianSparsityDetector can't be used to compute Hessian sparsity."))
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end
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"""
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KnownHessianSparsityDetector(hessian_sparsity::AbstractMatrix) <: AbstractSparsityDetector
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Trivial sparsity detector used to return a known Hessian sparsity pattern.
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# See also
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- [`AbstractSparsityDetector`](@ref)
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- [`KnownJacobianSparsityDetector`](@ref)
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"""
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struct KnownHessianSparsityDetector{H <: AbstractMatrix} <: AbstractSparsityDetector
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hessian_sparsity::H
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end
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function hessian_sparsity(f, x, sd::KnownHessianSparsityDetector)
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sz = size(sd.hessian_sparsity)
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sz_expected = (length(x), length(x))
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sz != sz_expected &&
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throw(DimensionMismatch("Hessian size $sz of KnownHessianSparsityDetector doesn't match expected size $sz_expected."))
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return sd.hessian_sparsity
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end
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function jacobian_sparsity(f, x, sd::KnownHessianSparsityDetector)
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throw(ArgumentError("KnownHessianSparsityDetector can't be used to compute Jacobian sparsity."))
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end
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function jacobian_sparsity(f!, y, x, sd::KnownHessianSparsityDetector)
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throw(ArgumentError("KnownHessianSparsityDetector can't be used to compute Jacobian sparsity."))
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end
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## Coloring algorithm
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"""

‎test/runtests.jl

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@@ -7,6 +7,8 @@ using ADTypes: AbstractADType,
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SymbolicMode
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using ADTypes: dense_ad,
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NoSparsityDetector,
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KnownJacobianSparsityDetector,
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KnownHessianSparsityDetector,
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sparsity_detector,
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jacobian_sparsity,
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hessian_sparsity,

‎test/sparse.jl

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end
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@testset "Sparsity detector" begin
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sd = NoSparsityDetector()
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f_jac1(x) = vcat(x, x)
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f_jac2(x) = hcat(x, x)
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function f_jac! end
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f_hess(x) = sum(x)
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for x in (rand(2), rand(2, 3)), f in (f_jac1, f_jac2)
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y = f(x)
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Js = jacobian_sparsity(f, x, sd)
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@test Js isa AbstractMatrix
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@test size(Js) == (length(y), length(x))
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@test all(isone, Js)
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@testset "NoSparsityDetector" begin
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sd = NoSparsityDetector()
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for x in (rand(2), rand(2, 3)), f in (f_jac1, f_jac2)
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y = f(x)
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Js = jacobian_sparsity(f, x, sd)
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@test Js isa AbstractMatrix
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@test size(Js) == (length(y), length(x))
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@test all(isone, Js)
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end
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for x in (rand(2), rand(2, 3)), y in (rand(5), rand(5, 6))
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Js = jacobian_sparsity(f_jac!, y, x, sd)
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@test Js isa AbstractMatrix
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@test size(Js) == (length(y), length(x))
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@test all(isone, Js)
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end
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for x in (rand(2), rand(2, 3))
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Hs = hessian_sparsity(f_hess, x, sd)
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@test Hs isa AbstractMatrix
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@test size(Hs) == (length(x), length(x))
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@test all(isone, Hs)
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end
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end
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for x in (rand(2), rand(2, 3)), y in (rand(5), rand(5, 6))
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Js = jacobian_sparsity(f_jac!, y, x, sd)
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@test Js isa AbstractMatrix
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@test size(Js) == (length(y), length(x))
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@test all(isone, Js)
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@testset "KnownJacobianSparsityDetector" begin
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@testset "Jacobian sparsity detection" begin
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@testset "Out-of-place functions" begin
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for sx in ((2,), (2, 3)), f in (f_jac1, f_jac2)
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x = rand(sx...)
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nx = length(x)
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Jref = rand(Bool, 2 * nx, nx)
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sd = KnownJacobianSparsityDetector(Jref)
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Js = jacobian_sparsity(f, x, sd)
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@test Js isa AbstractMatrix
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@test size(Js) == (2 * nx, nx)
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@test Js === Jref
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end
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end
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@testset "In-place functions" begin
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for sx in ((2,), (2, 3)), sy in ((5,), (5, 6))
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x, y = rand(sx...), rand(sy...)
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nx, ny = length(x), length(y)
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Jref = rand(Bool, ny, nx)
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sd = KnownJacobianSparsityDetector(Jref)
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Js = jacobian_sparsity(f_jac!, y, x, sd)
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@test Js isa AbstractMatrix
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@test size(Js) == (ny, nx)
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@test Js === Jref
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end
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end
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end
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@testset "Exceptions: Hessian sparsity detection not supported" begin
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for sx in ((2,), (2, 3))
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x = rand(sx...)
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nx = length(x)
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Href = rand(Bool, nx, nx)
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sd = KnownJacobianSparsityDetector(Href)
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@test_throws ArgumentError hessian_sparsity(f_hess, x, sd)
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end
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end
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@testset "Exceptions: DimensionMismatch" begin
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sd = KnownJacobianSparsityDetector(rand(Bool, 6, 7)) # wrong Jacobian size
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for x in (rand(2), rand(2, 3)), f in (f_jac1, f_jac2)
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@test_throws DimensionMismatch jacobian_sparsity(f, x, sd)
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end
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for x in (rand(2), rand(2, 3)), y in (rand(5), rand(5, 6))
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@test_throws DimensionMismatch jacobian_sparsity(f_jac!, y, x, sd)
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end
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end
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end
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for x in (rand(2), rand(2, 3))
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Hs = hessian_sparsity(f_hess, x, sd)
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@test Hs isa AbstractMatrix
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@test size(Hs) == (length(x), length(x))
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@test all(isone, Hs)
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@testset "KnownHessianSparsityDetector" begin
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@testset "Hessian sparsity detection" begin
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for sx in ((2,), (2, 3))
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x = rand(sx...)
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nx = length(x)
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Href = rand(Bool, nx, nx)
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sd = KnownHessianSparsityDetector(Href)
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Hs = hessian_sparsity(f_hess, x, sd)
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@test Hs isa AbstractMatrix
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@test size(Hs) == (nx, nx)
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@test Hs === Href
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end
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end
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@testset "Exceptions: Jacobian sparsity detection not supported" begin
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@testset "Out-of-place functions" begin
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for sx in ((2,), (2, 3)), f in (f_jac1, f_jac2)
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x = rand(sx...)
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nx = length(x)
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Jref = rand(Bool, 2 * nx, nx)
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sd = KnownHessianSparsityDetector(Jref)
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@test_throws ArgumentError jacobian_sparsity(f, x, sd)
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end
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end
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@testset "In-place functions" begin
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for sx in ((2,), (2, 3)), sy in ((5,), (5, 6))
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x, y = rand(sx...), rand(sy...)
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nx, ny = length(x), length(y)
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Jref = rand(Bool, ny, nx)
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sd = KnownHessianSparsityDetector(Jref)
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@test_throws ArgumentError jacobian_sparsity(f_jac!, y, x, sd)
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end
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end
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end
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@testset "Exceptions: DimensionMismatch" begin
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sd = KnownHessianSparsityDetector(rand(Bool, 2, 3)) #wrong Hessian size
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for x in (rand(2), rand(2, 3))
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@test_throws DimensionMismatch hessian_sparsity(f_hess, x, sd)
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end
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end
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end
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end
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