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Update for NLPModels 0.22 #130

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18dc6d2
update for latest NLPModels
klamike Nov 17, 2025
bbdb17c
bump compat
klamike Nov 20, 2025
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4 changes: 2 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,8 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[compat]
NLPModels = "0.21"
NLPModelsModifiers = "0.7"
NLPModels = "0.22"
NLPModelsModifiers = "0.8"
julia = "^1.6"

[extras]
Expand Down
29 changes: 22 additions & 7 deletions src/allocs_model.jl
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Expand Up @@ -139,24 +139,39 @@ function test_allocs_nlpmodels(nlp::AbstractNLPModel; linear_api = false, exclud
x = get_x0(nlp)
vals = Vector{eltype(x)}(undef, nnzj)
fun = Symbol(:jac_, type, :_coord!)
eval(fun)(nlp, x, vals)
nlp_allocations[fun] = @allocated eval(fun)(nlp, x, vals)
if type == :lin
eval(fun)(nlp, vals)
nlp_allocations[fun] = @allocated eval(fun)(nlp, vals)
else
eval(fun)(nlp, x, vals)
nlp_allocations[fun] = @allocated eval(fun)(nlp, x, vals)
end
end
if !(jprod in exclude)
x = get_x0(nlp)
v = copy(x)
Jv = Vector{eltype(x)}(undef, nn)
fun = Symbol(:jprod_, type, :!)
eval(fun)(nlp, x, v, Jv)
nlp_allocations[fun] = @allocated eval(fun)(nlp, x, v, Jv)
if type == :lin
eval(fun)(nlp, v, Jv)
nlp_allocations[fun] = @allocated eval(fun)(nlp, v, Jv)
else
eval(fun)(nlp, x, v, Jv)
nlp_allocations[fun] = @allocated eval(fun)(nlp, x, v, Jv)
end
end
if !(jtprod in exclude)
x = get_x0(nlp)
v = copy(get_y0(nlp)[1:nn])
Jtv = similar(x)
fun = Symbol(:jtprod_, type, :!)
eval(fun)(nlp, x, v, Jtv)
nlp_allocations[fun] = @allocated eval(fun)(nlp, x, v, Jtv)
if type == :lin
eval(fun)(nlp, v, Jtv)
nlp_allocations[fun] = @allocated eval(fun)(nlp, v, Jtv)
else
eval(fun)(nlp, x, v, Jtv)
nlp_allocations[fun] = @allocated eval(fun)(nlp, x, v, Jtv)
end
end
if !(jac_op in exclude)
x = get_x0(nlp)
Expand All @@ -167,7 +182,7 @@ function test_allocs_nlpmodels(nlp::AbstractNLPModel; linear_api = false, exclud
w = randn(eltype(x), nn)
fun = Symbol(:jac_, type, :_op!)
if type == :lin
J = jac_lin_op!(nlp, x, Jv, Jtv)
J = jac_lin_op!(nlp, Jv, Jtv)
mul!(Jv, J, v)
nlp_allocations[Symbol(:jac_lin_op_prod!)] = @allocated mul!(Jv, J, v)
Jt = J'
Expand Down
27 changes: 9 additions & 18 deletions src/nlp/check-dimensions.jl
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Expand Up @@ -138,7 +138,6 @@ function check_nlp_dimensions(
end
if jac ∉ exclude
@test_throws DimensionError jac(nlp, badx)
linear_api && nlp.meta.nlin > 0 && @test_throws DimensionError jac_lin(nlp, badx)
linear_api && nlp.meta.nnln > 0 && @test_throws DimensionError jac_nln(nlp, badx)
end
if jprod ∉ exclude
Expand All @@ -149,11 +148,9 @@ function check_nlp_dimensions(
@test_throws DimensionError jprod!(nlp, x, v, badJv)

if linear_api && nlp.meta.nlin > 0
@test_throws DimensionError jprod_lin(nlp, badx, v)
@test_throws DimensionError jprod_lin(nlp, x, badv)
@test_throws DimensionError jprod_lin!(nlp, badx, v, Jv_lin)
@test_throws DimensionError jprod_lin!(nlp, x, badv, Jv_lin)
@test_throws DimensionError jprod_lin!(nlp, x, v, badJv_lin)
@test_throws DimensionError jprod_lin(nlp, badv)
@test_throws DimensionError jprod_lin!(nlp, badv, Jv_lin)
@test_throws DimensionError jprod_lin!(nlp, v, badJv_lin)
end

if linear_api && nlp.meta.nnln > 0
Expand All @@ -172,11 +169,9 @@ function check_nlp_dimensions(
@test_throws DimensionError jtprod!(nlp, x, w, badJtw)

if linear_api && nlp.meta.nlin > 0
@test_throws DimensionError jtprod_lin(nlp, badx, w_lin)
@test_throws DimensionError jtprod_lin(nlp, x, badw_lin)
@test_throws DimensionError jtprod_lin!(nlp, badx, w_lin, Jtw)
@test_throws DimensionError jtprod_lin!(nlp, x, badw_lin, Jtw)
@test_throws DimensionError jtprod_lin!(nlp, x, w_lin, badJtw)
@test_throws DimensionError jtprod_lin(nlp, badw_lin)
@test_throws DimensionError jtprod_lin!(nlp, badw_lin, Jtw)
@test_throws DimensionError jtprod_lin!(nlp, w_lin, badJtw)
end

if linear_api && nlp.meta.nnln > 0
Expand All @@ -197,9 +192,7 @@ function check_nlp_dimensions(
if linear_api && nlp.meta.nlin > 0
@test_throws DimensionError jac_lin_structure!(nlp, badjrows_lin, jcols_lin)
@test_throws DimensionError jac_lin_structure!(nlp, jrows_lin, badjcols_lin)
@test_throws DimensionError jac_lin_coord(nlp, badx)
@test_throws DimensionError jac_lin_coord!(nlp, badx, jvals_lin)
@test_throws DimensionError jac_lin_coord!(nlp, x, badjvals_lin)
@test_throws DimensionError jac_lin_coord!(nlp, badjvals_lin)
end

if linear_api && nlp.meta.nnln > 0
Expand All @@ -222,10 +215,8 @@ function check_nlp_dimensions(
@test_throws DimensionError jac_op!(nlp, jrows, jcols, jvals, Jv, badJtw)

if linear_api && nlp.meta.nlin > 0
@test_throws DimensionError jac_lin_op(nlp, badx)
@test_throws DimensionError jac_lin_op!(nlp, badx, Jv_lin, Jtw)
@test_throws DimensionError jac_lin_op!(nlp, x, badJv_lin, Jtw)
@test_throws DimensionError jac_lin_op!(nlp, x, Jv_lin, badJtw)
@test_throws DimensionError jac_lin_op!(nlp, badJv_lin, Jtw)
@test_throws DimensionError jac_lin_op!(nlp, Jv_lin, badJtw)
@test_throws DimensionError jac_lin_op!(
nlp,
badjrows_lin,
Expand Down
24 changes: 12 additions & 12 deletions src/nlp/consistency.jl
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Expand Up @@ -395,14 +395,14 @@ function consistent_functions(nlps; linear_api = false, rtol = 1.0e-8, exclude =
end

if (intersect([jac, jac_coord], exclude) == []) && test_lin
Js = [jac_lin(nlp, x) for nlp in nlps]
Js = [jac_lin(nlp) for nlp in nlps]
Jmin = minimum(map(norm, Js))
for i = 1:N
vi = norm(Js[i])
for j = (i + 1):N
@test isapprox(vi, norm(Js[j]), atol = rtol * max(Jmin, 1.0))
end
V = jac_lin_coord(nlps[i], x)
V = jac_lin_coord(nlps[i])
I, J = jac_lin_structure(nlps[i])
@test length(I) == length(J) == length(V) == nlps[i].meta.lin_nnzj
@test isapprox(sparse(I, J, V, mlin, n), Js[i], atol = rtol * max(Jmin, 1.0))
Expand All @@ -411,7 +411,7 @@ function consistent_functions(nlps; linear_api = false, rtol = 1.0e-8, exclude =
@test IS == I
@test JS == J
tmp_V = zeros(nlps[i].meta.lin_nnzj)
jac_lin_coord!(nlps[i], x, tmp_V)
jac_lin_coord!(nlps[i], tmp_V)
@test tmp_V == V
end
end
Expand Down Expand Up @@ -470,26 +470,26 @@ function consistent_functions(nlps; linear_api = false, rtol = 1.0e-8, exclude =
end

if (!(jprod in exclude)) && test_lin
Jlinops = Any[jac_lin_op(nlp, x) for nlp in nlps]
Jps = Any[jprod_lin(nlp, x, v) for nlp in nlps]
Jlinops = Any[jac_lin_op(nlp) for nlp in nlps]
Jps = Any[jprod_lin(nlp, v) for nlp in nlps]
for i = 1:N
@test isapprox(Jps[i], Jlinops[i] * v, atol = rtol * max(Jmin, 1.0))
vi = norm(Jps[i])
for j = (i + 1):N
@test isapprox(vi, norm(Jps[j]), atol = rtol * max(Jmin, 1.0))
end
tmpjv = jprod_lin!(nlps[i], x, v, tmp_mlin)
tmpjv = jprod_lin!(nlps[i], v, tmp_mlin)
@test isapprox(tmpjv, tmp_mlin, atol = rtol * max(Jmin, 1.0))
@test isapprox(Jps[i], tmp_mlin, atol = rtol * max(Jmin, 1.0))
fill!(tmp_mlin, 0)
J = jac_lin_op!(nlps[i], x, tmp_mlin, tmp_n)
J = jac_lin_op!(nlps[i], tmp_mlin, tmp_n)
res = J * v
@test isapprox(res, Jps[i], atol = rtol * max(Jmin, 1.0))
@test isapprox(res, tmp_mlin, atol = rtol * max(Jmin, 1.0))

if !(jac_coord in exclude)
rows, cols = jac_lin_structure(nlps[i])
vals = jac_lin_coord(nlps[i], x)
vals = jac_lin_coord(nlps[i])
jprod_lin!(nlps[i], rows, cols, vals, v, tmp_mlin)
@test isapprox(Jps[i], tmp_mlin, atol = rtol * max(Jmin, 1.0))

Expand Down Expand Up @@ -564,25 +564,25 @@ function consistent_functions(nlps; linear_api = false, rtol = 1.0e-8, exclude =

if (!(jtprod in exclude)) && test_lin
w = 10 * [-(-1.0)^i for i = 1:mlin]
Jtps = Any[jtprod_lin(nlp, x, w) for nlp in nlps]
Jtps = Any[jtprod_lin(nlp, w) for nlp in nlps]
for i = 1:N
@test isapprox(Jtps[i], Jlinops[i]' * w, atol = rtol * max(Jmin, 1.0))
vi = norm(Jtps[i])
for j = (i + 1):N
@test isapprox(vi, norm(Jtps[j]), atol = rtol * max(Jmin, 1.0))
end
tmpjtv = jtprod_lin!(nlps[i], x, w, tmp_n)
tmpjtv = jtprod_lin!(nlps[i], w, tmp_n)
@test isapprox(Jtps[i], tmp_n, atol = rtol * max(Jmin, 1.0))
@test isapprox(tmpjtv, tmp_n, atol = rtol * max(Jmin, 1.0))
fill!(tmp_n, 0)
J = jac_lin_op!(nlps[i], x, tmp_mlin, tmp_n)
J = jac_lin_op!(nlps[i], tmp_mlin, tmp_n)
res = J' * w
@test isapprox(res, Jtps[i], atol = rtol * max(Jmin, 1.0))
@test isapprox(res, tmp_n, atol = rtol * max(Jmin, 1.0))

if !(jac_coord in exclude)
rows, cols = jac_lin_structure(nlps[i])
vals = jac_lin_coord(nlps[i], x)
vals = jac_lin_coord(nlps[i])
jtprod_lin!(nlps[i], rows, cols, vals, w, tmp_n)
@test isapprox(Jtps[i], tmp_n, atol = rtol * max(Jmin, 1.0))

Expand Down
8 changes: 4 additions & 4 deletions src/nlp/coord-memory.jl
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Expand Up @@ -48,11 +48,11 @@ function coord_memory_nlp(nlp::AbstractNLPModel; linear_api = false, exclude = [
al2 = @allocated jac_coord!(nlp, x, vals)
@test (al2 < al1) | (al2 == 0)
if linear_api && nlp.meta.nlin > 0
vals = jac_lin_coord(nlp, x)
al1 = @allocated vals = jac_lin_coord(nlp, x)
vals = jac_lin_coord(nlp)
al1 = @allocated vals = jac_lin_coord(nlp)
V = zeros(nlp.meta.lin_nnzj)
jac_lin_coord!(nlp, x, vals)
al2 = @allocated jac_lin_coord!(nlp, x, vals)
jac_lin_coord!(nlp, vals)
al2 = @allocated jac_lin_coord!(nlp, vals)
@test (al2 < al1) | (al2 == 0)
end
if linear_api && nlp.meta.nnln > 0
Expand Down
6 changes: 3 additions & 3 deletions src/nlp/multiple-precision.jl
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Expand Up @@ -74,8 +74,8 @@ function multiple_precision_nlp(
end
if linear_api && nlp.meta.nlin > 0
@test cons ∈ exclude || typeof(cons_lin(nlp, x)) == S
@test jac ∈ exclude || eltype(jac_lin(nlp, x)) == T
@test jac_op ∈ exclude || eltype(jac_lin_op(nlp, x)) == T
@test jac ∈ exclude || eltype(jac_lin(nlp)) == T
@test jac_op ∈ exclude || eltype(jac_lin_op(nlp)) == T
end
if jac_coord ∉ exclude && jac_op ∉ exclude
rows, cols = jac_structure(nlp)
Expand All @@ -94,7 +94,7 @@ function multiple_precision_nlp(
end
if linear_api && nlp.meta.nlin > 0
rows, cols = jac_lin_structure(nlp)
vals = jac_lin_coord(nlp, x)
vals = jac_lin_coord(nlp)
@test typeof(vals) == S
Av = fill!(S(undef, nlp.meta.nlin), T(0))
Atv = fill!(S(undef, nlp.meta.nvar), T(0))
Expand Down
12 changes: 6 additions & 6 deletions src/nlp/problems/hs14.jl
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Expand Up @@ -161,10 +161,10 @@ function NLPModels.jac_nln_structure!(
return rows, cols
end

function NLPModels.jac_lin_coord!(nlp::HS14, x::AbstractVector{T}, vals::AbstractVector) where {T}
@lencheck 2 x
function NLPModels.jac_lin_coord!(nlp::HS14, vals::AbstractVector)
@lencheck 2 vals
increment!(nlp, :neval_jac_lin)
T = eltype(vals)
vals[1] = T(1)
vals[2] = T(-2)
return vals
Expand All @@ -179,9 +179,9 @@ function NLPModels.jac_nln_coord!(nlp::HS14, x::AbstractVector, vals::AbstractVe
return vals
end

function NLPModels.jprod_lin!(nlp::HS14, x::AbstractVector, v::AbstractVector, Jv::AbstractVector)
function NLPModels.jprod_lin!(nlp::HS14, v::AbstractVector, Jv::AbstractVector)
@lencheck 1 Jv
@lencheck 2 x v
@lencheck 2 v
increment!(nlp, :neval_jprod_lin)
Jv[1] = v[1] - 2 * v[2]
return Jv
Expand All @@ -204,8 +204,8 @@ function NLPModels.jtprod!(nlp::HS14, x::AbstractVector, v::AbstractVector, Jtv:
return Jtv
end

function NLPModels.jtprod_lin!(nlp::HS14, x::AbstractVector, v::AbstractVector, Jtv::AbstractVector)
@lencheck 2 x Jtv
function NLPModels.jtprod_lin!(nlp::HS14, v::AbstractVector, Jtv::AbstractVector)
@lencheck 2 Jtv
@lencheck 1 v
increment!(nlp, :neval_jtprod_lin)
Jtv[1] = v[1]
Expand Down
11 changes: 5 additions & 6 deletions src/nlp/problems/lincon.jl
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Expand Up @@ -197,10 +197,10 @@ function NLPModels.jac_lin_structure!(
return rows, cols
end

function NLPModels.jac_lin_coord!(nlp::LINCON, x::AbstractVector{T}, vals::AbstractVector) where {T}
@lencheck 15 x
function NLPModels.jac_lin_coord!(nlp::LINCON, vals::AbstractVector)
@lencheck 17 vals
increment!(nlp, :neval_jac_lin)
T = eltype(vals)
vals[1] = T(15)
vals[2] = T(1)
vals[3] = T(2)
Expand All @@ -221,8 +221,8 @@ function NLPModels.jac_lin_coord!(nlp::LINCON, x::AbstractVector{T}, vals::Abstr
return vals
end

function NLPModels.jprod_lin!(nlp::LINCON, x::AbstractVector, v::AbstractVector, Jv::AbstractVector)
@lencheck 15 x v
function NLPModels.jprod_lin!(nlp::LINCON, v::AbstractVector, Jv::AbstractVector)
@lencheck 15 v
@lencheck 11 Jv
increment!(nlp, :neval_jprod_lin)
Jv[1] = 15 * v[15]
Expand All @@ -241,11 +241,10 @@ end

function NLPModels.jtprod_lin!(
nlp::LINCON,
x::AbstractVector,
v::AbstractVector,
Jtv::AbstractVector,
)
@lencheck 15 x Jtv
@lencheck 15 Jtv
@lencheck 11 v
increment!(nlp, :neval_jtprod_lin)
Jtv[1] = 1 * v[7] + 3 * v[8]
Expand Down
11 changes: 5 additions & 6 deletions src/nlp/problems/linsv.jl
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Expand Up @@ -135,18 +135,18 @@ function NLPModels.jac_lin_structure!(
return rows, cols
end

function NLPModels.jac_lin_coord!(nlp::LINSV, x::AbstractVector{T}, vals::AbstractVector) where {T}
@lencheck 2 x
function NLPModels.jac_lin_coord!(nlp::LINSV, vals::AbstractVector)
@lencheck 3 vals
increment!(nlp, :neval_jac_lin)
T = eltype(vals)
vals[1] = T(1)
vals[2] = T(1)
vals[3] = T(1)
return vals
end

function NLPModels.jprod_lin!(nlp::LINSV, x::AbstractVector, v::AbstractVector, Jv::AbstractVector)
@lencheck 2 x v Jv
function NLPModels.jprod_lin!(nlp::LINSV, v::AbstractVector, Jv::AbstractVector)
@lencheck 2 v Jv
increment!(nlp, :neval_jprod_lin)
Jv[1] = v[1] + v[2]
Jv[2] = v[2]
Expand All @@ -155,11 +155,10 @@ end

function NLPModels.jtprod_lin!(
nlp::LINSV,
x::AbstractVector,
v::AbstractVector,
Jtv::AbstractVector,
)
@lencheck 2 x v Jtv
@lencheck 2 v Jtv
increment!(nlp, :neval_jtprod_lin)
Jtv[1] = v[1]
Jtv[2] = v[1] + v[2]
Expand Down
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