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Add attributes jump_variables and jump_constraints in the model #228

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ab61a3c
Add an attribute jump_variables in the model
amontoison Jan 21, 2026
31afbe0
Store the label of the constraints
amontoison Jan 22, 2026
c5cabd3
Update src/MOI_wrapper.jl
amontoison Jan 25, 2026
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10 changes: 5 additions & 5 deletions docs/src/tutorial.md
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Expand Up @@ -23,15 +23,15 @@ Let's define the famous Rosenbrock function
```math
f(x) = (x_1 - 1)^2 + 100(x_2 - x_1^2)^2,
```
with starting point ``x^0 = (-1.2,1.0)``.
with starting point ``x_0 = (-1.2, 1.0)``.

```@example jumpnlp
using NLPModels, NLPModelsJuMP, JuMP

x0 = [-1.2; 1.0]
model = Model() # No solver is required
@variable(model, x[i=1:2], start=x0[i])
@NLobjective(model, Min, (x[1] - 1)^2 + 100 * (x[2] - x[1]^2)^2)
@objective(model, Min, (x[1] - 1)^2 + 100 * (x[2] - x[1]^2)^2)

nlp = MathOptNLPModel(model)
```
Expand Down Expand Up @@ -148,9 +148,9 @@ using NLPModels, NLPModelsJuMP, JuMP
model = Model()
x0 = [-1.2; 1.0]
@variable(model, x[i=1:2] >= 0.0, start=x0[i])
@NLobjective(model, Min, (x[1] - 1)^2 + 100 * (x[2] - x[1]^2)^2)
@objective(model, Min, (x[1] - 1)^2 + 100 * (x[2] - x[1]^2)^2)
@constraint(model, x[1] + x[2] == 3.0)
@NLconstraint(model, x[1] * x[2] >= 1.0)
@constraint(model, x[1] * x[2] >= 1.0)

nlp = MathOptNLPModel(model)

Expand Down Expand Up @@ -184,7 +184,7 @@ using NLPModels, NLPModelsJuMP, JuMP
model = Model()
x0 = [-1.2; 1.0]
@variable(model, x[i=1:2], start=x0[i])
@NLexpression(model, F1, x[1] - 1)
@expression(model, F1, x[1] - 1)
@NLexpression(model, F2, 10 * (x[2] - x[1]^2))

nls = MathOptNLSModel(model, [F1, F2], name="rosen-nls")
Expand Down
4 changes: 2 additions & 2 deletions src/MOI_wrapper.jl
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Expand Up @@ -92,9 +92,9 @@ function MOI.copy_to(dest::Optimizer, src::MOI.ModelLike)
"No solver specified, use for instance `using Percival; JuMP.set_attribute(model, \"solver\", PercivalSolver)`",
)
end
dest.nlp, index_map = nlp_model(src)
dest.nlp = nlp_model(src)
dest.solver = dest.options["solver"](dest.nlp)
return index_map
return dest
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@blegat blegat Jan 22, 2026

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MOI.copy_to should return the index_map

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@blegat blegat Jan 22, 2026

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This is what is making the tests fail

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@amontoison amontoison Jan 22, 2026

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The index_map was just a dummy dict VariableRef(
i) => VariableRef(i).
Why do you need it?
Is it because of bridges?

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end

function MOI.optimize!(model::Optimizer)
Expand Down
33 changes: 25 additions & 8 deletions src/moi_nlp_model.jl
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Expand Up @@ -3,6 +3,8 @@ export MathOptNLPModel
mutable struct MathOptNLPModel <: AbstractNLPModel{Float64, Vector{Float64}}
meta::NLPModelMeta{Float64, Vector{Float64}}
eval::MOI.Nonlinear.Evaluator
jump_variables::Dict{String,Int}
jump_constraints::Dict{String,Int}
lincon::LinearConstraints
quadcon::QuadraticConstraints
nlcon::NonLinearStructure
Expand All @@ -26,15 +28,14 @@ function MathOptNLPModel(jmodel::JuMP.Model; kws...)
end

function MathOptNLPModel(moimodel::MOI.ModelLike; kws...)
return nlp_model(moimodel; kws...)[1]
return nlp_model(moimodel; kws...)
end

function nlp_model(moimodel::MOI.ModelLike; hessian::Bool = true, name::String = "Generic")
index_map, nvar, lvar, uvar, x0 = parser_variables(moimodel)
nlin, lincon, lin_lcon, lin_ucon, quadcon, quad_lcon, quad_ucon =
parser_MOI(moimodel, index_map, nvar)
jump_variables, variables, nvar, lvar, uvar, x0 = parser_variables(moimodel)
nlin, lincon, lin_lcon, lin_ucon, quadcon, quad_lcon, quad_ucon, jump_constraints_linear, jump_constraints_quadratic, valid_label = parser_MOI(moimodel, variables)

nlp_data = _nlp_block(moimodel)
nlp_data, valid_label2, jump_constraints_nonlinear = _nlp_block(moimodel)
nlcon = parser_NL(nlp_data, hessian = hessian)
oracles = parser_oracles(moimodel)
counters = Counters()
Expand All @@ -44,7 +45,7 @@ function nlp_model(moimodel::MOI.ModelLike; hessian::Bool = true, name::String =
if nlp_data.has_objective
obj = Objective("NONLINEAR", 0.0, spzeros(Float64, nvar), COO(), 0)
else
obj = parser_objective_MOI(moimodel, nvar, index_map)
obj = parser_objective_MOI(moimodel, variables)
end

# Total counts
Expand Down Expand Up @@ -74,9 +75,26 @@ function nlp_model(moimodel::MOI.ModelLike; hessian::Bool = true, name::String =
hess_available = hessian && oracles.hessian_oracles_supported,
)

# Label of the constraints
jump_constraints = Dict{String, Int}()
if valid_label && valid_label2 && (oracles.ncon == 0)
sizehint!(jump_constraints, ncon)
for (key, val) in jump_constraints_linear
jump_constraints[key] = val
end
for (key, val) in jump_constraints_quadratic
jump_constraints[key] = val + nlin
end
for (key, val) in jump_constraints_nonlinear
jump_constraints[key] = val + nlin + quadcon.nquad
end
end

return MathOptNLPModel(
meta,
nlp_data.evaluator,
jump_variables,
jump_constraints,
lincon,
quadcon,
nlcon,
Expand All @@ -85,8 +103,7 @@ function nlp_model(moimodel::MOI.ModelLike; hessian::Bool = true, name::String =
hv,
obj,
counters,
),
index_map
)
end

function NLPModels.obj(nlp::MathOptNLPModel, x::AbstractVector)
Expand Down
30 changes: 24 additions & 6 deletions src/moi_nls_model.jl
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Expand Up @@ -5,6 +5,8 @@ mutable struct MathOptNLSModel <: AbstractNLSModel{Float64, Vector{Float64}}
nls_meta::NLSMeta{Float64, Vector{Float64}}
Feval::MOI.Nonlinear.Evaluator
ceval::MOI.Nonlinear.Evaluator
jump_variables::Dict{String,Int}
jump_constraints::Dict{String,Int}
lls::Objective
linequ::LinearEquations
nlequ::NonLinearStructure
Expand All @@ -26,17 +28,16 @@ Construct a `MathOptNLSModel` from a `JuMP` model and a container of JuMP
"""
function MathOptNLSModel(cmodel::JuMP.Model, F; hessian::Bool = true, name::String = "Generic")
moimodel = backend(cmodel)
index_map, nvar, lvar, uvar, x0 = parser_variables(moimodel)
jump_variables, variables, nvar, lvar, uvar, x0 = parser_variables(moimodel)

lls, linequ, nlinequ = parser_linear_expression(cmodel, nvar, index_map, F)
Feval, nlequ, nnlnequ = parser_nonlinear_expression(cmodel, nvar, F, hessian = hessian)
lls, linequ, nlinequ = parser_linear_expression(cmodel, variables, F)
Feval, nlequ, nnlnequ = parser_nonlinear_expression(cmodel, variables, F, hessian = hessian)

_nlp_sync!(cmodel)
moimodel = backend(cmodel)
nlin, lincon, lin_lcon, lin_ucon, quadcon, quad_lcon, quad_ucon =
parser_MOI(moimodel, index_map, nvar)
nlin, lincon, lin_lcon, lin_ucon, quadcon, quad_lcon, quad_ucon, jump_constraints_linear, jump_constraints_quadratic, valid_label = parser_MOI(moimodel, variables)

nlp_data = _nlp_block(moimodel)
nlp_data, valid_label2, jump_constraints_nonlinear = _nlp_block(moimodel)
nlcon = parser_NL(nlp_data, hessian = hessian)
oracles = parser_oracles(moimodel)
nls_counters = NLSCounters()
Expand Down Expand Up @@ -75,11 +76,28 @@ function MathOptNLSModel(cmodel::JuMP.Model, F; hessian::Bool = true, name::Stri

nls_meta = NLSMeta(nequ, nvar, nnzj = Fnnzj, nnzh = Fnnzh, lin = collect(1:nlinequ))

# Label of the constraints
jump_constraints = Dict{String, Int}()
if valid_label && valid_label2 && (oracles.ncon == 0)
sizehint!(jump_constraints, ncon)
for (key, val) in jump_constraints_linear
jump_constraints[key] = val
end
for (key, val) in jump_constraints_quadratic
jump_constraints[key] = val + nlin
end
for (key, val) in jump_constraints_nonlinear
jump_constraints[key] = val + nlin + quadcon.nquad
end
end

return MathOptNLSModel(
meta,
nls_meta,
Feval,
nlp_data.evaluator,
jump_variables,
jump_constraints,
lls,
linequ,
nlequ,
Expand Down
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