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Add support for custom model #273

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1b17e8c
Add support for DifferentiationBackend
blegat Apr 8, 2026
c978411
Custom AD backend
blegat Apr 8, 2026
9a82f23
Checkout MOI bl/nonlinear_model
blegat Apr 21, 2026
2572921
Fix
blegat May 5, 2026
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7 changes: 7 additions & 0 deletions .github/workflows/ci.yml
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Expand Up @@ -32,6 +32,13 @@ jobs:
version: ${{ matrix.version }}
arch: ${{ matrix.arch }}
- uses: julia-actions/cache@v1
- name: dev
shell: julia --project=@. {0}
run: |
using Pkg
Pkg.add([
PackageSpec(name="MathOptInterface", rev="bl/nonlinear_model"),
])
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@odow odow May 1, 2026

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Add [sources] section to Project.toml instead

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@odow odow May 1, 2026

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https://pkgdocs.julialang.org/v1/toml-files/#The-%5Bsources%5D-section

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@blegat blegat May 3, 2026

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It doesn't work on Julia v1.10: https://pkgdocs.julialang.org/v1.10/toml-files/#The-%5Bsources%5D-section

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@odow odow May 3, 2026

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Oh. Well at minimum, it'll work for the newer versions. Just exclude 1.10 during testing.

- uses: julia-actions/julia-buildpkg@v1
- uses: julia-actions/julia-runtest@v1
with:
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1 change: 1 addition & 0 deletions Project.toml
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Expand Up @@ -3,6 +3,7 @@ uuid = "76087f3c-5699-56af-9a33-bf431cd00edd"
version = "1.2.1"

[deps]
ArrayDiff = "c45fa1ca-6901-44ac-ae5b-5513a4852d50"
CEnum = "fa961155-64e5-5f13-b03f-caf6b980ea82"
MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
NLopt_jll = "079eb43e-fd8e-5478-9966-2cf3e3edb778"
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5 changes: 3 additions & 2 deletions ext/NLoptMathOptInterfaceExt.jl
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Expand Up @@ -5,6 +5,7 @@

module NLoptMathOptInterfaceExt

import ArrayDiff
import MathOptInterface as MOI
import NLopt

Expand All @@ -31,7 +32,7 @@ mutable struct Optimizer <: MOI.AbstractOptimizer
variables::MOI.Utilities.VariablesContainer{Float64}
starting_values::Vector{Union{Nothing,Float64}}
nlp_data::MOI.NLPBlockData
nlp_model::Union{Nothing,MOI.Nonlinear.Model}
nlp_model::Union{Nothing,MOI.Nonlinear.Model,ArrayDiff.Model}
ad_backend::MOI.Nonlinear.AbstractAutomaticDifferentiation
sense::Union{Nothing,MOI.OptimizationSense}
objective::Union{
Expand Down Expand Up @@ -598,7 +599,7 @@ function _init_nlp_model(model)
if !(model.nlp_data.evaluator isa _EmptyNLPEvaluator)
error("Cannot mix the new and legacy nonlinear APIs")
end
model.nlp_model = MOI.Nonlinear.Model()
model.nlp_model = ArrayDiff.model(model.ad_backend)
end
return
end
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24 changes: 24 additions & 0 deletions test/MOI_wrapper.jl
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Expand Up @@ -361,6 +361,30 @@ function test_ListOfSupportedNonlinearOperators()
return
end

function test_ScalarNonlinearFunction_SymbolicMode()
# Test that the AutomaticDifferentiationBackend is used when building the
# evaluator. SymbolicMode is a built-in alternative to SparseReverseMode.
model = NLopt.Optimizer()
MOI.set(model, MOI.RawOptimizerAttribute("algorithm"), :LD_LBFGS)
MOI.set(
model,
MOI.AutomaticDifferentiationBackend(),
MOI.Nonlinear.SymbolicMode(),
)
x = MOI.add_variable(model)
MOI.set(model, MOI.VariablePrimalStart(), x, 2.0)
# min (x - 1)^2 => x* = 1
f = MOI.ScalarNonlinearFunction(
:^,
Any[MOI.ScalarNonlinearFunction(:-, Any[x, 1.0]), 2.0],
)
MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
MOI.set(model, MOI.ObjectiveFunction{MOI.ScalarNonlinearFunction}(), f)
MOI.optimize!(model)
@test isapprox(MOI.get(model, MOI.VariablePrimal(), x), 1.0; atol = 1e-4)
return
end

end # module

TestMOIWrapper.runtests()
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