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‎.github/workflows/CI.yml‎

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jobs:
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call:
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uses: control-toolbox/CTActions/.github/workflows/ci.yml@main
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with:
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runners: '["ubuntu-latest"]'
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versions: '["1.12"]'

‎Project.toml‎

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@@ -5,11 +5,23 @@ authors = ["Olivier Cots <olivier.cots@toulouse-inp.fr>"]
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[deps]
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ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
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CommonSolve = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
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ExaModels = "1037b233-b668-4ce9-9b63-f9f681f55dd2"
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KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
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MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6"
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MadNLPMumps = "3b83494e-c0a4-4895-918b-9157a7a085a1"
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NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
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NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
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SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
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[compat]
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ADNLPModels = "0.8.13"
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CommonSolve = "0.2.4"
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ExaModels = "0.9.2"
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KernelAbstractions = "0.9.39"
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MadNLP = "0.8.12"
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MadNLPMumps = "0.5.1"
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NLPModels = "0.21.5"
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NLPModelsIpopt = "0.11.0"
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SolverCore = "0.3.8"
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julia = "1.11"

‎src/CTSolvers.jl‎

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@@ -3,7 +3,14 @@ module CTSolvers
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using ADNLPModels
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using ExaModels
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using KernelAbstractions
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using SolverCore
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using NLPModels
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using NLPModelsIpopt
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using MadNLP
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using MadNLPMumps
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using CommonSolve: CommonSolve, solve
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include("models.jl")
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include("solvers.jl")
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end

‎src/models.jl‎

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# ------------------------------------------------------------------------------
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# Problems definition
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struct ADNLPProblem
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build_adnlp_model::Function
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end
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function (prob::ADNLPProblem)(
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initial_guess::AbstractVector;
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kwargs...
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)::ADNLPModels.ADNLPModel
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return prob.build_adnlp_model(initial_guess; kwargs...)
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end
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struct ExaProblem
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build_exa_model::Function
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end
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function (prob::ExaProblem)(
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::Type{BaseType},
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initial_guess;
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kwargs...
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)::ExaModels.ExaModel where {BaseType<:AbstractFloat}
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return prob.build_exa_model(BaseType, initial_guess; kwargs...)
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end
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abstract type AbstractOptimizationProblem end
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# ------------------------------------------------------------------------------
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# Generic model builders with ADNLPModels and ExaModels backends
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abstract type AbstractNLPModelBackend end
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# ------------------------------------------------------------------------------
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# ADNLPModels
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struct ADNLPModelBackend <: AbstractNLPModelBackend
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# attributes
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show_time::Bool
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backend::Symbol
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empty_backends::NamedTuple
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kwargs
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# constructor
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function ADNLPModelBackend(;
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show_time::Bool=false,
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backend::Symbol=:optimized,
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empty_backends::NamedTuple=(
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hprod_backend=ADNLPModels.EmptyADbackend,
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jtprod_backend=ADNLPModels.EmptyADbackend,
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jprod_backend=ADNLPModels.EmptyADbackend,
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ghjvprod_backend=ADNLPModels.EmptyADbackend,
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),
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kwargs...,
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)
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return new(show_time, backend, empty_backends, kwargs)
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end
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end
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function build_model(
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prob::AbstractOptimizationProblem,
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initial_guess::AbstractVector,
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modeler::ADNLPModelBackend,
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)
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# build the backend options
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backend_options = if modeler.backend==:manual # we define the AD backend manually with sparsity pattern (OCP)
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(
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gradient_backend=ADNLPModels.ReverseDiffADGradient,
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jacobian_backend=ADNLPModels.SparseADJacobian,
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hessian_backend=ADNLPModels.SparseReverseADHessian,
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modeler.empty_backends...
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)
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else
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(backend=modeler.backend, modeler.empty_backends...)
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end
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# build the model
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return prob.build_adnlp_model(initial_guess;
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show_time=modeler.show_time,
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backend_options...,
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modeler.kwargs...,
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)
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end;
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# ------------------------------------------------------------------------------
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# ExaModels
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struct ExaModelBackend{
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BackendType<:Union{Nothing, KernelAbstractions.Backend}
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} <: AbstractNLPModelBackend
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base_type::DataType
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backend::BackendType
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kwargs
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function ExaModelBackend(;
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base_type::DataType=Float64,
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backend::Union{Nothing, KernelAbstractions.Backend}=nothing,
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kwargs...,
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)
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return new{typeof(backend)}(base_type, backend, kwargs)
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end
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end
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function build_model(
99+
prob::AbstractOptimizationProblem,
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initial_guess,
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modeler::ExaModelBackend,
102+
)
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return prob.build_exa_model(modeler.base_type, initial_guess;
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backend=modeler.backend,
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modeler.kwargs...,
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)
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end;

‎src/solvers.jl‎

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# ------------------------------------------------------------------------------
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# ------------------------------------------------------------------------------
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# Solvers on CPU: NLPModelsIpopt, MadNLP, MadNCL
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# NLPModelsIpopt
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function solve_with_ipopt(
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nlp::NLPModels.AbstractNLPModel;
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kwargs...,
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):: SolverCore.GenericExecutionStats
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solver = NLPModelsIpopt.IpoptSolver(nlp)
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return NLPModelsIpopt.solve!(solver, nlp; kwargs...)
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end
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# MadNLP
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function solve_with_madnlp(
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nlp::NLPModels.AbstractNLPModel;
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kwargs...,
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):: MadNLP.MadNLPExecutionStats
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solver = MadNLP.MadNLPSolver(nlp; kwargs...)
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return MadNLP.solve!(solver)
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end
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# ------------------------------------------------------------------------------
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# ------------------------------------------------------------------------------
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# Generic solvers
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abstract type AbstractNLPSolverBackend end
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# ------------------------------------------------------------------------------
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# NLPModelsIpopt
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struct NLPModelsIpoptBackend <: AbstractNLPSolverBackend
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# attributes
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max_iter::Int
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tol::Float64
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print_level::Int
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mu_strategy::String
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linear_solver::String
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sb::String
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kwargs
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41+
# constructor
42+
function NLPModelsIpoptBackend(;
43+
max_iter::Int=100,
44+
tol::Float64=1e-6,
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print_level::Int=5,
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mu_strategy::String="adaptive",
47+
linear_solver::String="Mumps",
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sb::String="yes",
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kwargs...,
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)
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return new(max_iter, tol, print_level, mu_strategy, linear_solver, sb, kwargs)
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end
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end
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function CommonSolve.solve(
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prob::AbstractOptimizationProblem,
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initial_guess,
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modeler::AbstractNLPModelBackend,
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solver::NLPModelsIpoptBackend;
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):: SolverCore.GenericExecutionStats
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# build the model
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nlp = build_model(prob, initial_guess, modeler)
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# solve the problem
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return solve_with_ipopt(nlp;
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max_iter=solver.max_iter,
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tol=solver.tol,
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print_level=solver.print_level,
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mu_strategy=solver.mu_strategy,
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linear_solver=solver.linear_solver,
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sb=solver.sb,
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solver.kwargs...
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)
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end
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# ------------------------------------------------------------------------------
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# MadNLP
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struct MadNLPBackend <: AbstractNLPSolverBackend
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# attributes
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max_iter::Int
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tol::Float64
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print_level::MadNLP.LogLevels
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linear_solver::Type{<:MadNLP.AbstractLinearSolver}
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kwargs
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# constructor
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function MadNLPBackend(;
89+
max_iter::Int=100,
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tol::Float64=1e-6,
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print_level::MadNLP.LogLevels=MadNLP.INFO,
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linear_solver::Type{<:MadNLP.AbstractLinearSolver}=MadNLPMumps.MumpsSolver,
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kwargs...,
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)
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return new(max_iter, tol, print_level, linear_solver, kwargs)
96+
end
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end
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function CommonSolve.solve(
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prob::AbstractOptimizationProblem,
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initial_guess,
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modeler::AbstractNLPModelBackend,
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solver::MadNLPBackend;
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):: MadNLP.MadNLPExecutionStats
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# build the model
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nlp = build_model(prob, initial_guess, modeler)
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# solve the problem
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return solve_with_madnlp(nlp;
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max_iter=solver.max_iter,
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tol=solver.tol,
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print_level=solver.print_level,
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linear_solver=solver.linear_solver,
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solver.kwargs...
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)
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end

‎test/Project.toml‎

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[deps]
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ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
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Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
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CommonSolve = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
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ExaModels = "1037b233-b668-4ce9-9b63-f9f681f55dd2"
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MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6"
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MadNLPMumps = "3b83494e-c0a4-4895-918b-9157a7a085a1"
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NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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‎test/rosenbrock.jl‎

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function rosenbrock_objective(x)
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return (x[1] - 1.0)^2 + 100*(x[2] - x[1]^2)^2
3+
end
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function rosenbrock_constraint(x)
5+
return x[1]
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end
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function rosenbrock_is_minimize()
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return true
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end
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11+
function Rosenbrock()
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# define common functions
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F(x) = rosenbrock_objective(x)
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c(x) = rosenbrock_constraint(x)
15+
lcon = [-Inf]
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ucon = [10.0]
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minimize = rosenbrock_is_minimize()
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# define ADNLPModels builder
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function build_adnlp_model(
21+
initial_guess::AbstractVector;
22+
kwargs...
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)::ADNLPModels.ADNLPModel
24+
return ADNLPModels.ADNLPModel(
25+
F,
26+
initial_guess,
27+
c,
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lcon,
29+
ucon;
30+
minimize=minimize,
31+
kwargs...
32+
)
33+
end
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# define ExaModels builder
36+
function build_exa_model(
37+
::Type{BaseType},
38+
initial_guess::AbstractVector;
39+
kwargs...
40+
)::ExaModels.ExaModel where {BaseType<:AbstractFloat}
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m = ExaModels.ExaCore(BaseType; minimize=minimize, kwargs...)
42+
x = ExaModels.variable(m, length(initial_guess); start=initial_guess)
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ExaModels.objective(m, F(x))
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ExaModels.constraint(m, c(x); lcon=lcon, ucon=ucon)
45+
return ExaModels.ExaModel(m)
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end
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# return the problem
49+
return OptimizationProblem(
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CTSolvers.ADNLPProblem(build_adnlp_model),
51+
CTSolvers.ExaProblem(build_exa_model),
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)
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end
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const rosenbrock_prob = Rosenbrock()
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const rosenbrock_init = [-1.2; 1.0]
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const rosenbrock_solu = [1.0; 1.0]

‎test/runtests.jl‎

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@@ -6,25 +6,30 @@ using CTSolvers
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using ADNLPModels
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using ExaModels
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using NLPModels
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using CommonSolve
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using MadNLP
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using MadNLPMumps
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1013
# ------------------------------------------------------------------------------
1114
# Problems definition
1215
struct OptimizationProblem <: CTSolvers.AbstractOptimizationProblem
1316
build_adnlp_model::CTSolvers.ADNLPProblem
1417
build_exa_model::CTSolvers.ExaProblem
1518
end
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1719
include("rosenbrock.jl")
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1921
# ------------------------------------------------------------------------------
2022
# Tests
2123
const VERBOSE = true
22-
@testset verbose = VERBOSE showtiming = true "CTSolvers tests" begin
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const SHOWTIMING = true
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@testset verbose=VERBOSE showtiming=SHOWTIMING "CTSolvers tests" begin
2327
for name in (
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# :aqua,
2529
:models,
30+
:solvers,
2631
)
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@testset "$(name)" begin
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@testset "$(name)" verbose=VERBOSE showtiming=SHOWTIMING begin
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test_name = Symbol(:test_, name)
2934
include("$(test_name).jl")
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@eval $test_name()

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