Add ipopt method for AbstractNLSModel

Project.toml

@@ -5,11 +5,13 @@ version = "0.10.4"
 [deps]
 Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
+NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
 SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 
 [compat]
 Ipopt = "1"
-NLPModels = "0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.21"
+NLPModels = "0.19, 0.20, 0.21"
+NLPModelsModifiers = "0.7"
 SolverCore = "0.3"
 julia = "^1.6"
 

src/NLPModelsIpopt.jl

@@ -3,6 +3,7 @@ module NLPModelsIpopt
 export ipopt, IpoptSolver, reset!, solve!
 
 using NLPModels, Ipopt, SolverCore
+using NLPModelsModifiers: FeasibilityFormNLS
 
 const ipopt_statuses = Dict(
   0 => :first_order,
@@ -181,6 +182,44 @@ function ipopt(nlp::AbstractNLPModel; kwargs...)
   return solve!(solver, nlp, stats; kwargs...)
 end
 
+"""
+    ipopt(nls::AbstractNLSModel; kwargs...)
+
+Solve the least-squares problem `nls` using `IPOPT` by moving the nonlinear residual to the constraints.
+
+# Arguments
+- `nls::AbstractNLSModel`: The least-squares problem to solve.
+
+For advanced usage, first define an `IpoptSolver` to preallocate the memory used in the algorithm, and then call `solve!`:
+    solver = IpoptSolver(nls)
+    solve!(solver, nls; kwargs...)
+
+# Examples
+```julia
+using NLPModelsIpopt, ADNLPModels
+nls = ADNLSModel(x -> [x[1] - 1, x[2] - 2], [0.0, 0.0], 2)
+stats = ipopt(nls, print_level = 0)
+```
+"""
+function ipopt(ff_nls::FeasibilityFormNLS; kwargs...)
+  solver = IpoptSolver(ff_nls)
+  stats = GenericExecutionStats(ff_nls)
+  stats = solve!(solver, ff_nls, stats; kwargs...)
+
+  return stats
+end
+
+function ipopt(nls::AbstractNLSModel; kwargs...)
+  ff_nls = FeasibilityFormNLS(nls)
+  stats = ipopt(ff_nls; kwargs...)
+
+  stats.solution = length(stats.solution) >= nls.meta.nvar ? stats.solution[1:nls.meta.nvar] : stats.solution
+  stats.multipliers_L = length(stats.multipliers_L) >= nls.meta.nvar ? stats.multipliers_L[1:nls.meta.nvar] : stats.multipliers_L
+  stats.multipliers_U = length(stats.multipliers_U) >= nls.meta.nvar ? stats.multipliers_U[1:nls.meta.nvar] : stats.multipliers_U
+  stats.multipliers = length(stats.multipliers) >= nls.meta.ncon ? stats.multipliers[end-nls.meta.ncon+1:end] : stats.multipliers
+  return stats
+end
+
 function SolverCore.solve!(
   solver::IpoptSolver,
   nlp::AbstractNLPModel,

test/runtests.jl

@@ -1,4 +1,5 @@
 using ADNLPModels, NLPModelsIpopt, NLPModels, Ipopt, SolverCore, Test
+using NLPModelsModifiers: FeasibilityFormNLS
 
 @testset "Restart NLPModelsIpopt" begin
   nlp = ADNLPModel(x -> (x[1] - 1)^2 + 100 * (x[2] - x[1]^2)^2, [-1.2; 1.0])
@@ -107,3 +108,12 @@ end
   @test stats.primal_feas ≈ 0.0
   @test stats.dual_feas ≈ 0.0 atol = 1.49e-8
 end
+
+@testset "ipopt with AbstractNLSModel" begin
+  nls = ADNLSModel(x -> [x[1] - 1, x[2] - 2], [0.0, 0.0], 2)
+  stats = ipopt(nls, print_level = 0)
+  @test isapprox(stats.solution, [1.0, 2.0], rtol = 1e-6)
+  @test stats.status == :first_order
+  @test stats.iter >= 0
+  @test isapprox(stats.dual_feas, 0.0; atol=1e-8)
+end