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dpo merged 2 commits into from
Apr 24, 2026
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Merged

add option to change subsolver in AL #310

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add option to change subsolver in AL
MaxenceGollier Mar 9, 2026
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MaxenceGollier Apr 24, 2026
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14 changes: 8 additions & 6 deletions src/AL_alg.jl
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Expand Up @@ -81,7 +81,7 @@ where y is an estimate of the Lagrange multiplier vector for the constraints lco

For advanced usage, first define a solver "ALSolver" to preallocate the memory used in the algorithm, and then call `solve!`:

solver = ALSolver(reg_nlp)
solver = ALSolver(reg_nlp; subsolver = R2Solver)
solve!(solver, reg_nlp)

stats = GenericExecutionStats(reg_nlp.model)
Expand All @@ -108,7 +108,7 @@ If adopted, the Hessian is accessed as an abstract operator and need not be the
- `max_iter::Int = 10000`: maximum number of iterations;
- `max_time::Float64 = 30.0`: maximum time limit in seconds;
- `max_eval::Int = -1`: maximum number of evaluation of the objective function (negative number means unlimited);
- `subsolver::AbstractOptimizationSolver = has_bounds(nlp) ? TR : R2`: the procedure used to compute a step (e.g. `PG`, `R2`, `TR` or `TRDH`);
- `subsolver::AbstractOptimizationSolver = R2Solver`: the procedure used to compute a step (e.g. `R2Solver`, `R2NSolver`, `R2DHSolver`, `TRSolver` or `TRDHSolver`);
- `subsolver_logger::AbstractLogger`: a logger to pass to the subproblem solver;
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- `init_penalty::T = T(10)`: initial penalty parameter;
- `factor_penalty_up::T = T(2)`: multiplicative factor to increase the penalty parameter;
Expand Down Expand Up @@ -148,7 +148,7 @@ mutable struct ALSolver{T, V, M, Pb, ST} <: AbstractOptimizationSolver
sub_stats::GenericExecutionStats{T, V, V, T}
end

function ALSolver(reg_nlp::AbstractRegularizedNLPModel{T, V}; kwargs...) where {T, V}
function ALSolver(reg_nlp::AbstractRegularizedNLPModel{T, V}; subsolver = R2Solver, kwargs...) where {T, V}
nlp = reg_nlp.model
nvar, ncon = nlp.meta.nvar, nlp.meta.ncon
x = V(undef, nvar)
Expand All @@ -157,7 +157,7 @@ function ALSolver(reg_nlp::AbstractRegularizedNLPModel{T, V}; kwargs...) where {
has_bnds = has_bounds(nlp)
sub_model = AugLagModel(nlp, V(undef, ncon), T(0), x, T(0), cx)
sub_problem = RegularizedNLPModel(sub_model, reg_nlp.h, reg_nlp.selected)
sub_solver = R2Solver(reg_nlp; kwargs...)
sub_solver = subsolver(sub_problem; kwargs...)
sub_stats = RegularizedExecutionStats(sub_problem)
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M = typeof(nlp)
ST = typeof(sub_solver)
Expand All @@ -182,8 +182,10 @@ end
"AL(::Val{:equ}, ...) should only be called for equality-constrained problems with bounded variables. Use AL(...)",
)
end
solver = ALSolver(reg_nlp)
solve!(solver, reg_nlp; kwargs...)
kwargs_dict = Dict(kwargs...)
subsolver = pop!(kwargs_dict, :subsolver, R2Solver)
solver = ALSolver(reg_nlp, subsolver = subsolver)
solve!(solver, reg_nlp; kwargs_dict...)
end

function SolverCore.solve!(
Expand Down
9 changes: 8 additions & 1 deletion test/test_AL.jl
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Expand Up @@ -5,7 +5,14 @@ problem_list = [:hs8]
for problem in problem_list
nlp = eval(problem)(backend = :optimized)
for h in (NormL1(1.0),)
stats = AL(nlp, h, atol = 1e-3, verbose = 1)
stats = AL(nlp, h, subsolver = R2Solver, atol = 1e-3, verbose = 1, subsolver_max_iter = 1000)
@test stats.status == :first_order
@test stats.primal_feas <= 1e-2
@test stats.dual_feas <= 1e-2
@test length(stats.solution) == nlp.meta.nvar
@test typeof(stats.solution) == typeof(nlp.meta.x0)

stats = AL(LBFGSModel(nlp), h, subsolver = R2NSolver, atol = 1e-3, verbose = 1, subsolver_max_iter = 1000)
@test stats.status == :first_order
@test stats.primal_feas <= 1e-2
@test stats.dual_feas <= 1e-2
Expand Down
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