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Merged
merged 1 commit into from
Apr 2, 2025
Merged

Specify reset! SolverCore and NLPModels #291

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6 changes: 3 additions & 3 deletions src/fomo.jl
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Expand Up @@ -264,7 +264,7 @@ function SolverCore.reset!(solver::FomoSolver{T}) where {T}
solver
end

SolverCore.reset!(solver::FomoSolver, ::AbstractNLPModel) = reset!(solver)
SolverCore.reset!(solver::FomoSolver, ::AbstractNLPModel) = SolverCore.reset!(solver)

"""
fo(nlp; kwargs...)
Expand Down Expand Up @@ -407,7 +407,7 @@ function SolverCore.reset!(solver::FoSolver{T}) where {T}
solver
end

SolverCore.reset!(solver::FoSolver, ::AbstractNLPModel) = reset!(solver)
SolverCore.reset!(solver::FoSolver, ::AbstractNLPModel) = SolverCore.reset!(solver)

function SolverCore.solve!(
solver::Union{FoSolver, FomoSolver},
Expand Down Expand Up @@ -440,7 +440,7 @@ function SolverCore.solve!(
use_momentum = typeof(solver) <: FomoSolver
is_r2 = typeof(step_backend) <: r2_step

reset!(stats)
SolverCore.reset!(stats)
start_time = time()
set_time!(stats, 0.0)

Expand Down
8 changes: 4 additions & 4 deletions src/lbfgs.jl
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Expand Up @@ -127,11 +127,11 @@ function LBFGSSolver(nlp::M; kwargs...) where {T, V, M <: AbstractNLPModel{T, V}
end

function SolverCore.reset!(solver::LBFGSSolver)
reset!(solver.H)
LinearOperators.reset!(solver.H)
end

function SolverCore.reset!(solver::LBFGSSolver, nlp::AbstractNLPModel)
reset!(solver.H)
LinearOperators.reset!(solver.H)
solver.h = LineModel(nlp, solver.x, solver.d)
solver
end
Expand Down Expand Up @@ -169,7 +169,7 @@ function SolverCore.solve!(
error("lbfgs should only be called for unconstrained problems. Try tron instead")
end

reset!(stats)
SolverCore.reset!(stats)
start_time = time()
set_time!(stats, 0.0)

Expand All @@ -187,7 +187,7 @@ function SolverCore.solve!(
d = solver.d
h = solver.h
H = solver.H
reset!(H)
LinearOperators.reset!(H)

f, ∇f = objgrad!(nlp, x, ∇f)

Expand Down
2 changes: 1 addition & 1 deletion src/tron.jl
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Expand Up @@ -259,7 +259,7 @@ function SolverCore.solve!(
μ₁ = value(solver.params.μ₁)
σ = value(solver.params.σ)

reset!(stats)
SolverCore.reset!(stats)
ℓ = nlp.meta.lvar
u = nlp.meta.uvar
n = nlp.meta.nvar
Expand Down
4 changes: 2 additions & 2 deletions src/tronls.jl
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Expand Up @@ -224,7 +224,7 @@ end
function SolverCore.reset!(solver::TronSolverNLS, nlp::AbstractNLPModel)
solver.A = jac_op_residual!(nlp, solver.xc, solver.Av, solver.Atv)
solver.AZ = solver.A * solver.ls_op
reset!(solver)
SolverCore.reset!(solver)
solver
end

Expand Down Expand Up @@ -285,7 +285,7 @@ function SolverCore.solve!(
μ₁ = value(solver.params.μ₁)
σ = value(solver.params.σ)

reset!(stats)
SolverCore.reset!(stats)
ℓ = nlp.meta.lvar
u = nlp.meta.uvar
n = nlp.meta.nvar
Expand Down
2 changes: 1 addition & 1 deletion src/trunk.jl
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Expand Up @@ -207,7 +207,7 @@ function SolverCore.solve!(
monotone = value(solver.params.monotone)
nm_itmax = value(solver.params.nm_itmax)

reset!(stats)
SolverCore.reset!(stats)
start_time = time()
set_time!(stats, 0.0)

Expand Down
2 changes: 1 addition & 1 deletion src/trunkls.jl
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Expand Up @@ -241,7 +241,7 @@ function SolverCore.solve!(
monotone = value(solver.params.monotone)
nm_itmax = value(solver.params.nm_itmax)

reset!(stats)
SolverCore.reset!(stats)
start_time = time()
set_time!(stats, 0.0)

Expand Down
8 changes: 4 additions & 4 deletions test/allocs.jl
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Expand Up @@ -48,8 +48,8 @@ if Sys.isunix()
end
with_logger(NullLogger()) do
SolverCore.solve!(solver, nlp, stats)
reset!(solver)
reset!(nlp)
SolverCore.reset!(solver)
NLPModels.reset!(nlp)
al = @wrappedallocs SolverCore.solve!(solver, nlp, stats)
@test al == 0
end
Expand All @@ -65,8 +65,8 @@ if Sys.isunix()
stats = GenericExecutionStats(nlp)
with_logger(NullLogger()) do
SolverCore.solve!(solver, nlp, stats)
reset!(solver)
reset!(nlp)
SolverCore.reset!(solver)
NLPModels.reset!(nlp)
al = @wrappedallocs SolverCore.solve!(solver, nlp, stats)
@test al == 0
end
Expand Down
8 changes: 4 additions & 4 deletions test/consistency.jl
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Expand Up @@ -12,11 +12,11 @@ function consistency()

@testset "NLP with $mtd" for mtd in [trunk, lbfgs, tron, R2, fomo]
with_logger(NullLogger()) do
reset!(unlp)
NLPModels.reset!(unlp)
stats = mtd(unlp; args...)
@test stats isa GenericExecutionStats
@test stats.status == :first_order
reset!(unlp)
NLPModels.reset!(unlp)
stats = mtd(unlp; max_eval = 1)
@test stats.status == :max_eval
slow_nlp = ADNLPModel(x -> begin
Expand All @@ -30,7 +30,7 @@ function consistency()

@testset "Quasi-Newton NLP with $mtd" for mtd in [trunk, lbfgs, tron, R2, fomo]
with_logger(NullLogger()) do
reset!(qnlp)
NLPModels.reset!(qnlp)
stats = mtd(qnlp; args...)
@test stats isa GenericExecutionStats
@test stats.status == :first_order
Expand All @@ -42,7 +42,7 @@ function consistency()
stats = mtd(unls; args...)
@test stats isa GenericExecutionStats
@test stats.status == :first_order
reset!(unls)
NLPModels.reset!(unls)
stats = mtd(unls; max_eval = 1)
@test stats.status == :max_eval
slow_nls = ADNLSModel(x -> begin
Expand Down
4 changes: 2 additions & 2 deletions test/solvers/tronls.jl
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Expand Up @@ -11,7 +11,7 @@
@test isapprox(stats.objective, 0, atol = 1e-6)
@test neval_jac_residual(model) == 0
stline = statsline(stats, [:objective, :dual_feas, :elapsed_time, :iter, :status])
reset!(model)
NLPModels.reset!(model)
end

@test_throws ErrorException tron(model, subsolver = :minres)
Expand All @@ -33,6 +33,6 @@ end
@test isapprox(stats.objective, 0, atol = 1e-6)
@test neval_jac_residual(model) == 0
stline = statsline(stats, [:objective, :dual_feas, :elapsed_time, :iter, :status])
reset!(model)
NLPModels.reset!(model)
end
end
4 changes: 2 additions & 2 deletions test/solvers/trunkls.jl
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Expand Up @@ -13,7 +13,7 @@
@test isapprox(stats.objective, 0, atol = 1e-6)
@test neval_jac_residual(model) == 0
stline = statsline(stats, [:objective, :dual_feas, :elapsed_time, :iter, :status])
reset!(model)
NLPModels.reset!(model)
end

@test_throws ErrorException trunk(model, subsolver_type = MinresSolver)
Expand All @@ -35,6 +35,6 @@ end
@test isapprox(stats.objective, 0, atol = 1e-6)
@test neval_jac_residual(model) == 0
stline = statsline(stats, [:objective, :dual_feas, :elapsed_time, :iter, :status])
reset!(model)
NLPModels.reset!(model)
end
end