Specify reset! SolverCore and NLPModels

Author: tmigot

src/main.jl

@@ -113,7 +113,7 @@ function SolverCore.solve!(
   xt, xtnext, ∇f, ∇fnext = workspace.xt, workspace.xtnext, workspace.∇f, workspace.∇fnext
   d = workspace.d
   Hx = get_hess(workspace.Hstruct)
-  reset!(stats)
+  SolverCore.reset!(stats)
 
   α = TR.α₀
   max_unsuccinarow = TR.max_unsuccinarow

src/trarc_solver.jl

@@ -141,7 +141,7 @@ end
 function SolverCore.reset!(solver::TRARCSolver, nlp::AbstractNLPModel)
   @assert nlp.meta.nvar == solver.stp.pb.meta.nvar
   @assert nlp.meta.ncon == solver.stp.pb.meta.ncon
-  reset!(solver)
+  SolverCore.reset!(solver)
   solver.stp.pb = nlp
   reinit!(solver.stp)
   solver

test/allocs.jl

@@ -41,8 +41,8 @@ using NLPModelsTest, NLPModels, SolverCore
         solver = TRARCSolver(nlp; hess_type = ht, pdata_type = PDataKARC)
         stats = GenericExecutionStats(nlp)
         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
@@ -58,8 +58,8 @@ using NLPModelsTest, NLPModels, SolverCore
         solver = TRARCSolver(nlp; hess_type = ht, pdata_type = PDataKARC)
         stats = GenericExecutionStats(nlp)
         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
@@ -88,10 +88,10 @@ norm_∇f = norm(∇f)
 using Profile, PProf
 Profile.Allocs.clear()
 NLPModels.reset!(nlp)
-reset!(solver)
+SolverCore.reset!(solver)
 @time solve!(solver, nlp, stats)
 NLPModels.reset!(nlp)
-reset!(solver)
+SolverCore.reset!(solver)
 @time Profile.Allocs.@profile sample_rate=1 solve!(solver, nlp, stats)
 PProf.Allocs.pprof(from_c = false)
 

test/runtests.jl

@@ -39,10 +39,10 @@ end
     eval(solver)(nlpstop, verbose = verbose)
     final_nlp_at_x, optimal = nlpstop.current_state, nlpstop.meta.optimal
     @test optimal
-    reset!(nlp)
+    NLPModels.reset!(nlp)
     stats = eval(solver)(nlp, verbose = verbose)
     @test stats.status == :first_order
-    reset!(nlp)
+    NLPModels.reset!(nlp)
   end
 end