test with adnlpproblmems

Author: MaxenceGollier

Project.toml

@@ -4,40 +4,44 @@ author = ["Robert Baraldi <rbaraldi@uw.edu> and Dominique Orban <dominique.orban
 version = "0.1.0"
 
 [deps]
+ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
 Arpack = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 ManualNLPModels = "30dfa513-9b2f-4fb3-9796-781eabac1617"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
+OptimizationProblems = "5049e819-d29b-5fba-b941-0eee7e64c1c6"
 Percival = "01435c0c-c90d-11e9-3788-63660f8fbccc"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProximalOperators = "a725b495-10eb-56fe-b38b-717eba820537"
 RegularizedProblems = "ea076b23-609f-44d2-bb12-a4ae45328278"
+ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 ShiftedProximalOperators = "d4fd37fa-580c-4e43-9b30-361c21aae263"
 SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 
 [compat]
+ADNLPModels = "0.8.13"
 Arpack = "0.5"
-CUTEst = "1.3.4"
 LinearOperators = "2.10.0"
 ManualNLPModels = "0.2.0"
 NLPModels = "0.19, 0.20, 0.21"
 NLPModelsModifiers = "0.7"
+OptimizationProblems = "0.9.2"
 Percival = "0.7.2"
 ProximalOperators = "0.15"
 RegularizedProblems = "0.1.1"
+ReverseDiff = "1.16.1"
 ShiftedProximalOperators = "0.2"
 SolverCore = "0.3.0"
 julia = "^1.6.0"
 
 [extras]
-CUTEst = "1b53aba6-35b6-5f92-a507-53c67d53f819"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RegularizedProblems = "ea076b23-609f-44d2-bb12-a4ae45328278"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestSetExtensions = "98d24dd4-01ad-11ea-1b02-c9a08f80db04"
 
 [targets]
-test = ["CUTEst", "Random", "RegularizedProblems", "Test", "TestSetExtensions"]
+test = ["Random", "RegularizedProblems", "Test", "TestSetExtensions"]

test/runtests.jl

@@ -1,7 +1,7 @@
 using LinearAlgebra: length
 using LinearAlgebra, Random, Test
 using ProximalOperators
-using CUTEst, NLPModels, NLPModelsModifiers, RegularizedProblems, RegularizedOptimization, SolverCore
+using ADNLPModels, OptimizationProblems, OptimizationProblems.ADNLPProblems, NLPModels, NLPModelsModifiers, RegularizedProblems, RegularizedOptimization, SolverCore
 
 const global compound = 1
 const global nz = 10 * compound
@@ -10,6 +10,7 @@ const global bpdn, bpdn_nls, sol = bpdn_model(compound)
 const global bpdn2, bpdn_nls2, sol2 = bpdn_model(compound, bounds = true)
 const global λ = norm(grad(bpdn, zeros(bpdn.meta.nvar)), Inf) / 10
 
+include("test_allocs.jl")
 include("test_AL.jl")
 
 for (mod, mod_name) ∈ ((x -> x, "exact"), (LSR1Model, "lsr1"), (LBFGSModel, "lbfgs"))

test/test_AL.jl

@@ -1,8 +1,7 @@
-problem_list = ["HS8",]
 
 @testset "Augmented Lagrangian" begin
-  for problem_name in problem_list
-    nlp = CUTEstModel(problem_name)
+  for problem in problem_list
+    nlp = eval(problem)(backend = :optimized)
     for h in (NormL1(1.0), NormL2(1.0))
       stats = AL(nlp, h, atol = 1e-3, verbose = 1)
       @test stats.status == :first_order

test/test_allocs.jl

@@ -66,7 +66,8 @@ end
       end
     end
     @testset "Augmented Lagrangian" begin
-      reg_nlp = RegularizedNLPModel(CUTEstModel("HS8"), h)
+      continue # FIXME : fails due to type instabilities in ADNLPModels...
+      reg_nlp = RegularizedNLPModel(hs8(backend = :generic), h)
       solver = ALSolver(reg_nlp)
       stats = RegularizedExecutionStats(reg_nlp)
       @test @wrappedallocs(solve!(solver, reg_nlp, stats, atol = 1e-3)) == 0