Use NLPModelsTest instead of ADNLPModels (#84)

Author: tmigot

Project.toml

@@ -12,10 +12,10 @@ NLPModels = "0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.21"
 julia = "^1.6"
 
 [extras]
-ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
+NLPModelsTest = "7998695d-6960-4d3a-85c4-e1bceb8cd856"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["ADNLPModels", "LinearAlgebra", "Logging", "Test"]
+test = ["LinearAlgebra", "Logging", "NLPModelsTest", "Test"]

test/runtests.jl

@@ -2,14 +2,14 @@
 using SolverCore
 
 # Auxiliary packages
-using ADNLPModels, NLPModels
+using NLPModels, NLPModelsTest
 
 # stdlib
 using LinearAlgebra, Logging, Test
 
 if VERSION ≥ v"1.6"
   @testset "Test allocations of solver specific" begin
-    nlp = ADNLPModel(x -> sum(x), ones(2))
+    nlp = BROWNDEN()
     stats = GenericExecutionStats(nlp) # stats = GenericExecutionStats(nlp, solver_specific = Dict{Symbol, Bool}())
     function fake_solver(stats)
       set_solver_specific!(stats, :test, true)

test/test_callback.jl

@@ -1,6 +1,5 @@
 @testset "test callback" begin
-  nlp =
-    ADNLPModel(x -> dot(x, x) / 2, ones(2), x -> [sum(x .^ 3) - 1], [0.0], [0.0], name = "linquad")
+  nlp = HS10()
   callback(nlp, solver, stats) = begin
     if stats.iter ≥ 3
       set_status!(stats, :user)

test/test_logging.jl

@@ -1,9 +1,5 @@
 function test_logging()
-  nlps = [ADNLPModel(x -> sum(x .^ k), ones(2k), name = "Sum of power $k") for k = 2:4]
-  push!(
-    nlps,
-    ADNLPModel(x -> dot(x, x), ones(2), x -> [sum(x) - 1], [0.0], [0.0], name = "linquad"),
-  )
+  nlp = HS10()
 
   @info "Testing logger"
   log_header([:col_float, :col_int, :col_symbol, :col_string], [Float64, Int, Symbol, String])
@@ -12,8 +8,7 @@ function test_logging()
 
   with_logger(ConsoleLogger()) do
     @info "Testing dummy solver with logger"
-    SolverCore.dummy_solver(nlps[1], max_eval = 20)
-    reset!.(nlps)
+    SolverCore.dummy_solver(nlp, max_eval = 20)
   end
 end
 

test/test_restart.jl

@@ -1,5 +1,5 @@
 @testset "test restart" begin
-  nlp = ADNLPModel(x -> dot(x, x) / 2, ones(2), x -> [sum(x .^ 3) - 1], [0.0], [0.0])
+  nlp = HS10()
   solver = SolverCore.DummySolver(nlp)
   stats = GenericExecutionStats(nlp)
   solve!(solver, nlp, stats, verbose = false)
@@ -10,7 +10,7 @@
   solve!(solver, nlp, stats, verbose = false)
   @test stats.status == :first_order
   # Try with a new problem of the same size
-  nlp = ADNLPModel(x -> dot(x, x) / 2, ones(2), x -> [sum(x .^ 3)], [0.0], [0.0])
+  nlp = HS10()
   reset!(solver, nlp)
   solve!(solver, nlp, stats, verbose = false)
   @test stats.status == :first_order

test/test_stats.jl

@@ -1,6 +1,6 @@
 function test_stats()
   show_statuses()
-  nlp = ADNLPModel(x -> dot(x, x), zeros(2))
+  nlp = HS10()
   stats = GenericExecutionStats(nlp)
   set_status!(stats, :first_order)
   set_objective!(stats, 1.0)
@@ -36,7 +36,7 @@ function test_stats()
 
   @testset "Testing inference" begin
     for T in (Float16, Float32, Float64, BigFloat)
-      nlp = ADNLPModel(x -> dot(x, x), ones(T, 2))
+      nlp = BROWNDEN(T)
 
       stats = GenericExecutionStats(nlp)
       set_status!(stats, :first_order)
@@ -46,7 +46,7 @@ function test_stats()
       @test typeof(stats.dual_feas) == T
       @test typeof(stats.primal_feas) == T
 
-      nlp = ADNLPModel(x -> dot(x, x), ones(T, 2), x -> [sum(x) - 1], T[0], T[0])
+      nlp = HS14(T)
 
       stats = GenericExecutionStats(nlp)
       set_status!(stats, :first_order)
@@ -78,7 +78,7 @@ function test_stats()
 
   @testset "Testing Dummy Solver with multi-precision" begin
     for T in (Float16, Float32, Float64, BigFloat)
-      nlp = ADNLPModel(x -> dot(x, x), ones(T, 2))
+      nlp = HS10(T)
       solver = SolverCore.DummySolver(nlp)
 
       stats = with_logger(NullLogger()) do
@@ -92,21 +92,6 @@ function test_stats()
       @test eltype(stats.multipliers_L) == T
       @test eltype(stats.multipliers_U) == T
 
-      nlp = ADNLPModel(x -> dot(x, x), ones(T, 2), x -> [sum(x) - 1], T[0], T[0])
-      solver = SolverCore.DummySolver(nlp)
-      stats = GenericExecutionStats(nlp)
-
-      with_logger(NullLogger()) do
-        solve!(solver, nlp, stats)
-      end
-      @test typeof(stats.objective) == T
-      @test typeof(stats.dual_feas) == T
-      @test typeof(stats.primal_feas) == T
-      @test eltype(stats.solution) == T
-      @test eltype(stats.multipliers) == T
-      @test eltype(stats.multipliers_L) == T
-      @test eltype(stats.multipliers_U) == T
-
       stats = GenericExecutionStats{T, Vector{T}, Vector{T}, Any}()
       reset!(stats, nlp)
       with_logger(NullLogger()) do
@@ -124,7 +109,7 @@ function test_stats()
 
   @testset "Test stats setters" begin
     T = Float64
-    nlp = ADNLPModel(x -> dot(x, x), ones(T, 2), x -> [sum(x) - 1], T[0], T[0])
+    nlp = HS14(T)
     stats = GenericExecutionStats(nlp)
     fields = (
       "status",
@@ -173,7 +158,7 @@ end
 test_stats()
 
 @testset "Test get_status" begin
-  nlp = ADNLPModel(x -> sum(x), ones(2))
+  nlp = BROWNDEN()
   @test get_status(nlp, optimal = true) == :first_order
   @test get_status(nlp, small_residual = true) == :small_residual
   @test get_status(nlp, infeasible = true) == :infeasible
@@ -191,7 +176,7 @@ test_stats()
 end
 
 @testset "Test get_status for NLS" begin
-  nlp = ADNLSModel(x -> x, ones(2), 2)
+  nlp = BNDROSENBROCK()
   @test get_status(nlp, optimal = true) == :first_order
   @test get_status(nlp, small_residual = true) == :small_residual
   @test get_status(nlp, infeasible = true) == :infeasible