Update to NLPModels 0.14 + Modifiers + Test

Author: abelsiqueira

LICENSE.md

@@ -1,4 +1,4 @@
-Copyright (c) 2018-2019: Dominique Orban
+Copyright (c) 2018-2021: Dominique Orban
 
 QuadraticModels.jl is licensed under the [MPL version 2.0](https://www.mozilla.org/MPL/2.0/).
 

Project.toml

@@ -4,24 +4,26 @@ authors = ["Dominique Orban <[email protected]>"]
 version = "0.2.0"
 
 [deps]
-FastClosures = "9aa1b823-49e4-5ca5-8b0f-3971ec8bab6a"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
+NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [compat]
-FastClosures = "0.2.1, 0.3.0"
 LinearOperators = "0.7.0, 1"
-NLPModels = "0.13"
+NLPModels = "0.14"
+NLPModelsModifiers = "0.1"
 Requires = "0.3, 0.4, 0.5"
 julia = "^1.0.0"
 
 [extras]
+ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
+NLPModelsTest = "7998695d-6960-4d3a-85c4-e1bceb8cd856"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 QPSReader = "10f199a5-22af-520b-b891-7ce84a7b1bd0"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Printf", "Test", "QPSReader"]
+test = ["ADNLPModels", "NLPModelsTest", "Printf", "QPSReader", "Test"]

src/QuadraticModels.jl

@@ -4,10 +4,10 @@ module QuadraticModels
 using LinearAlgebra, SparseArrays
 
 # our packages
-using LinearOperators, NLPModels
+using LinearOperators, NLPModels, NLPModelsModifiers
 
 # auxiliary packages
-using FastClosures, Requires
+using Requires
 
 import NLPModels:
     objgrad, objgrad!, obj, grad, grad!,

src/qpmodel.jl

@@ -184,7 +184,7 @@ end
 
 function SlackModel!(qp :: AbstractQuadraticModel)
   qp.meta.ncon == length(qp.meta.jfix) && return qp
-  
+
   nfix = length(qp.meta.jfix)
   ns = qp.meta.ncon - nfix
   T = eltype(qp.data.c)
@@ -195,11 +195,11 @@ function SlackModel!(qp :: AbstractQuadraticModel)
   append!(qp.data.Avals, (-one(T) for _ = 1 : ns))
   append!(qp.data.c, (zero(T) for _ = 1 : ns))
 
-  qp.meta = NLPModels.slack_meta(qp.meta, name=qp.meta.name)
+  qp.meta = NLPModelsModifiers.slack_meta(qp.meta, name=qp.meta.name)
   return qp
 end
 
-function NLPModels.SlackModel(qp :: AbstractQuadraticModel, name=qp.meta.name * "-slack")
+function NLPModelsModifiers.SlackModel(qp :: AbstractQuadraticModel, name=qp.meta.name * "-slack")
   qp.meta.ncon == length(qp.meta.jfix) && return qp
   nfix = length(qp.meta.jfix)
   ns = qp.meta.ncon - nfix
@@ -214,7 +214,7 @@ function NLPModels.SlackModel(qp :: AbstractQuadraticModel, name=qp.meta.name *
                 [qp.data.Acols; qp.meta.nvar+1:qp.meta.nvar+ns],
                 [qp.data.Avals; .-ones(T, ns)],
                 )
-  meta = NLPModels.slack_meta(qp.meta, name=qp.meta.name)
-  
+  meta = NLPModelsModifiers.slack_meta(qp.meta, name=qp.meta.name)
+
   return QuadraticModel( meta, Counters(), data)
 end

test/runtests.jl

@@ -1,11 +1,8 @@
 # stdlib
-using Printf, SparseArrays, Test
+using LinearAlgebra, Printf, SparseArrays, Test
 
 # our packages
-using LinearAlgebra, LinearOperators, NLPModels, QPSReader, QuadraticModels
-
-nlpmodels_path = joinpath(dirname(pathof(NLPModels)), "..", "test")
-nlpmodels_problems_path = joinpath(nlpmodels_path, "problems")
+using ADNLPModels, LinearOperators, NLPModels, NLPModelsModifiers, NLPModelsTest, QPSReader, QuadraticModels
 
 # Definition of quadratic problems
 qp_problems_Matrix = ["bndqp", "eqconqp"]
@@ -14,7 +11,6 @@ for qp in [qp_problems_Matrix; qp_problems_COO]
   include(joinpath("problems", "$qp.jl"))
 end
 
-include(joinpath(nlpmodels_path, "consistency.jl"))
 include("test_consistency.jl")
 
 function testSM(sm) # test function for a specific problem
@@ -35,7 +31,7 @@ function testSM(sm) # test function for a specific problem
   @test all(sparse(sm.data.Arows, sm.data.Acols, sm.data.Avals, 2, 5) .== A_sm_true)
   @test all(sparse(sm.data.Hrows, sm.data.Hcols, sm.data.Hvals, 5, 5) .== H_sm_true)
 end
-  
+
 
 @testset "SlackModel" begin
   H = [6. 2. 1.
@@ -51,7 +47,7 @@ end
   qp = QuadraticModel(c, H, A=A, lcon=[-3; -4], ucon=[-2.; Inf], lvar=l, uvar=u, c0=0., name="QM1")
   sm = SlackModel(qp)
   testSM(sm)
-  
+
   SlackModel!(qp)
   testSM(qp)
 end

test/test_consistency.jl

@@ -52,24 +52,23 @@ for problem in qp_problems_COO
   @info "  Consistency checks ✓"
 end
 
-for problem in [:brownden, :hs5, :hs6, :hs10, :hs11, :hs14, :lincon]
-  @info "Testing consistency of quadratic approximation of problem $problem"
-  include(joinpath(nlpmodels_problems_path, "$problem.jl"))
-  problem_s = string(problem)
-  nlp = eval(Meta.parse("$(problem)_autodiff"))()
-  x = nlp.meta.x0
+for problem in NLPModelsTest.nlp_problems
+  @testset "Testing consistency of quadratic approximation of problem $problem" begin
+    nlp = eval(Symbol(problem))()
+    x = nlp.meta.x0
 
-  fx, gx, Hx = obj(nlp, x), grad(nlp, x), Symmetric(hess(nlp, x), :L)
-  nlp_ad = if nlp.meta.ncon > 0
-    cx, Ax = cons(nlp, x), jac(nlp, x)
-    ADNLPModel(s -> fx + dot(gx, s) + dot(s, Hx * s) / 2, zeros(nlp.meta.nvar),
-               nlp.meta.lvar - x, nlp.meta.uvar - x,
-               s -> Ax * s, nlp.meta.lcon - cx, nlp.meta.ucon - cx)
-  else
-    ADNLPModel(s -> fx + dot(gx, s) + dot(s, Hx * s) / 2, zeros(nlp.meta.nvar),
-               nlp.meta.lvar - x, nlp.meta.uvar - x)
+    fx, gx, Hx = obj(nlp, x), grad(nlp, x), Symmetric(hess(nlp, x), :L)
+    nlp_ad = if nlp.meta.ncon > 0
+      cx, Ax = cons(nlp, x), jac(nlp, x)
+      ADNLPModel(s -> fx + dot(gx, s) + dot(s, Hx * s) / 2, zeros(nlp.meta.nvar),
+                nlp.meta.lvar - x, nlp.meta.uvar - x,
+                s -> Ax * s, nlp.meta.lcon - cx, nlp.meta.ucon - cx)
+    else
+      ADNLPModel(s -> fx + dot(gx, s) + dot(s, Hx * s) / 2, zeros(nlp.meta.nvar),
+                nlp.meta.lvar - x, nlp.meta.uvar - x)
+    end
+    nlp_qm = QuadraticModel(nlp, x)
+    nlps = [nlp_ad, nlp_qm]
+    consistent_nlps(nlps)
   end
-  nlp_qm = QuadraticModel(nlp, x)
-  nlps = [nlp_ad, nlp_qm]
-  consistent_nlps(nlps)
 end