Bump to ADNLPModels 0.8

Author: tmigot

test/Project.toml

@@ -9,15 +9,17 @@ Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 QPSReader = "10f199a5-22af-520b-b891-7ce84a7b1bd0"
 SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+SparseConnectivityTracer = "9f842d2f-2579-4b1d-911e-f412cf18a3f5"
 SparseMatricesCOO = "fa32481b-f100-4b48-8dc8-c62f61b13870"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
-ADNLPModels = "0.7"
+ADNLPModels = "0.8"
 LinearOperators = "2"
 NLPModels = "0.21"
 NLPModelsModifiers = "0.7"
 NLPModelsTest = "0.10"
 QPSReader = "0.2"
 SolverCore = "0.3"
+SparseConnectivityTracer = "0.6"
 SparseMatricesCOO = "0.2"

test/runtests.jl

@@ -9,6 +9,7 @@ using ADNLPModels,
   NLPModelsTest,
   QPSReader,
   QuadraticModels,
+  SparseConnectivityTracer,
   SparseMatricesCOO
 
 @testset "test utils" begin

test/test_consistency.jl

@@ -57,7 +57,7 @@ for problem in NLPModelsTest.nlp_problems
     nlp = eval(Symbol(problem))()
     x = nlp.meta.x0
 
-    fx, gx, Hx = obj(nlp, x), grad(nlp, x), Symmetric(hess(nlp, x), :L)
+    fx, gx, Hx = obj(nlp, x), grad(nlp, x), hess(nlp, x)
     nlp_ad = if nlp.meta.ncon > 0
       cx, Ax = cons(nlp, x), jac(nlp, x)
       ADNLPModel(
@@ -68,13 +68,15 @@ for problem in NLPModelsTest.nlp_problems
         Ax,
         nlp.meta.lcon - cx,
         nlp.meta.ucon - cx,
+        detector = SparseConnectivityTracer.TracerLocalSparsityDetector(),
       )
     else
       ADNLPModel(
         s -> fx + dot(gx, s) + dot(s, Hx * s) / 2,
         zeros(nlp.meta.nvar),
         nlp.meta.lvar - x,
         nlp.meta.uvar - x,
+        detector = SparseConnectivityTracer.TracerLocalSparsityDetector(),
       )
     end
     nlp_qm = QuadraticModel(nlp, x)