Use test_zero_allocations from NLPModelsTest

Author: amontoison

test/test_allocations.jl

@@ -1,42 +1,33 @@
-function test_only_zeros(table)
-  for (key, vals) in table
-    if !isnan(vals)
-      @test vals == 0
-      vals != 0 && println(key)
-    end
-  end
-end
-
 @testset "allocations" begin
   @testset "allocs QPSData" begin
     for problem in qp_problems_Matrix
       nlp_qps = eval(Symbol(problem * "_QPSData"))()
       allocs = test_allocs_nlpmodels(nlp_qps; linear_api = true, exclude = [jac_op])
-      test_only_zeros(allocs)
+      test_zero_allocations(allocs)
     end
   end
 
   @testset "allocs QP_dense" begin
     for problem in qp_problems_Matrix
       nlp_qm_dense = eval(Symbol(problem * "_QP_dense"))()
       allocs = test_allocs_nlpmodels(nlp_qm_dense; linear_api = true, exclude = [jac_op])
-      test_only_zeros(allocs)
+      test_zero_allocations(allocs)
     end
   end
 
   @testset "allocs COO QPSData" begin
     for problem in qp_problems_COO
       nlp_qps = eval(Symbol(problem * "_QPSData"))()
       allocs = test_allocs_nlpmodels(nlp_qps; linear_api = true, exclude = [jac_op])
-      test_only_zeros(allocs)
+      test_zero_allocations(allocs)
     end
   end
 
   @testset "allocs COO QP" begin
     for problem in qp_problems_COO
       nlp_qm_dense = eval(Symbol(problem * "_QP"))()
       allocs = test_allocs_nlpmodels(nlp_qm_dense; linear_api = true, exclude = [jac_op])
-      test_only_zeros(allocs)
+      test_zero_allocations(allocs)
     end
   end
 
@@ -46,7 +37,7 @@ end
       x = nlp.meta.x0
       nlp_qm = QuadraticModel(nlp, x)
       allocs = test_allocs_nlpmodels(nlp_qm; linear_api = true, exclude = [jac_op])
-      test_only_zeros(allocs)
+      test_zero_allocations(allocs)
     end
   end
 end