normalize constraints test

Author: andrewrosemberg

test/nlp_problems/hs10.jl

@@ -27,11 +27,11 @@ function hs10_oracle()
     # Bounds: 0 ≤ g(x) ≤ +∞
     set = MOI.VectorNonlinearOracle(;
         dimension = 2,                # number of input variables (x1, x2)
-        l = [0.0],                    # lower bound on g(x)
+        l = [-1.0],                    # lower bound on g(x)
         u = [Inf],                    # upper bound on g(x)
         eval_f = (ret, xv) -> begin
             # ret[1] = g(x)
-            ret[1] = -3 * xv[1]^2 + 2 * xv[1] * xv[2] - xv[2]^2 + 1
+            ret[1] = -3 * xv[1]^2 + 2 * xv[1] * xv[2] - xv[2]^2
         end,
         # Jacobian of g(x): ∇g = [∂g/∂x1, ∂g/∂x2]
         # ∂g/∂x1 = -6 x1 + 2 x2

test/nlp_problems/hs14.jl

@@ -29,11 +29,11 @@ function hs14_oracle()
     # g(x) = -x1^2/4 - x2^2 + 1,  0 ≤ g(x) ≤ +∞
     set = MOI.VectorNonlinearOracle(;
         dimension = 2,                # 2 inputs: x1, x2
-        l = [0.0],                    # lower bound on g(x)
+        l = [-1.0],                    # lower bound on g(x)
         u = [Inf],                    # upper bound on g(x)
         eval_f = (ret, xv) -> begin
             # ret[1] = g(x)
-            ret[1] = -0.25 * xv[1]^2 - xv[2]^2 + 1
+            ret[1] = -0.25 * xv[1]^2 - xv[2]^2
         end,
         # Jacobian of g(x): ∇g = [∂g/∂x1, ∂g/∂x2]
         # ∂g/∂x1 = -0.5 x1

test/runtests.jl

@@ -21,6 +21,8 @@ end
 include("test_moi_nlp_model.jl")
 include("test_moi_nls_model.jl")
 
+include("test_moi_nlp_oracle.jl")
+
 include("nlp_consistency.jl")
 include("nls_consistency.jl")
 

test/test_moi_nlp_oracle.jl

@@ -16,6 +16,6 @@
     if m > 0
         ncx_no_oracle = cons(nlp_no_oracle, x)
         ncx_with_oracle = cons(nlp_with_oracle, x)
-        @test isapprox(ncx_no_oracle, ncx_with_oracle; rtol = 1e-1)
+        @test isapprox(ncx_no_oracle, ncx_with_oracle; atol = 1e-8, rtol = 1e-8)
     end
 end