revive NLP+quadratic tests

jump-dev · Apr 15, 2018 · 7bc291d · 7bc291d
1 parent 9c091fc
commit 7bc291d
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Showing 2 changed files with 44 additions and 26 deletions.
diff --git a/test/nlp_solver.jl b/test/nlp_solver.jl
@@ -441,4 +441,48 @@ new_optimizer() = IpoptOptimizer(print_level=0)
         test_result()
         @test JuMP.objectivevalue(m) ≈ 5.8446115 atol=1e-6
     end
+
+    @testset "Quadratic inequality constraints, linear objective" begin
+        m = Model(optimizer=new_optimizer())
+        @variable(m, -2 <= x <= 2)
+        @variable(m, -2 <= y <= 2)
+        @objective(m, Min, x - y)
+        @constraint(m, x + x^2 + x*y + y^2 <= 1)
+        JuMP.optimize(m)
+
+        @test JuMP.hasresultvalues(m)
+        @test JuMP.terminationstatus(m) == MOI.Success
+        @test JuMP.primalstatus(m) == MOI.FeasiblePoint
+        @test JuMP.objectivevalue(m) ≈ -1-4/sqrt(3) atol=1e-6
+        @test JuMP.resultvalue(x) + JuMP.resultvalue(y) ≈ -1/3 atol=1e-3
+    end
+
+    @testset "Quadratic inequality constraints, NL objective" begin
+        m = Model(optimizer=new_optimizer())
+        @variable(m, -2 <= x <= 2)
+        @variable(m, -2 <= y <= 2)
+        @NLobjective(m, Min, x - y)
+        @constraint(m, x + x^2 + x*y + y^2 <= 1)
+        JuMP.optimize(m)
+
+        @test JuMP.hasresultvalues(m)
+        @test JuMP.terminationstatus(m) == MOI.Success
+        @test JuMP.primalstatus(m) == MOI.FeasiblePoint
+        @test JuMP.objectivevalue(m) ≈ -1-4/sqrt(3) atol=1e-6
+        @test JuMP.resultvalue(x) + JuMP.resultvalue(y) ≈ -1/3 atol=1e-3
+    end
+
+    @testset "Quadratic equality constraints" begin
+        m = Model(optimizer=new_optimizer())
+        @variable(m, 0 <= x[1:2] <= 1)
+        @constraint(m, x[1]^2 + x[2]^2 == 1/2)
+        @NLobjective(m, Max, x[1] - x[2])
+        JuMP.optimize(m)
+
+        @test JuMP.hasresultvalues(m)
+        @test JuMP.terminationstatus(m) == MOI.Success
+        @test JuMP.primalstatus(m) == MOI.FeasiblePoint
+        @test JuMP.objectivevalue(m) ≈ sqrt(1/2) atol=1e-6
+        @test JuMP.resultvalue.(x) ≈ [sqrt(1/2), 0] atol=1e-6
+    end
 end
diff --git a/test/old/nonlinear.jl b/test/old/nonlinear.jl
@@ -116,21 +116,6 @@ end
     end
 
 
-    @testset "Quad con solve through NL pathway" for nlp_solver in convex_nlp_solvers
-        # Solve a problem with linear objective with quadratic
-        # constraints, but force it to use the nonlinear code.
-        m = Model(solver=nlp_solver)
-        @variable(m, -2 <= x <= 2)
-        @variable(m, -2 <= y <= 2)
-        @NLobjective(m, Min, x - y)
-        @constraint(m, x + x^2 + x*y + y^2 <= 1)
-        status = solve(m)
-
-        @test status == :Optimal
-        @test isapprox(getobjectivevalue(m), -1-4/sqrt(3), atol=1e-6)
-        @test isapprox(getvalue(x) + getvalue(y), -1/3, atol=1e-3)
-    end
-
     @testset "Resolve with parameter with $nlp_solver (simplify = $simplify)" for nlp_solver in convex_nlp_solvers, simplify in [true,false]
         m = Model(solver=nlp_solver, simplify_nonlinear_expressions=simplify)
         @variable(m, z)
@@ -146,17 +131,6 @@ end
         @test isapprox(getvalue(z), 5.0, atol=1e-3)
     end
 
-    @testset "Quadratic equality constraints with $nlp_solver" for nlp_solver in nlp_solvers
-        m = Model(solver=nlp_solver)
-        @variable(m, 0 <= x[1:2] <= 1)
-        @constraint(m, x[1]^2 + x[2]^2 == 1/2)
-        @NLobjective(m, Max, x[1] - x[2])
-        status = solve(m)
-
-        @test status == :Optimal
-        @test isapprox(getvalue(x), [sqrt(1/2), 0], atol=1e-6)
-    end
-
     if ipt
         @testset "Passing starting solutions through QP pathway with Ipopt" begin
             # https://discourse.julialang.org/t/create-quadratic-objective-will-objective-and-nlobjective-lead-to-different-solution-using-ipopt/1666