More tests

src/nlp_expr.jl

@@ -120,19 +120,18 @@ end
 #
 # However, ifelse is a builtin, so we can't add methods to it.
 
-_to_float(x) = x
-_to_float(x::Real) = convert(Float64, x)
-
 # We need only very generic fallbacks for these, because all other cases are
 # caught with more specific methods.
 for f in (:+, :-, :*, :^, :/, :atan)
     op = Meta.quot(f)
     @eval begin
-        function Base.$(f)(x::AbstractJuMPScalar, y::_Scalar)
-            return NonlinearExpr($op, x, _to_float(y))
+        function Base.$(f)(x::AbstractJuMPScalar, y::_Constant)
+            rhs = convert(Float64, _constant_to_number(y))
+            return NonlinearExpr($op, x, rhs)
         end
-        function Base.$(f)(x::_Scalar, y::AbstractJuMPScalar)
-            return NonlinearExpr($op, _to_float(x), y)
+        function Base.$(f)(x::_Constant, y::AbstractJuMPScalar)
+            lhs = convert(Float64, _constant_to_number(x))
+            return NonlinearExpr($op, lhs, y)
         end
         function Base.$(f)(x::AbstractJuMPScalar, y::AbstractJuMPScalar)
             return NonlinearExpr($op, x, y)

test/nlp_expr.jl

@@ -179,6 +179,7 @@ function test_constraint_name()
     @test name(c) == "c"
     set_name(c, "d")
     @test name(c) == "d"
+    @test startswith(string(c), "d: ")
     return
 end
 
@@ -287,8 +288,7 @@ function test_nlobjective_with_nlexpr()
     y = sin(x)
     @NLobjective(model, Min, y^2)
     nlp = nonlinear_model(model)
-    expr = :(sin($(index(x)))^2)
-    @test nlp.objective == MOI.Nonlinear.parse_expression(nlp, expr)
+    @test isequal_canonical(jump_function(model, nlp.objective), sin(x)^2)
     return
 end
 
@@ -298,9 +298,22 @@ function test_nlconstraint_with_nlexpr()
     y = sin(x)
     @NLconstraint(model, c, y^2 <= 1)
     nlp = nonlinear_model(model)
-    expr = :(sin($(index(x)))^2 - 1.0)
-    @test nlp.constraints[index(c)].expression ==
-          MOI.Nonlinear.parse_expression(nlp, expr)
+    @test isequal_canonical(
+        jump_function(model, nlp.constraints[index(c)].expression),
+        sin(x)^2 - 1,
+    )
+    return
+end
+
+function test_jump_function_nonlinearexpr()
+    model = Model()
+    @variable(model, x)
+    @NLparameter(model, p == 1)
+    @NLexpression(model, expr1, sin(p + x))
+    @NLexpression(model, expr2, sin(expr1))
+    nlp = nonlinear_model(model)
+    @test string(jump_function(model, nlp[index(expr1)])) == "sin(+($p, $x))"
+    @test string(jump_function(model, nlp[index(expr2)])) == "sin($expr1)"
     return
 end