Update to latest JuMP syntax (#29)

Project.toml

@@ -9,24 +9,7 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
 [compat]
-Ipopt = "1"
-JuMP = "1.2"
-LinearAlgebra = "<0.0.1, 1.6"
-MathOptInterface = "1.4"
-PowerModels = "0.19"
-Random = "<0.0.1, 1.6"
-Symbolics = "4, 5"
+MathOptInterface = "1.25"
 SparseArrays = "<0.0.1, 1.6"
-Test = "<0.0.1, 1.6"
+Symbolics = "4, 5"
 julia = "1.6"
-
-[extras]
-Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
-JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-PowerModels = "c36e90e8-916a-50a6-bd94-075b64ef4655"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[targets]
-test = ["Ipopt", "JuMP", "LinearAlgebra", "PowerModels", "Random", "Test"]

README.md

@@ -21,8 +21,13 @@ import Ipopt
 import MathOptSymbolicAD
 model = Model(Ipopt.Optimizer)
 @variable(model, x[1:2])
-@NLobjective(model, Min, (1 - x[1])^2 + 100 * (x[2] - x[1]^2)^2)
-optimize!(model; _differentiation_backend = MathOptSymbolicAD.DefaultBackend())
+@objective(model, Min, (1 - x[1])^2 + 100 * (x[2] - x[1]^2)^2)
+set_attribute(
+    model,
+    MOI.AutomaticDifferentiationBackend(),
+    MathOptSymbolicAD.DefaultBackend(),
+)
+optimize!(model)
 ```
 
 ## Background
@@ -52,7 +57,7 @@ large with few unique constraints. For example, a model like:
 ```julia
 model = Model()
 @variable(model, 0 <= x[1:10_000] <= 1)
-@NLconstraint(model, [i=1:10_000], sin(x[i]) <= 1)
+@constraint(model, [i=1:10_000], sin(x[i]) <= 1)
 @objective(model, Max, sum(x))
 ```
 is ideal, because although the Jacobian matrix has 10,000 rows, we can compute

test/Project.toml

@@ -0,0 +1,21 @@
+[deps]
+Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
+JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MathOptSymbolicAD = "309f4015-3481-4d63-a8f9-aeb13adfe8eb"
+PowerModels = "c36e90e8-916a-50a6-bd94-075b64ef4655"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[compat]
+Ipopt = "1"
+JuMP = "1.2"
+LinearAlgebra = "<0.0.1, 1.6"
+PowerModels = "0.19, 0.20"
+Random = "<0.0.1, 1.6"
+SparseArrays = "<0.0.1, 1.6"
+SpecialFunctions = "2"
+Test = "<0.0.1, 1.6"
+

test/runtests.jl

@@ -9,10 +9,11 @@ using JuMP
 
 import Ipopt
 import LinearAlgebra
+import MathOptSymbolicAD
 import PowerModels
 import Random
 import SparseArrays
-import MathOptSymbolicAD
+import SpecialFunctions
 import Test
 
 function runtests()
@@ -270,6 +271,44 @@ function test_optimizer_clnlbeam(; N::Int = 10)
     return
 end
 
+function test_optimizer_clnlbeam_expr(; N::Int = 10)
+    h = 1 / N
+    model = Model(Ipopt.Optimizer)
+    @variable(model, -1 <= t[1:(N+1)] <= 1)
+    @variable(model, -0.05 <= x[1:(N+1)] <= 0.05)
+    @variable(model, u[1:(N+1)])
+    @objective(
+        model,
+        Min,
+        sum(
+            h / 2 * (u[i+1]^2 + u[i]^2) +
+            350 * h / 2 * (cos(t[i+1]) + cos(t[i])) for i in 1:N
+        ),
+    )
+    for i in 1:N
+        @constraint(model, x[i+1] - x[i] == h / 2 * (sin(t[i+1]) + sin(t[i])))
+        @constraint(model, t[i+1] - t[i] == h / 2 * (u[i+1] - u[i]))
+    end
+    set_attribute(
+        model,
+        MOI.AutomaticDifferentiationBackend(),
+        MathOptSymbolicAD.DefaultBackend(),
+    )
+    optimize!(model)
+    Test.@test isapprox(objective_value(model), 350; atol = 1e-6)
+    t_sol = value.(t)
+    u_sol = value.(u)
+    Test.@test isapprox(
+        sum(
+            h / 2 * (u_sol[i+1]^2 + u_sol[i]^2) +
+            350 * h / 2 * (cos(t_sol[i+1]) + cos(t_sol[i])) for i in 1:N
+        ),
+        350.0;
+        atol = 1e-6,
+    )
+    return
+end
+
 function test_optimizer_case5_pjm()
     model = power_model("pglib_opf_case5_pjm.m")
     set_optimizer(model, Ipopt.Optimizer)
@@ -297,6 +336,26 @@ function test_user_defined_functions()
     return
 end
 
+function test_user_defined_functions_expr()
+    model = Model(Ipopt.Optimizer)
+    @variable(model, 0.5 <= x <= 1.0)
+    @operator(model, mysin, 1, a -> sin(a))
+    @operator(model, pow, 2, (a, b) -> a^b)
+    @objective(
+        model,
+        Max,
+        mysin(x) + log(x) + SpecialFunctions.dawson(x) - pow(x, 2),
+    )
+    set_attribute(
+        model,
+        MOI.AutomaticDifferentiationBackend(),
+        MathOptSymbolicAD.DefaultBackend(),
+    )
+    optimize!(model)
+    Test.@test termination_status(model) == LOCALLY_SOLVED
+    return
+end
+
 function test_nested_subexpressions()
     model = Model(Ipopt.Optimizer)
     @variable(model, 0.5 <= x <= 1.0)
@@ -312,6 +371,23 @@ function test_nested_subexpressions()
     return
 end
 
+function test_nested_subexpressions_expr()
+    model = Model(Ipopt.Optimizer)
+    @variable(model, 0.5 <= x <= 1.0)
+    @expression(model, my_expr1, x - 1)
+    @expression(model, my_expr2, my_expr1^2)
+    @objective(model, Min, my_expr2)
+    set_attribute(
+        model,
+        MOI.AutomaticDifferentiationBackend(),
+        MathOptSymbolicAD.DefaultBackend(),
+    )
+    optimize!(model)
+    Test.@test termination_status(model) == LOCALLY_SOLVED
+    Test.@test ≈(value(x), 1.0; atol = 1e-3)
+    return
+end
+
 function test_constant_subexpressions()
     model = Model(Ipopt.Optimizer)
     @variable(model, 0.5 <= x <= 1.0)
@@ -327,6 +403,23 @@ function test_constant_subexpressions()
     return
 end
 
+function test_constant_subexpressions_expr()
+    model = Model(Ipopt.Optimizer)
+    @variable(model, 0.5 <= x <= 1.0)
+    @expression(model, my_expr1, 1.0)
+    @expression(model, my_expr2, x)
+    @objective(model, Min, (my_expr2 - my_expr1)^2)
+    set_attribute(
+        model,
+        MOI.AutomaticDifferentiationBackend(),
+        MathOptSymbolicAD.DefaultBackend(),
+    )
+    optimize!(model)
+    Test.@test termination_status(model) == LOCALLY_SOLVED
+    Test.@test ≈(value(x), 1.0; atol = 1e-3)
+    return
+end
+
 end  # module
 
 RunTests.runtests()