Fix objective sensitivity when parameters appear in the objective (#337)

* fix parametric obj

* format

Author: klamike

src/NonLinearProgram/nlp_utilities.jl

@@ -489,9 +489,11 @@ function _compute_sensitivity(model::Model; tol = 1e-6)
     # Dual bounds upper
     ∂s[((num_w+num_cons+num_lower+1):end), :] *= -_sense_multiplier
 
-    # dual wrt parameter
+    grad = _compute_gradient(model)
     primal_idx = [i.value for i in model.cache.primal_vars]
-    df_dx = _compute_gradient(model)[primal_idx]
-    df_dp = df_dx'∂s[1:num_vars, :]
+    params_idx = [i.value for i in model.cache.params]
+    df_dx = grad[primal_idx]
+    df_dp_direct = grad[params_idx]
+    df_dp = df_dx'∂s[1:num_vars, :] + df_dp_direct'
     return ∂s, df_dp
 end

test/nlp_program.jl

@@ -740,6 +740,41 @@ function test_ObjectiveSensitivity_model2()
     @test isapprox(dp, -1.5; atol = 1e-4)
 end
 
+function test_ObjectiveSensitivity_direct_param_contrib()
+    model = DiffOpt.nonlinear_diff_model(Ipopt.Optimizer)
+    set_silent(model)
+
+    p_val = 3.0
+    @variable(model, p ∈ MOI.Parameter(p_val))
+    @variable(model, x ≥ 1)
+    @objective(model, Min, p^2 * x^2)
+
+    optimize!(model)
+    @assert is_solved_and_feasible(model)
+
+    Δp = 0.1
+    DiffOpt.set_forward_parameter(model, p, Δp)
+    DiffOpt.forward_differentiate!(model)
+
+    df_dp = MOI.get(model, DiffOpt.ForwardObjectiveSensitivity())
+    @test isapprox(df_dp, 2 * p_val * Δp, atol = 1e-8)   # ≈ 0.6 for p=3
+
+    ε = 1e-6
+    df_dp_fd =
+        (
+            begin
+                set_parameter_value(p, p_val + ε)
+                optimize!(model)
+                Δp * objective_value(model)
+            end - begin
+                set_parameter_value(p, p_val - ε)
+                optimize!(model)
+                Δp * objective_value(model)
+            end
+        ) / (2ε)
+
+    @test isapprox(df_dp, df_dp_fd, atol = 1e-4)
+end
 function test_ObjectiveSensitivity_subset_parameters()
     # Model with 10 parameters, differentiate only w.r.t. 3rd and 7th
     model = Model(() -> DiffOpt.diff_optimizer(Ipopt.Optimizer))