Add OPF benchmark

perf/opf/Project.toml

@@ -0,0 +1,11 @@
+[deps]
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
+JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
+MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
+PGLib = "07a8691f-3d11-4330-951b-3c50f98338be"
+PowerModels = "c36e90e8-916a-50a6-bd94-075b64ef4655"
+
+[compat]
+PGLib = "0.2"
+PowerModels = "0.21"

perf/opf/opf.jl

@@ -0,0 +1,40 @@
+# Copyright (c) 2017: Miles Lubin and contributors
+# Copyright (c) 2017: Google Inc.
+#
+# Use of this source code is governed by an MIT-style license that can be found
+# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
+
+import Ipopt
+import JuMP
+import MathOptInterface as MOI
+import PGLib
+import PowerModels
+
+model = JuMP.direct_model(Ipopt.Optimizer())
+pm = PowerModels.instantiate_model(
+    PGLib.pglib("pglib_opf_case10000_goc"),
+    PowerModels.ACPPowerModel,
+    PowerModels.build_opf;
+    jump_model = model,
+);
+
+ipopt = JuMP.backend(model)
+x = MOI.get(ipopt, MOI.ListOfVariableIndices())
+m, n = length(ipopt.nlp_model.constraints), length(x)
+
+evaluator = MOI.Nonlinear.Evaluator(
+    ipopt.nlp_model,
+    MOI.Nonlinear.SparseReverseMode(),
+    x,
+)
+MOI.initialize(evaluator, [:Grad, :Jac, :Hess])
+
+H_struct = MOI.hessian_lagrangian_structure(evaluator)
+H = zeros(length(H_struct))
+μ = rand(m)
+σ = 0.0
+v = rand(n)
+@time MOI.eval_hessian_lagrangian(evaluator, H, v, σ, μ)
+
+using BenchmarkTools
+@benchmark MOI.eval_hessian_lagrangian($evaluator, $H, $v, $σ, $μ) seconds = 100