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Add OPF benchmark #2739

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Add OPF benchmark #2739

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45abf13
Add OPF benchmark
blegat Apr 26, 2025
ce48631
Add bench
blegat Apr 27, 2025
ae37715
Add odow's bench
blegat Apr 28, 2025
48f4d53
Add BenchmarkTools
blegat Apr 28, 2025
39222b5
Remove opf.jl
blegat May 5, 2025
a9fc510
Rename
blegat May 5, 2025
87dd43e
Remove Revise
blegat May 5, 2025
70a961e
Add compat bounds
blegat May 5, 2025
70018e5
Fix format
blegat May 8, 2025
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11 changes: 11 additions & 0 deletions perf/opf/Project.toml
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[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"
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These need compat


[compat]
PGLib = "0.2"
PowerModels = "0.21"
34 changes: 34 additions & 0 deletions perf/opf/opf.jl
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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
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