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Merged
franckgaga merged 9 commits into from
Mar 4, 2026
Merged

bench: including OrthogonalCollocation and various maintenance #329

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a2a0631
doc: minor addition
franckgaga Mar 4, 2026
78e5f5f
added: precompile `OrthogonalCollocation`
franckgaga Mar 4, 2026
827e04a
bench: `OrthogonalCollocation` unit tests
franckgaga Mar 4, 2026
b11d624
bench: increase tolerance in MPC unit tests
franckgaga Mar 4, 2026
68825c5
bench: MadNLP with `MultipleShooting` and Hessian
franckgaga Mar 4, 2026
8eefb3c
bench: re-enabling `UnoSolver.jl`
franckgaga Mar 4, 2026
28139ef
bench: `OrthogonalCollocation` on pendulum
franckgaga Mar 4, 2026
f1a9372
bench: debug last commit
franckgaga Mar 4, 2026
532110e
bench: MHE with `MadNLP` with Hessians
franckgaga Mar 4, 2026
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42 changes: 22 additions & 20 deletions benchmark/2_bench_state_estim.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -343,50 +343,52 @@ JuMP.set_attribute(mhe_pendulum_ipopt_predh.optim, "tol", 1e-7)

optim = JuMP.Model(MadNLP.Optimizer, add_bridges=false)
direct = true
mhe_pendulum_madnlp_curr = MovingHorizonEstimator(
model; He, σQ, σR, nint_u, σQint_u, optim, direct
hessian = true
mhe_pendulum_madnlp_currh = MovingHorizonEstimator(
model; He, σQ, σR, nint_u, σQint_u, optim, direct, hessian
)
mhe_pendulum_madnlp_curr = setconstraint!(mhe_pendulum_madnlp_curr; v̂min, v̂max)
JuMP.unset_time_limit_sec(mhe_pendulum_madnlp_curr.optim)
JuMP.set_attribute(mhe_pendulum_madnlp_curr.optim, "tol", 1e-7)
mhe_pendulum_madnlp_currh = setconstraint!(mhe_pendulum_madnlp_currh; v̂min, v̂max)
JuMP.unset_time_limit_sec(mhe_pendulum_madnlp_currh.optim)
JuMP.set_attribute(mhe_pendulum_madnlp_currh.optim, "tol", 1e-7)

optim = JuMP.Model(MadNLP.Optimizer, add_bridges=false)
direct = false
mhe_pendulum_madnlp_pred = MovingHorizonEstimator(
model; He, σQ, σR, nint_u, σQint_u, optim, direct
hessian = true
mhe_pendulum_madnlp_predh = MovingHorizonEstimator(
model; He, σQ, σR, nint_u, σQint_u, optim, direct, hessian
)
mhe_pendulum_madnlp_pred = setconstraint!(mhe_pendulum_madnlp_pred; v̂min, v̂max)
JuMP.unset_time_limit_sec(mhe_pendulum_madnlp_pred.optim)
JuMP.set_attribute(mhe_pendulum_madnlp_pred.optim, "tol", 1e-7)
mhe_pendulum_madnlp_pred = setconstraint!(mhe_pendulum_madnlp_predh; v̂min, v̂max)
JuMP.unset_time_limit_sec(mhe_pendulum_madnlp_predh.optim)
JuMP.set_attribute(mhe_pendulum_madnlp_predh.optim, "tol", 1e-7)

samples, evals, seconds = 25, 1, 15*60
CASE_ESTIM["Pendulum"]["MovingHorizonEstimator"]["Ipopt"]["Current form"] =
@benchmarkable(
sim!($mhe_pendulum_ipopt_curr, $N, $u; plant=$plant, x_0=$x_0, x̂_0=$x̂_0, progress=false),
samples=samples, evals=evals, seconds=seconds
samples=samples, evals=evals, seconds=seconds, setup=GC.gc()
)
CASE_ESTIM["Pendulum"]["MovingHorizonEstimator"]["Ipopt"]["Current form (Hessian)"] =
@benchmarkable(
sim!($mhe_pendulum_ipopt_currh, $N, $u; plant=$plant, x_0=$x_0, x̂_0=$x̂_0, progress=false),
samples=samples, evals=evals, seconds=seconds
samples=samples, evals=evals, seconds=seconds, setup=GC.gc()
)
CASE_ESTIM["Pendulum"]["MovingHorizonEstimator"]["Ipopt"]["Prediction form"] =
@benchmarkable(
sim!($mhe_pendulum_ipopt_pred, $N, $u; plant=$plant, x_0=$x_0, x̂_0=$x̂_0, progress=false),
samples=samples, evals=evals, seconds=seconds
samples=samples, evals=evals, seconds=seconds, setup=GC.gc()
)
CASE_ESTIM["Pendulum"]["MovingHorizonEstimator"]["Ipopt"]["Prediction form (Hessian)"] =
@benchmarkable(
sim!($mhe_pendulum_ipopt_predh, $N, $u; plant=$plant, x_0=$x_0, x̂_0=$x̂_0, progress=false),
samples=samples, evals=evals, seconds=seconds
samples=samples, evals=evals, seconds=seconds, setup=GC.gc()
)
CASE_ESTIM["Pendulum"]["MovingHorizonEstimator"]["MadNLP"]["Current form"] =
CASE_ESTIM["Pendulum"]["MovingHorizonEstimator"]["MadNLP"]["Current form (Hessian)"] =
@benchmarkable(
sim!($mhe_pendulum_madnlp_curr, $N, $u; plant=$plant, x_0=$x_0, x̂_0=$x̂_0, progress=false),
samples=samples, evals=evals, seconds=seconds
sim!($mhe_pendulum_madnlp_currh, $N, $u; plant=$plant, x_0=$x_0, x̂_0=$x̂_0, progress=false),
samples=samples, evals=evals, seconds=seconds, setup=GC.gc()
)
CASE_ESTIM["Pendulum"]["MovingHorizonEstimator"]["MadNLP"]["Prediction form"] =
CASE_ESTIM["Pendulum"]["MovingHorizonEstimator"]["MadNLP"]["Prediction form (Hessian)"] =
@benchmarkable(
sim!($mhe_pendulum_madnlp_pred, $N, $u; plant=$plant, x_0=$x_0, x̂_0=$x̂_0, progress=false),
samples=samples, evals=evals, seconds=seconds
sim!($mhe_pendulum_madnlp_predh, $N, $u; plant=$plant, x_0=$x_0, x̂_0=$x̂_0, progress=false),
samples=samples, evals=evals, seconds=seconds, setup=GC.gc()
)
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