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changed: hessian=true default to dense ForwardDiff for MovingHorizonEstimation #369

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changed: hessian=true default to dense ForwardDiff for MovingHorizonEstimation #369

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changed: `hessian=true` to dense ForwardDiff for `MovingHorizonEstima…
franckgaga Jun 4, 2026
f21ba7c
Merge branch 'main' into mhe_default_hessain_dense
franckgaga Jun 24, 2026
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35 changes: 1 addition & 34 deletions src/estimator/mhe/construct.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -2,11 +2,7 @@ const DEFAULT_LINMHE_OPTIMIZER = OSQP.MathOptInterfaceOSQP.Optimizer
const DEFAULT_NONLINMHE_OPTIMIZER = optimizer_with_attributes(Ipopt.Optimizer,"sb"=>"yes")
const DEFAULT_NONLINMHE_GRADIENT = AutoForwardDiff()
const DEFAULT_NONLINMHE_JACOBIAN = AutoForwardDiff()
const DEFAULT_NONLINMHE_HESSIAN = AutoSparse(
AutoForwardDiff();
sparsity_detector=TracerSparsityDetector(),
coloring_algorithm=GreedyColoringAlgorithm(ALL_COLORING_ORDERS, postprocessing=true),
)
const DEFAULT_NONLINMHE_HESSIAN = AutoForwardDiff()

@doc raw"""
Include all the data for the constraints of [`MovingHorizonEstimator`](@ref).
Expand Down Expand Up @@ -457,35 +453,6 @@ MovingHorizonEstimator estimator with a sample time Ts = 10.0 s:
[`JuMP` documentation](@extref JuMP Common-mistakes-when-writing-a-user-defined-operator)
for common mistakes when writing these functions. Also, an [`UnscentedKalmanFilter`](@ref)
estimates the arrival covariance by default.

One exception about AD: the selected backend for the Hessian of the Lagrangian function
with `hessian=true` options is sparse:
```julia
AutoSparse(
AutoForwardDiff();
sparsity_detector = TracerSparsityDetector(),
coloring_algorithm = GreedyColoringAlgorithm(
(
NaturalOrder(),
LargestFirst(),
SmallestLast(),
IncidenceDegree(),
DynamicLargestFirst(),
RandomOrder(StableRNG(0), 0)
),
postprocessing = true
)
)
```
that is, it will test many coloring orders at preparation and keep the best. The
argument `covestim` customizes the arrival covariance estimator. The supported types are
[`SteadyKalmanFilter`](@ref), [`KalmanFilter`](@ref), [`UnscentedKalmanFilter`](@ref)
and [`ExtendedKalmanFilter`](@ref). A constant arrival covariance is supported using
the [`SteadyKalmanFilter`](@ref). The constant is fixed by `σP_0`, `σPint_ym_0` and
`σPint_u_0` arguments. If `isnothing(σP_0)`, it is fixed at `covestim.cov.P̂`, which is
the steady-state value ``\mathbf{P̂}(∞)`` for the [`SteadyKalmanFilter`](@ref). For
[`NonLinModel`](@ref), construct a [`SteadyKalmanFilter`](@ref) with an arbitrary
[`LinModel`](@ref), as long as the number of estimated states `nx̂` matches the MHE.

Note that if `Cwt≠Inf`, the attribute `nlp_scaling_max_gradient` of `Ipopt` is set to
`10/Cwt` (if not already set), to scale the small values of ``ε``. Use the second
Expand Down