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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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27 changes: 1 addition & 26 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 @@ -449,27 +445,6 @@ MovingHorizonEstimator estimator with a sample time Ts = 10.0 s:
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.

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
constructor to specify the arrival covariance estimation method.
Expand Down
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