Scale dynamics constraints as residuals for optimal control - #4841
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Formulate dynamics as (∂x - tₛ*f(x)) / scale == 0 instead of scaling each side independently. This prevents degenerate tₛ → 0 solutions and improves Ipopt convergence for problems with multi-scale state variables. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Split out of #4394.
Checklist
contributor guidelines, in particular the SciML Style Guide and
COLPRAC.
Additional context
The dynamics constraints are currently emitted as
∂x ~ tₛ*f(x), scaling each sideindependently. For problems with multi-scale state variables this admits degenerate
tₛ → 0solutions and converges poorly in Ipopt. This reformulates them as a singlescaled residual:
The scale defaults to
getnominal(dvs[i]), so with no nominal value metadata and nouser-provided scales the formulation reduces exactly to the original. A
scaleskwarg on the problem constructors lets the user override per variable.
This builds on the nominal value metadata from #4425 —
process_DynamicOptProblemnow returns the resolved
scalesalongsidepmapso the backends can apply them inadd_equational_constraints!.Draft pending a decision on one point: the observed-bounds split (#4840) lifts
observed bounds into auxiliary variables with an equality constraint that is
currently unscaled. If both land, they should probably agree on a single scaling
convention rather than one using nominal values and the other not.