PreparationMismatchError when using ComponentArrays

[pitx-perf]

Describe the example

Using ComponentArrays for initial guess, lower and upper bounds used to work but now a check happening in the integration with DifferentiationInterface makes it error.

Minimal Reproducible Example 👇

using ComponentArrays
using Optimization, OptimizationMOI, Ipopt, ForwardDiff

# Arrays version: working
x0 = [1.0, 2.0, 3.0]
lb = [0.0, 0.0, 0.0]
ub = [2.0, 3.0, 4.0]

function obj_fun_vector(x, p)
    return (x[1] - 1)^2 + (x[2] - 2)^2 + (x[3] - 3)^2
end

p = [666.0]
optprob = OptimizationFunction(obj_fun_vector, Optimization.AutoForwardDiff())
prob = OptimizationProblem(optprob, x0, p, lb=lb, ub=ub)
opt = OptimizationMOI.MOI.OptimizerWithAttributes(Ipopt.Optimizer)
sol = solve(prob, opt)

# ComponentArrays version: NOT working
x0_ca = ComponentArray(x=1.0, y=2.0, z=3.0)
lb_ca = ComponentArray(x=0.0, y=0.0, z=0.0)
ub_ca = ComponentArray(x=2.0, y=3.0, z=4.0)
p_ca = ComponentArray(p1=666.0)

axes_x = getaxes(x0_ca)

function obj_fun_componentarray(x, p)
    @info x

    # force the ComponentArray to be used - weirdly in some iterations x is a Vector
    # iterations in float: x is a Vector
    # iterations in Dual: x is a ComponentArray
    _x = ComponentArray(x, axes_x)

    return (_x.x - 1)^2 + (_x.y - 2)^2 + (_x.z - 3)^2
end
optprob = OptimizationFunction(obj_fun_componentarray, Optimization.AutoForwardDiff())
prob = OptimizationProblem(optprob, x0_ca, p_ca, lb=lb_ca, ub=ub_ca)
opt = OptimizationMOI.MOI.OptimizerWithAttributes(Ipopt.Optimizer)
sol = solve(prob, opt)

Error & Stacktrace ⚠️

ERROR: PreparationMismatchError (inconsistent types between preparation and execution):
  - f: ✅
  - backend: ✅
  - x: ❌
    - prep: ComponentVector{Float64, Vector{Float64}, Tuple{Axis{(x = 1, y = 2, z = 3)}}}
    - exec: Vector{Float64}
  - contexts: ✅
If you are confident that this check is superfluous, you can disable it by running preparation with the keyword argument `strict=Val(false)` inside DifferentiationInterface.

Not Working Environment (please complete the following information):

• Output of using Pkg; Pkg.status()

  [b0b7db55] ComponentArrays v0.15.29
⌅ [f6369f11] ForwardDiff v0.10.38
  [b6b21f68] Ipopt v1.10.6
  [7f7a1694] Optimization v4.4.0
  [fd9f6733] OptimizationMOI v0.5.5

• Output of using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)

  [47edcb42] ADTypes v1.15.0
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  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.17.0
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  [163ba53b] DiffResults v1.1.0
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  [a0c0ee7d] DifferentiationInterface v0.7.3
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  [fd9f6733] OptimizationMOI v0.5.5
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  [bbf590c4] OrdinaryDiffEqCore v1.26.2
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  [f517fe37] Polyester v0.7.18
  [1d0040c9] PolyesterWeave v0.2.2
  [85a6dd25] PositiveFactorizations v0.2.4
⌅ [aea7be01] PrecompileTools v1.2.1
  [21216c6a] Preferences v1.4.3
  [08abe8d2] PrettyTables v2.4.0
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  [410a4b4d] Tricks v0.1.10
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  [e33a78d0] Hwloc_jll v2.12.1+0
  [1d5cc7b8] IntelOpenMP_jll v2025.2.0+0
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  [81d17ec3] L_BFGS_B_jll v3.0.1+0
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⌅ [656ef2d0] OpenBLAS32_jll v0.3.24+0
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  [ade2ca70] Dates
  [8ba89e20] Distributed
  [f43a241f] Downloads v1.6.0
  [7b1f6079] FileWatching
  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
  [4af54fe1] LazyArtifacts
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• Output of versioninfo()

Julia Version 1.10.8
Commit 4c16ff44be (2025-01-22 10:06 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 16 × Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, skylake)
Threads: 12 default, 0 interactive, 6 GC (on 16 virtual cores)
Environment:
  JULIA_PKG_SERVER = juliahub.com
  JULIA_EDITOR = code
  JULIA_VSCODE_REPL = 1
  JULIA_NUM_THREADS = 12

Working Environment (please complete the following information):

• Output of using Pkg; Pkg.status()

• Output of using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)

• Output of versioninfo()

Additional context

Add any other context about the problem here.

[pitx-perf]

I dug further and I think the issue arises from the fact that the type of x changes depending on the iterations:

• when eltype(x) is Float64, then x is a Vector.
• when eltype(x) is ForwardDiff.Dual, then x is a ComponentVector

that's why I enforce the conversion back to a ComponentVector: _x = ComponentArray(x, axes_x)

Ideally we would want x to always be a ComponentVector, and I suspect that the DifferentiationInterface error would go away.
Where is this conversion from ComponentVector to Vector happening ?

I understand that some solvers can't deal with ComponentArrays #736 (comment) . Is it the case for Ipopt ?

[gdalle]

Where is this conversion from ComponentVector to Vector happening ?

Maybe a simple operation like concatenation?

julia> x = ComponentVector(a=[1, 2])
ComponentVector{Int64}(a = [1, 2])

julia> vcat(x, x)
4-element Vector{Int64}:
 1
 2
 1
 2