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Example with ADOLC #535

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Example with ADOLC #535

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dhendryc
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Works well if n is small. But even for n=50, the gradient computation seems to be instable.
For n=500, it errors with

Vanilla Frank-Wolfe Algorithm.
MEMORY_MODE: FrankWolfe.InplaceEmphasis() STEPSIZE: Adaptive{Float64, Int64} EPSILON: 1.0e-7 MAXITERATION: 10000 TYPE: Float64
MOMENTUM: nothing GRADIENTTYPE: Nothing
LMO: FrankWolfe.ProbabilitySimplexOracle{Float64}
[ Info: In memory_mode memory iterates are written back into x0!
ADOL-C Warning: Branch switch detected in comparison (operator le_zero).
Forward sweep aborted! Retaping recommended!

-------------------------------------------------------------------------------------------------
  Type     Iteration         Primal           Dual       Dual Gap           Time         It/sec
-------------------------------------------------------------------------------------------------
     I             1   6.248921e+00   6.248921e+00   0.000000e+00   0.000000e+00            Inf
ADOL-C Warning: Branch switch detected in comparison (operator le_zero).
Forward sweep aborted! Retaping recommended!
  Last             1   6.248921e+00   6.248921e+00   0.000000e+00   9.962060e-01   1.003808e+00
-------------------------------------------------------------------------------------------------

@dhendryc dhendryc mentioned this pull request Dec 19, 2024
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@dhendryc dhendryc linked an issue Dec 19, 2024 that may be closed by this pull request
ADOLC.jl example #465
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pokutta commented Dec 20, 2024

we need to discuss with andrea et al

matbesancon
matbesancon reviewed Jan 8, 2025
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examples/adolc_example.jl
println("Automatic differentiation")

const tape_id = 1
ADOLC.derivative(f, zeros(n), :jac, tape_id=tape_id)
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maybe an explanation on the line here, since this call is not returning any variable

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ADOLC.jl example
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