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Example with ADOLC #535

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Example with ADOLC #535

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f265da6
Example with ADOLC.
Dec 16, 2024
88dc0e7
Clean up.
Dec 20, 2024
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72 changes: 72 additions & 0 deletions examples/adolc_example.jl
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using FrankWolfe
using ADOLC
using LinearAlgebra
using Test

## Data setup
n = Int(10000)

xpi = rand(n);
total = sum(xpi);
const xp = xpi ./ total;

f(x) = sum(abs2, x-xp)

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

c = CxxVector(zeros(n))

function grad!(storage, x)
ADOLC.gradient!(c, f, n, x, tape_id, true) # derivate in the direction y
@. storage = c
end

lmo_radius = 2.5
lmo = FrankWolfe.FrankWolfe.ProbabilitySimplexOracle(lmo_radius)

x00 = FrankWolfe.compute_extreme_point(lmo, zeros(n))
gradient = collect(x00)

x_au, _, primal_au, dual_gap_au, _ = FrankWolfe.frank_wolfe(
f,
grad!,
lmo,
collect(copy(x00)),
line_search=FrankWolfe.Adaptive(),
memory_mode=FrankWolfe.InplaceEmphasis(),
verbose=true,
trajectory=false,
);

println("\nHandwritten Gradient")

f(x) = norm(x-xp)^2

function grad!(storage, x)
@. storage = 2 * (x - xp)
return nothing
end

lmo_radius = 2.5
lmo = FrankWolfe.FrankWolfe.ProbabilitySimplexOracle(lmo_radius)

x00 = FrankWolfe.compute_extreme_point(lmo, zeros(n))
gradient = collect(x00)

x, _, primal, dual_gap, _ = FrankWolfe.frank_wolfe(
f,
grad!,
lmo,
collect(copy(x00)),
line_search=FrankWolfe.Adaptive(),
memory_mode=FrankWolfe.InplaceEmphasis(),
verbose=true,
trajectory=false,
);

@testset "Automatic differentiation" begin
@test primal >= primal_au - dual_gap_au
@test primal_au >= primal - dual_gap
end
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