using FrankWolfe
using LinearAlgebra
using LaTeXStrings
using Plots

FrankWolfe for scaled, shifted $\ell^1$ and $\ell^{\infty}$ norm balls

In this example, we run the vanilla FrankWolfe algorithm on a scaled and shifted $\ell^1$ and $\ell^{\infty}$ norm ball, using the DiamondLMO and BoxLMO LMOs. We shift both onto the point $(1,0)$ and then scale them by a factor of $2$ along the x-axis. We project the point $(2,1)$ onto the polytopes.

n = 2

k = 1000

xp = [2.0, 1.0]

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

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

lower = [-1.0, -1.0]
upper = [3.0, 1.0]

l1 = FrankWolfe.DiamondLMO(lower, upper)

linf = FrankWolfe.BoxLMO(lower, upper)

x1 = FrankWolfe.compute_extreme_point(l1, zeros(n))
gradient = collect(x1)

x_l1, v_1, primal_1, dual_gap_1, status_1, trajectory_1 = FrankWolfe.frank_wolfe(
    f,
    grad!,
    l1,
    collect(copy(x1)),
    max_iteration=k,
    line_search=FrankWolfe.Shortstep(2.0),
    print_iter=50,
    memory_mode=FrankWolfe.InplaceEmphasis(),
    verbose=true,
    trajectory=true,
);

println("\nFinal solution: ", x_l1)

x2 = FrankWolfe.compute_extreme_point(linf, zeros(n))
gradient = collect(x2)

x_linf, v_2, primal_2, dual_gap_2, status_2, trajectory_2 = FrankWolfe.frank_wolfe(
    f,
    grad!,
    linf,
    collect(copy(x2)),
    max_iteration=k,
    line_search=FrankWolfe.Shortstep(2.0),
    print_iter=50,
    memory_mode=FrankWolfe.InplaceEmphasis(),
    verbose=true,
    trajectory=true,
);

println("\nFinal solution: ", x_linf)

Vanilla Frank-Wolfe Algorithm.
MEMORY_MODE: FrankWolfe.InplaceEmphasis() STEPSIZE: Shortstep EPSILON: 1.0e-7 MAXITERATION: 1000 TYPE: Float64
MOMENTUM: nothing GRADIENT_TYPE: Vector{Float64}
LMO: FrankWolfe.DiamondLMO{Float64, 1, Vector{Float64}, Vector{Float64}}

-------------------------------------------------------------------------------------------------
  Type     Iteration         Primal           Dual       Dual Gap           Time         It/sec
-------------------------------------------------------------------------------------------------
     I             1   2.000000e+00  -6.000000e+00   8.000000e+00   0.000000e+00            Inf
    FW            50   2.198243e-01   1.859119e-01   3.391239e-02   9.177286e-02   5.448234e+02
    FW           100   2.104540e-01   1.927834e-01   1.767061e-02   9.200651e-02   1.086880e+03
    FW           150   2.071345e-01   1.951277e-01   1.200679e-02   9.225118e-02   1.625996e+03
    FW           200   2.054240e-01   1.963167e-01   9.107240e-03   9.247435e-02   2.162762e+03
    FW           250   2.043783e-01   1.970372e-01   7.341168e-03   9.269830e-02   2.696921e+03
    FW           300   2.036722e-01   1.975209e-01   6.151268e-03   9.292232e-02   3.228503e+03
    FW           350   2.031630e-01   1.978684e-01   5.294582e-03   9.316716e-02   3.756688e+03
    FW           400   2.027782e-01   1.981301e-01   4.648079e-03   9.339447e-02   4.282909e+03
    FW           450   2.024772e-01   1.983344e-01   4.142727e-03   9.361710e-02   4.806814e+03
    FW           500   2.022352e-01   1.984984e-01   3.736776e-03   9.383675e-02   5.328403e+03
    FW           550   2.020364e-01   1.986329e-01   3.403479e-03   9.405570e-02   5.847599e+03
    FW           600   2.018701e-01   1.987452e-01   3.124906e-03   9.428489e-02   6.363692e+03
    FW           650   2.017290e-01   1.988404e-01   2.888583e-03   9.450795e-02   6.877728e+03
    FW           700   2.016078e-01   1.989222e-01   2.685564e-03   9.472712e-02   7.389647e+03
    FW           750   2.015024e-01   1.989932e-01   2.509264e-03   9.494640e-02   7.899193e+03
    FW           800   2.014101e-01   1.990554e-01   2.354727e-03   9.517323e-02   8.405725e+03
    FW           850   2.013284e-01   1.991103e-01   2.218154e-03   9.540161e-02   8.909703e+03
    FW           900   2.012558e-01   1.991592e-01   2.096580e-03   9.562167e-02   9.412093e+03
    FW           950   2.011906e-01   1.992030e-01   1.987662e-03   9.583445e-02   9.912928e+03
    FW          1000   2.011319e-01   1.992424e-01   1.889519e-03   9.605464e-02   1.041074e+04
  Last          1001   2.011297e-01   1.992439e-01   1.885794e-03   9.618282e-02   1.040726e+04
-------------------------------------------------------------------------------------------------

Final solution: [1.799813188674937, 0.5986834801090863]

Vanilla Frank-Wolfe Algorithm.
MEMORY_MODE: FrankWolfe.InplaceEmphasis() STEPSIZE: Shortstep EPSILON: 1.0e-7 MAXITERATION: 1000 TYPE: Float64
MOMENTUM: nothing GRADIENT_TYPE: Vector{Float64}
LMO: FrankWolfe.BoxLMO{Float64, 1, Vector{Float64}, Vector{Float64}}

-------------------------------------------------------------------------------------------------
  Type     Iteration         Primal           Dual       Dual Gap           Time         It/sec
-------------------------------------------------------------------------------------------------
     I             1   1.300000e+01  -1.900000e+01   3.200000e+01   0.000000e+00            Inf
    FW            50   1.084340e-02  -7.590380e-02   8.674720e-02   4.946456e-02   1.010825e+03
    FW           100   5.509857e-03  -3.856900e-02   4.407886e-02   4.969423e-02   2.012306e+03
    FW           150   3.695414e-03  -2.586790e-02   2.956331e-02   4.992166e-02   3.004708e+03
    FW           200   2.780453e-03  -1.946317e-02   2.224362e-02   5.015314e-02   3.987786e+03
    FW           250   2.228830e-03  -1.560181e-02   1.783064e-02   5.037821e-02   4.962463e+03
    FW           300   1.859926e-03  -1.301948e-02   1.487941e-02   5.059852e-02   5.929027e+03
    FW           350   1.595838e-03  -1.117087e-02   1.276670e-02   5.082128e-02   6.886879e+03
    FW           400   1.397443e-03  -9.782098e-03   1.117954e-02   5.104642e-02   7.836005e+03
    FW           450   1.242935e-03  -8.700548e-03   9.943483e-03   5.127395e-02   8.776387e+03
    FW           500   1.119201e-03  -7.834409e-03   8.953610e-03   5.149630e-02   9.709436e+03
    FW           550   1.017878e-03  -7.125146e-03   8.143024e-03   5.171507e-02   1.063520e+04
    FW           600   9.333816e-04  -6.533671e-03   7.467053e-03   5.193514e-02   1.155287e+04
    FW           650   8.618413e-04  -6.032889e-03   6.894730e-03   5.216654e-02   1.246009e+04
    FW           700   8.004890e-04  -5.603423e-03   6.403912e-03   5.238973e-02   1.336140e+04
    FW           750   7.472928e-04  -5.231050e-03   5.978342e-03   5.260675e-02   1.425673e+04
    FW           800   7.007275e-04  -4.905093e-03   5.605820e-03   5.282684e-02   1.514382e+04
    FW           850   6.596259e-04  -4.617381e-03   5.277007e-03   5.305663e-02   1.602062e+04
    FW           900   6.230796e-04  -4.361557e-03   4.984637e-03   5.328224e-02   1.689118e+04
    FW           950   5.903710e-04  -4.132597e-03   4.722968e-03   5.349945e-02   1.775719e+04
    FW          1000   5.609256e-04  -3.926479e-03   4.487405e-03   5.371854e-02   1.861555e+04
  Last          1001   5.598088e-04  -3.918661e-03   4.478470e-03   5.383693e-02   1.859318e+04
-------------------------------------------------------------------------------------------------

Final solution: [2.0005598087769556, 0.9763463450796975]

We plot the polytopes alongside the solutions from above:

xcoord1 = [1, 3, 1, -1, 1]
ycoord1 = [-1, 0, 1, 0, -1]

xcoord2 = [3, 3, -1, -1, 3]
ycoord2 = [-1, 1, 1, -1, -1]

plot(
    xcoord1,
    ycoord1,
    title="Visualization of scaled shifted norm balls",
    lw=2,
    label=L"\ell^1 \textrm{ norm}",
)
plot!(xcoord2, ycoord2, lw=2, label=L"\ell^{\infty} \textrm{ norm}")
plot!(
    [x_l1[1]],
    [x_l1[2]],
    seriestype=:scatter,
    lw=5,
    color="blue",
    label=L"\ell^1 \textrm{ solution}",
)
plot!(
    [x_linf[1]],
    [x_linf[2]],
    seriestype=:scatter,
    lw=5,
    color="orange",
    label=L"\ell^{\infty} \textrm{ solution}",
    legend=:bottomleft,
)
Example block output

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