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 ScaledBoundL1NormBall and ScaledBoundLInfNormBall 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

@. storage = 2 * (x - xp)
return nothing
end

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

l1 = FrankWolfe.ScaledBoundL1NormBall(lower, upper)

linf = FrankWolfe.ScaledBoundLInfNormBall(lower, upper)

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

x_l1, v_1, primal_1, dual_gap_1, trajectory_1 = FrankWolfe.frank_wolfe(
f,
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))

x_linf, v_2, primal_2, dual_gap_2, trajectory_2 = FrankWolfe.frank_wolfe(
f,
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
LMO: FrankWolfe.ScaledBoundL1NormBall{Float64, 1, Vector{Float64}, Vector{Float64}}
[ Info: In memory_mode memory iterates are written back into x0!

-------------------------------------------------------------------------------------------------
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   5.737830e-02   8.714096e+02
FW           100   2.104540e-01   1.927834e-01   1.767061e-02   5.771176e-02   1.732749e+03
FW           150   2.071345e-01   1.951277e-01   1.200679e-02   5.798052e-02   2.587076e+03
FW           200   2.054240e-01   1.963167e-01   9.107240e-03   5.828354e-02   3.431500e+03
FW           250   2.043783e-01   1.970372e-01   7.341168e-03   5.856923e-02   4.268453e+03
FW           300   2.036722e-01   1.975209e-01   6.151268e-03   5.882865e-02   5.099556e+03
FW           350   2.031630e-01   1.978684e-01   5.294582e-03   5.914581e-02   5.917579e+03
FW           400   2.027782e-01   1.981301e-01   4.648079e-03   5.943606e-02   6.729921e+03
FW           450   2.024772e-01   1.983344e-01   4.142727e-03   5.973451e-02   7.533334e+03
FW           500   2.022352e-01   1.984984e-01   3.736776e-03   6.001678e-02   8.331004e+03
FW           550   2.020364e-01   1.986329e-01   3.403479e-03   6.034213e-02   9.114693e+03
FW           600   2.018701e-01   1.987452e-01   3.124906e-03   6.066946e-02   9.889654e+03
FW           650   2.017290e-01   1.988404e-01   2.888583e-03   6.093587e-02   1.066695e+04
FW           700   2.016078e-01   1.989222e-01   2.685564e-03   6.117855e-02   1.144192e+04
FW           750   2.015024e-01   1.989932e-01   2.509264e-03   6.143077e-02   1.220887e+04
FW           800   2.014101e-01   1.990554e-01   2.354727e-03   6.167075e-02   1.297211e+04
FW           850   2.013284e-01   1.991103e-01   2.218154e-03   6.190835e-02   1.372997e+04
FW           900   2.012558e-01   1.991592e-01   2.096580e-03   6.216169e-02   1.447837e+04
FW           950   2.011906e-01   1.992030e-01   1.987662e-03   6.244553e-02   1.521326e+04
FW          1000   2.011319e-01   1.992424e-01   1.889519e-03   6.269379e-02   1.595054e+04
Last          1001   2.011297e-01   1.992439e-01   1.885794e-03   6.285185e-02   1.592634e+04
-------------------------------------------------------------------------------------------------

Final solution: [1.7998131886749367, 0.5986834801090858]

Vanilla Frank-Wolfe Algorithm.
MEMORY_MODE: FrankWolfe.InplaceEmphasis() STEPSIZE: Shortstep EPSILON: 1.0e-7 MAXITERATION: 1000 TYPE: Float64
LMO: FrankWolfe.ScaledBoundLInfNormBall{Float64, 1, Vector{Float64}, Vector{Float64}}
[ Info: In memory_mode memory iterates are written back into x0!

-------------------------------------------------------------------------------------------------
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   4.830346e-02   1.035123e+03
FW           100   5.509857e-03  -3.856900e-02   4.407886e-02   4.856582e-02   2.059061e+03
FW           150   3.695414e-03  -2.586790e-02   2.956331e-02   4.881653e-02   3.072730e+03
FW           200   2.780453e-03  -1.946317e-02   2.224362e-02   4.907241e-02   4.075610e+03
FW           250   2.228830e-03  -1.560181e-02   1.783064e-02   4.932116e-02   5.068819e+03
FW           300   1.859926e-03  -1.301948e-02   1.487941e-02   4.956914e-02   6.052152e+03
FW           350   1.595838e-03  -1.117087e-02   1.276670e-02   4.985821e-02   7.019907e+03
FW           400   1.397443e-03  -9.782098e-03   1.117954e-02   5.012110e-02   7.980670e+03
FW           450   1.242935e-03  -8.700548e-03   9.943483e-03   5.037610e-02   8.932807e+03
FW           500   1.119201e-03  -7.834409e-03   8.953610e-03   5.064309e-02   9.873016e+03
FW           550   1.017878e-03  -7.125146e-03   8.143024e-03   5.093013e-02   1.079911e+04
FW           600   9.333816e-04  -6.533671e-03   7.467053e-03   5.118773e-02   1.172156e+04
FW           650   8.618413e-04  -6.032889e-03   6.894730e-03   5.143182e-02   1.263809e+04
FW           700   8.004890e-04  -5.603423e-03   6.403912e-03   5.167111e-02   1.354722e+04
FW           750   7.472928e-04  -5.231050e-03   5.978342e-03   5.194778e-02   1.443757e+04
FW           800   7.007275e-04  -4.905093e-03   5.605820e-03   5.223521e-02   1.531534e+04
FW           850   6.596259e-04  -4.617381e-03   5.277007e-03   5.248860e-02   1.619399e+04
FW           900   6.230796e-04  -4.361557e-03   4.984637e-03   5.273217e-02   1.706738e+04
FW           950   5.903710e-04  -4.132597e-03   4.722968e-03   5.297696e-02   1.793232e+04
FW          1000   5.609256e-04  -3.926479e-03   4.487405e-03   5.322377e-02   1.878860e+04
Last          1001   5.598088e-04  -3.918661e-03   4.478470e-03   5.338272e-02   1.875139e+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,
)