# FrankWolfe.jl

This package is a toolbox for Frank-Wolfe and conditional gradients algorithms.

## Overview

Frank-Wolfe algorithms were designed to solve optimization problems of the form $\min_{x ∈ C} f(x)$, where f is a differentiable convex function and C is a convex and compact set. They are especially useful when we know how to optimize a linear function over C in an efficient way.

A paper presenting the package with mathematical explanations and numerous examples can be found here:

## Installation

The most recent release is available via the julia package manager, e.g., with

using Pkg
Pkg.add("FrankWolfe")

or the master branch:

Pkg.add(url="https://github.com/ZIB-IOL/FrankWolfe.jl", rev="master")

## Getting started

Let's say we want to minimize the Euclidian norm over the probability simplex Δ. Using FrankWolfe.jl, this is what the code looks like (in dimension 3):

julia> using FrankWolfe

julia> f(p) = sum(abs2, p)  # objective function

# # function d ⟻ argmin ⟨p,d⟩ st. p ∈ Δ
julia> lmo = FrankWolfe.ProbabilitySimplexOracle(1.)

julia> p0 = [1., 0., 0.]

julia> p_opt, _ = frank_wolfe(f, grad!, lmo, p0; verbose=true);

Vanilla Frank-Wolfe Algorithm.
MEMORY_MODE: FrankWolfe.InplaceEmphasis() STEPSIZE: Adaptive EPSILON: 1.0e-7 MAXITERATION: 10000 TYPE: Float64
[ Info: In memory_mode memory iterates are written back into x0!

-------------------------------------------------------------------------------------------------
Type     Iteration         Primal           Dual       Dual Gap           Time         It/sec
-------------------------------------------------------------------------------------------------
I             1   1.000000e+00  -1.000000e+00   2.000000e+00   0.000000e+00            Inf
Last            24   3.333333e-01   3.333332e-01   9.488992e-08   1.533181e+00   1.565373e+01
-------------------------------------------------------------------------------------------------

julia> p_opt
3-element Vector{Float64}:
0.33333334349923327
0.33333332783841896
0.3333333286623478

## Documentation and examples

To explore the content of the package, go to the documentation.

Beyond those presented in the documentation, many more use cases are implemented in the examples folder. To run them, you will need to activate the test environment, which can be done simply with TestEnv.jl (we recommend you install it in your base Julia).

julia> using TestEnv

julia> TestEnv.activate()
"/tmp/jl_Ux8wKE/Project.toml"

# necessary for plotting
julia> include("examples/plot_utils.jl")
julia> include("examples/linear_regression.jl")
...

If you need the plotting utilities in your own code, make sure Plots.jl is included in your current project and run:

using Plots
using FrankWolfe

include(joinpath(dirname(pathof(FrankWolfe)), "../examples/plot_utils.jl"))