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:

FrankWolfe.jl: A high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients.

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

julia> grad!(storage, p) = storage .= 2p  # in-place gradient computation

# # 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
MOMENTUM: nothing GRADIENTTYPE: Nothing
[ 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

Note that active-set based methods like Away Frank-Wolfe and Blended Pairwise Conditional Gradient also include a post processing step. In post-processing all values are recomputed and in particular the dual gap is computed at the current FW vertex, which might be slightly larger than the best dual gap observed as the gap is not monotonic. This is expected behavior.

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"))