We present FrankWolfe.jl, a new Julia package implementing several Frank-Wolfe algorithms to optimize differentiable functions with convex constraints.
The Julia optimization ecosystem includes toolboxes for unconstrained optimization on one hand and domain-specific modelling languages for constrained optimization on the other hand.
This package offers the possibility to optimize functions defined as Julia code with DSL-based closed-form or arbitrary convex constraints in an efficient manner.

MathOptInterface has become a pillar of constrained optimization in Julia, defining a common language unifying multiple branches of mathematical optimization. We will present MathOptSetDistances.jl, a package to compute distances to and projections onto sets, and the differentiation of these operations. We will cover the motivation behind it, how it started and highlight learned lessons on the way.