  We introduce a few variants on Frank-Wolfe style algorithms suitable for
large scale optimization. We show how to modify the standard Frank-Wolfe
algorithm using stochastic gradients, approximate subproblem solutions, and
sketched decision variables in order to scale to enormous problems while
preserving (up to constants) the optimal convergence rate
$\mathcal{O}(\frac{1}{k})$.
