  We investigate a projection free method, namely conditional gradient sliding
on batched, stochastic and finite-sum non-convex problem. CGS is a smart
combination of Nesterov's accelerated gradient method and Frank-Wolfe (FW)
method, and outperforms FW in the convex setting by saving gradient
computations. However, the study of CGS in the non-convex setting is limited.
In this paper, we propose the non-convex conditional gradient sliding (NCGS)
which surpasses the non-convex Frank-Wolfe method in batched, stochastic and
finite-sum setting.
