  We propose several variants of the Frank-Wolfe algorithm to minimize a sum of
functions. The main proposed algorithm is inspired from the dual averaging
scheme of Nesterov adapted for Frank Wolfe in a stochastic setting. A
distributed version of this scheme is also suggested. Additionally, we propose
a Frank-Wolfe variant based on incremental gradient techniques. The convergence
rates for all the proposed algorithms are established. The performance is
studied on least squares regression and multinomial classification.
