  Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documented
merits in machine learning and signal processing applications. Unlike
projection-based methods, momentum cannot improve the convergence rate of FW,
in general. This limitation motivates the present work, which deals with heavy
ball momentum, and its impact to FW. Specifically, it is established that heavy
ball offers a unifying perspective on the primal-dual (PD) convergence, and
enjoys a tighter per iteration PD error rate, for multiple choices of step
sizes, where PD error can serve as the stopping criterion in practice. In
addition, it is asserted that restart, a scheme typically employed jointly with
Nesterov's momentum, can further tighten this PD error bound. Numerical results
demonstrate the usefulness of heavy ball momentum in FW iterations.
