  This paper presents new projection-free algorithms for Online Convex
Optimization (OCO) over a convex domain $\mathcal{K} \subset \mathbb{R}^d$.
Classical OCO algorithms (such as Online Gradient Descent) typically need to
perform Euclidean projections onto the convex set $\cK$ to ensure feasibility
of their iterates. Alternative algorithms, such as those based on the
Frank-Wolfe method, swap potentially-expensive Euclidean projections onto
$\mathcal{K}$ for linear optimization over $\mathcal{K}$. However, such
algorithms have a sub-optimal regret in OCO compared to projection-based
algorithms. In this paper, we look at a third type of algorithms that output
approximate Newton iterates using a self-concordant barrier for the set of
interest. The use of a self-concordant barrier automatically ensures
feasibility without the need for projections. However, the computation of the
Newton iterates requires a matrix inverse, which can still be expensive. As our
main contribution, we show how the stability of the Newton iterates can be
leveraged to compute the inverse Hessian only a vanishing fraction of the
rounds, leading to a new efficient projection-free OCO algorithm with a
state-of-the-art regret bound.
