Universal Algorithms: Beyond the Simplex

The bulk of universal algorithms in the online convex optimisation literature are variants of the Hedge (exponential weights) algorithm on the simplex. While these algorithms extend to polytope domains by assigning weights to the vertices, this process is computationally unfeasible for many important classes of polytopes where the number $V$ of vertices depends exponentially on the dimension $d$. In this paper we show the Subgradient algorithm is universal, meaning it has $O(\sqrt N)$ regret in the antagonistic setting and $O(1)$ pseudo-regret in the i.i.d setting, with two main advantages over Hedge: (1) The update step is more efficient as the action vectors have length only $d$ rather than $V$; and (2) Subgradient gives better performance if the cost vectors satisfy Euclidean rather than sup-norm bounds. This paper extends the authors' recent results for Subgradient on the simplex. We also prove the same $O(\sqrt N)$ and $O(1)$ bounds when the domain is the unit ball. To the authors' knowledge this is the first instance of these bounds on a domain other than a polytope.Comment: 1 figure, 40 page

Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes

Optimization algorithms such as projected Newton's method, FISTA, mirror descent and its variants enjoy near-optimal regret bounds and convergence rates, but suffer from a computational bottleneck of computing "projections'' in potentially each iteration (e.g., $O(T^{1/2})$ regret of online mirror descent). On the other hand, conditional gradient variants solve a linear optimization in each iteration, but result in suboptimal rates (e.g., $O(T^{3/4})$ regret of online Frank-Wolfe). Motivated by this trade-off in runtime v/s convergence rates, we consider iterative projections of close-by points over widely-prevalent submodular base polytopes $B(f)$. We develop a toolkit to speed up the computation of projections using both discrete and continuous perspectives. We subsequently adapt the away-step Frank-Wolfe algorithm to use this information and enable early termination. For the special case of cardinality based submodular polytopes, we improve the runtime of computing certain Bregman projections by a factor of $\Omega(n/\log(n))$. Our theoretical results show orders of magnitude reduction in runtime in preliminary computational experiments