In constrained convex optimization, existing methods based on the ellipsoid or cu ing plane method do not scale well with the dimension of the ambient space. Alternative approaches such as Projected Gradient Descent only provide a computational bene t for simple convex sets such as Euclidean balls, where Euclidean projections can be performed e ciently. For other sets, the cost of the projections can be too high. To circumvent these issues, alternative methods based on the famous Frank-Wolfe algorithm have been studied and used. Such methods use a Linear Optimization Oracle at each iteration instead of Euclidean projections; the former can o en be performed e ciently. Such methods have also been extended to the online and stochastic optimization se ings. However, the Frank-Wolfe algorithm and its variants do not achieve the optimal performance, in terms of regret or rate, for general convex sets. What is more, the Linear Optimization Oracle they use can still be computationally expensive in some cases. In this paper, we move away from Frank-Wolfe style algorithms and present a new reduction that turns any algorithm A de ned on a Euclidean ball (where projections are cheap) to an algorithm on a constrained set C contained within the ball, without sacri cing the performance of the original algorithm A by much. Our reduction requires 𝑂 (𝑇 ln𝑇 ) calls to a Membership Oracle on C a er 𝑇 rounds, and no linear optimization on C is needed. Using our reduction, we recover optimal regret bounds [resp. rates], in terms of the number of iterations, in online [resp. stochastic] convex optimization. Our guarantees are also useful in the o ine convex optimization se ing when the dimension of the ambient space is large.
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