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Projection-Free Non-Smooth Convex Programming
We study nonsmooth convex minimization over a compact convex set without projections. For a globally $L$-Lipschitz objective, optimistic primal-dual updates give error at most $2LD/\sqrt{T}$ after $T$ iterations, where $D$ is the set's diameter. Each iteration uses at most one linear optimization ca
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