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We propose an accelerated meta-algorithm, which allows to obtain acceleratedmethods for convex unconstrained minimization in different settings. As anapplication of the general scheme we propose nearly optimal methods forminimizing smooth functions with Lipschitz derivatives of an arbitrary order,as well as for smooth minimax optimization problems. The proposedmeta-algorithm is more general than the ones in the literature and allows toobtain better convergence rates and practical performance in several settings.

Accelerated Meta-Algorithm for Convex Optimization Problems