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We propose accelerated randomized coordinate descent algorithms forstochastic optimization and online learning. Our algorithms have significantlyless per-iteration complexity than the known accelerated gradient algorithms.The proposed algorithms for online learning have better regret performance thanthe known randomized online coordinate descent algorithms. Furthermore, theproposed algorithms for stochastic optimization exhibit as good convergencerates as the best known randomized coordinate descent algorithms. We also showsimulation results to demonstrate performance of the proposed algorithms.

Accelerated Randomized Coordinate Descent Algorithms for Stochastic Optimization and Online Learning