  We propose accelerated randomized coordinate descent algorithms for
stochastic optimization and online learning. Our algorithms have significantly
less per-iteration complexity than the known accelerated gradient algorithms.
The proposed algorithms for online learning have better regret performance than
the known randomized online coordinate descent algorithms. Furthermore, the
proposed algorithms for stochastic optimization exhibit as good convergence
rates as the best known randomized coordinate descent algorithms. We also show
simulation results to demonstrate performance of the proposed algorithms.
