We develop an accelerated randomized proximal coordinate gradient (APCG) method, for solving a broad class of composite convex optimization problems. In particular, our method achieves faster linear convergence rates for minimizing strongly convex functions than existing randomized proximal coordinate gradient methods. We show how to apply the APCG method to solve the dual of the regularized empirical risk minimization (ERM) problem, and devise efficient implementations that avoid full-dimensional vector operations. For ill-conditioned ERM problems, our method obtains improved convergence rates than the state-of-the-art stochastic dual coordinate ascent (SDCA) method.
|Original language||English (US)|
|Number of pages||9|
|Journal||Advances in Neural Information Processing Systems|
|State||Published - Jan 1 2014|
|Event||28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada|
Duration: Dec 8 2014 → Dec 13 2014