  We present a novel method for convex unconstrained optimization that, without
any modifications, ensures: (i) accelerated convergence rate for smooth
objectives, (ii) standard convergence rate in the general (non-smooth) setting,
and (iii) standard convergence rate in the stochastic optimization setting. To
the best of our knowledge, this is the first method that simultaneously applies
to all of the above settings. At the heart of our method is an adaptive
learning rate rule that employs importance weights, in the spirit of adaptive
online learning algorithms (Duchi et al., 2011; Levy, 2017), combined with an
update that linearly couples two sequences, in the spirit of (Allen-Zhu and
Orecchia, 2017). An empirical examination of our method demonstrates its
applicability to the above mentioned scenarios and corroborates our theoretical
findings.
