  Accelerated algorithms for minimizing smooth strongly convex functions
usually require knowledge of the strong convexity parameter $\mu$. In the case
of an unknown $\mu$, current adaptive techniques are based on restart schemes.
When the optimal value $f^*$ is known, these strategies recover the accelerated
linear convergence bound without additional grid search. In this paper we
propose a new approach that has the same bound without any restart, using an
online estimation of strong convexity parameter. We show the robustness of the
Fast Gradient Method when using a sequence of upper bounds on $\mu$. We also
present a good candidate for this estimate sequence and detail consistent
empirical results.
