Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
XML PDF e-pub
PDF e-Pub

Section: New Results

Polyak Steps for Adaptive Fast Gradient Methods.

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.