Continuous-time Analysis of Anchor Acceleration

Neural Information Processing Systems 

Recently, the anchor acceleration, an acceleration mechanism distinct from Nesterov's, has been discovered for minimax optimization and fixed-point problems, but its mechanism is not understood well, much less so than Nesterov acceleration. In this work, we analyze continuous-time models of anchor acceleration. We provide tight, unified analyses for characterizing the convergence rate as a function of the anchor coefficient \beta(t), thereby providing insight into the anchor acceleration mechanism and its accelerated \mathcal{O}(1/k 2) -convergence rate. Finally, we present an adaptive method inspired by the continuous-time analyses and establish its effectiveness through theoretical analyses and experiments.