Continuous-Time Analysis of Anchor Acceleration

Abstract

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.

Cite

Text

Suh et al. "Continuous-Time Analysis of Anchor Acceleration." Neural Information Processing Systems, 2023.

Markdown

[Suh et al. "Continuous-Time Analysis of Anchor Acceleration." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/suh2023neurips-continuoustime/)

BibTeX

@inproceedings{suh2023neurips-continuoustime,
  title     = {{Continuous-Time Analysis of Anchor Acceleration}},
  author    = {Suh, Jaewook and Park, Jisun and Ryu, Ernest},
  booktitle = {Neural Information Processing Systems},
  year      = {2023},
  url       = {https://mlanthology.org/neurips/2023/suh2023neurips-continuoustime/}
}