Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems

Abstract

We analyze continuous-time models of accelerated gradient methods through deriving conservation laws in dilated coordinate systems. Namely, instead of analyzing the dynamics of $X(t)$, we analyze the dynamics of $W(t)=t^\alpha(X(t)-X_c)$ for some $\alpha$ and $X_c$ and derive a conserved quantity, analogous to physical energy, in this dilated coordinate system. Through this methodology, we recover many known continuous-time analyses in a streamlined manner and obtain novel continuous-time analyses for OGM-G, an acceleration mechanism for efficiently reducing gradient magnitude that is distinct from that of Nesterov. Finally, we show that a semi-second-order symplectic Euler discretization in the dilated coordinate system leads to an $\mathcal{O}(1/k^2)$ rate on the standard setup of smooth convex minimization, without any further assumptions such as infinite differentiability.

Cite

Text

Suh et al. "Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems." International Conference on Machine Learning, 2022.

Markdown

[Suh et al. "Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems." International Conference on Machine Learning, 2022.](https://mlanthology.org/icml/2022/suh2022icml-continuoustime/)

BibTeX

@inproceedings{suh2022icml-continuoustime,
  title     = {{Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems}},
  author    = {Suh, Jaewook J and Roh, Gyumin and Ryu, Ernest K},
  booktitle = {International Conference on Machine Learning},
  year      = {2022},
  pages     = {20640-20667},
  volume    = {162},
  url       = {https://mlanthology.org/icml/2022/suh2022icml-continuoustime/}
}