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Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:20640-20667, 2022.
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)=tα(X(t)−Xc) for some α and Xc 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 O(1/k2) rate on the standard setup of smooth convex minimization, without any further assumptions such as infinite differentiability.