Adaptive Accelerated Gradient Method for Smooth Convex Optimization
Optimization and Control
2025-12-24 v1
Abstract
We propose an adaptive accelerated gradient method for solving smooth convex optimization problems. The method incorporates a scheme to determine the step size adaptively, by means of a local estimation of the smoothness constant, which is assumed unknown, without resorting to line search procedures. The sequence generated by this method converges weakly to a minimizer of the objective function, and the function values converge at a fast rate of . Moreover, if the objective function is strongly convex, the function values converge at a linear rate.
Cite
@article{arxiv.2512.20478,
title = {Adaptive Accelerated Gradient Method for Smooth Convex Optimization},
author = {Zepeng Wang and Juan Peypouquet},
journal= {arXiv preprint arXiv:2512.20478},
year = {2025}
}