Convergence rates of proximal gradient methods via the convex conjugate
Optimization and Control
2018-01-10 v2
Abstract
We give a novel proof of the and convergence rates of the proximal gradient and accelerated proximal gradient methods for composite convex minimization. The crux of the new proof is an upper bound constructed via the convex conjugate of the objective function.
Keywords
Cite
@article{arxiv.1801.02509,
title = {Convergence rates of proximal gradient methods via the convex conjugate},
author = {David H. Gutman and Javier F. Pena},
journal= {arXiv preprint arXiv:1801.02509},
year = {2018}
}