Theoretical and numerical comparison of first order algorithms for cocoercive equations and smooth convex optimization
Date Issued
2023-05
Author(s) USM
DOI
10.1016/j.sigpro.2022.108900
Abstract
This paper provides a theoretical and numerical comparison of classical first-order splitting methods for solving smooth convex optimization problems and cocoercive equations. From a theoretical point of view, we compare convergence rates of gradient desc
