Convergence rate analysis of randomized and cyclic coordinate descent for convex optimization through semidefinite programming
Optimization and Control
2022-12-26 v1
Abstract
In this paper, we study randomized and cyclic coordinate descent for convex unconstrained optimization problems. We improve the known convergence rates in some cases by using the numerical semidefinite programming performance estimation method. As a spin-off we provide a method to analyse the worst-case performance of the Gauss-Seidel iterative method for linear systems where the coefficient matrix is positive semidefinite with a positive diagonal.
Cite
@article{arxiv.2212.12384,
title = {Convergence rate analysis of randomized and cyclic coordinate descent for convex optimization through semidefinite programming},
author = {Hadi Abbaszadehpeivasti and Etienne de Klerk and Moslem Zamani},
journal= {arXiv preprint arXiv:2212.12384},
year = {2022}
}