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Volume 5, Issue 2, 1 August 2023, Pages 141-153
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.
How to Cite this Article:
H. Abbaszadehpeivasti, E. de Klerk, M. Zamani, Convergence rate analysis of randomized and cyclic coordinate descent for convex optimization through semidefinite programming, Appl. Set-Valued Anal. Optim. 5 (2023), 141-153.