Analyzing random permutations for cyclic coordinate descent

DOI10.1090/mcom/3530zbMath1442.65049arXiv1706.00908OpenAlexW3006529314MaRDI QIDQ5113666

Ching-pei Lee, Stephen J. Wright

Publication date: 15 June 2020

Published in: Mathematics of Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1706.00908

zbMATH Keywords

permutationsrandomizationGauss-Seidel methodcoordinate descent

Mathematics Subject Classification ID

Convex programming (90C25) Iterative numerical methods for linear systems (65F10) Randomized algorithms (68W20)

Related Items

Cyclic Coordinate Dual Averaging with Extrapolation, Randomized Block Adaptive Linear System Solvers, Randomized Douglas–Rachford Methods for Linear Systems: Improved Accuracy and Efficiency, On the Global Convergence of Randomized Coordinate Gradient Descent for Nonconvex Optimization, Inexact variable metric stochastic block-coordinate descent for regularized optimization, Two Symmetrized Coordinate Descent Methods Can Be $O(n^2)$ Times Slower Than the Randomized Version, An Implicit Representation and Iterative Solution of Randomly Sketched Linear Systems, Distributed block-diagonal approximation methods for regularized empirical risk minimization

Cites Work

• Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems
• On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization
• Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
• On the Convergence of Block Coordinate Descent Type Methods
• Random permutations fix a worst case for cyclic coordinate descent