On the rate of convergence of alternating minimization for non-smooth non-strongly convex optimization in Banach spaces

DOI10.1007/S11590-021-01753-WzbMath1487.90522arXiv1911.00404OpenAlexW3165796876MaRDI QIDQ2115326

Jakub Wiktor Both

Publication date: 15 March 2022

Published in: Optimization Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.00404

zbMATH Keywords

rate of convergenceBanach spacesconvex optimizationlinear convergencesublinear convergencealternating minimization

Mathematics Subject Classification ID

Convex programming (90C25) Programming in abstract spaces (90C48)

Uses Software

• Unnamed Item

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Error bounds and convergence analysis of feasible descent methods: A general approach
• On the convergence of the block nonlinear Gauss-Seidel method under convex constraints
• Coordinate descent algorithms
• Restricted strong convexity and its applications to convergence analysis of gradient-type methods in convex optimization
• Linear convergence of first order methods for non-strongly convex optimization
• On the Convergence of Alternating Minimization for Convex Programming with Applications to Iteratively Reweighted Least Squares and Decomposition Schemes
• Rate of Convergence of Some Space Decomposition Methods for Linear and Nonlinear Problems
• Globally convergent block-coordinate techniques for unconstrained optimization
• DUNE — The Distributed and Unified Numerics Environment
• On the Convergence of Block Coordinate Descent Type Methods

This page was built for publication: On the rate of convergence of alternating minimization for non-smooth non-strongly convex optimization in Banach spaces