Proximal-like contraction methods for monotone variational inequalities in a unified framework. II: General methods and numerical experiments

DOI10.1007/s10589-010-9373-zzbMath1268.90101OpenAlexW1979735396MaRDI QIDQ429466

Li-Zhi Liao, Xiang Wang, Bing-sheng He

Publication date: 19 June 2012

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-010-9373-z

zbMATH Keywords

monotonevariational inequalitycontraction methods

Mathematics Subject Classification ID

Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming) (90C33)

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Cites Work

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• Proximal-like contraction methods for monotone variational inequalities in a unified framework. I: Effective quadruplet and primary methods
• An approximate proximal-extragradient type method for monotone variational inequalities
• Approximate iterations in Bregman-function-based proximal algorithms
• Comparison of two kinds of prediction-correction methods for monotone variational inequalities
• Error bounds for proximal point subproblems and associated inexact proximal point algorithms
• A new inexact alternating directions method for monotone variational inequalities
• A globally convergent Newton method for solving strongly monotone variational inequalities
• Calibrating Least Squares Semidefinite Programming with Equality and Inequality Constraints
• Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
• Nonlinear Proximal Point Algorithms Using Bregman Functions, with Applications to Convex Programming
• A simple proof for some important properties of the projection mapping
• Improvements of some projection methods for monotone nonlinear variational inequalities

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