The strong convergence of prediction-correction and relaxed hybrid steepest-descent method for variational inequalities
type: Item
instance of: scholarly article
title: The strong convergence of prediction-correction and relaxed hybrid steepest-descent method for variational inequalities
zbMATH Open document ID: 1291.90267
DOI: 10.1155/2013/515902
author: Haiwen Xu
published in: Abstract and Applied Analysis
publication date: 2014-06-23T00:00:00Z
review text: Summary: We establish the strong convergence of prediction-correction and relaxed hybrid steepest-descent method (PRH method) for variational inequalities under some suitable conditions that simplify the proof. And it is to be noted that the proof is different from the previous results and also is not similar to the previous results. More importantly, we design a set of practical numerical experiments. The results demonstrate that the PRH method under some descent directions is more slightly efficient than that of the modified and relaxed hybrid steepest-descent method, and the PRH Method under some new conditions is more efficient than that under some old conditions.
Mathematics Subject Classification ID: 90C33
Mathematics Subject Classification ID: 47J25
Mathematics Subject Classification ID: 49J40
zbMATH DE Number: 6306962
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/515902
OpenAlex ID: W2125212852
Wikidata QID: Q58916977
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description: scientific article; zbMATH DE number 6306962