Solving of a Variational Inequality Problem Under the Presence of Computational Errors

W. Takahashi and M. Toyoda (2003) proved weak convergence of an iteration process of solving a variational inequality problem for an inverse strongly-monotone mapping. In our recent work we showed that, for the same iterative process, most of its exact iterates are approximate solutions of the variational inequality. In this paper, we show that the iteration process for solving a variational inequality problem for an inverse strongly monotone mapping generates an approximate solution in the presence of small computational errors. We also estimate a number of iterates needed in order to obtain such an approximate solution.

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Publication Details

Journal
Mathematics
Published
2026-02-13
DOI
https://doi.org/10.3390/math14040664
Primary Topic
Optimization and Variational Analysis
Type
article
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article

Solving of a Variational Inequality Problem Under the Presence of Computational Errors

Alexander J. Zaslavski
Mathematics
Optimization and Variational Analysis
article

Solving of a Variational Inequality Problem Under the Presence of Computational Errors

Alexander J. Zaslavski
article en

Abstract

W. Takahashi and M. Toyoda (2003) proved weak convergence of an iteration process of solving a variational inequality problem for an inverse strongly-monotone mapping. In our recent work we showed that, for the same iterative process, most of its exact iterates are approximate solutions of the variational inequality. In this paper, we show that the iteration process for solving a variational inequality problem for an inverse strongly monotone mapping generates an approximate solution in the presence of small computational errors. We also estimate a number of iterates needed in order to obtain such an approximate solution.

MathematicsVol. 14(4)
Technion – Israel Institute of Technology (IL)
Reduced inequalities
Openalex Percentile: Top 55%
Optimization and Variational Analysis
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