About descent property for extragradient algorithm

A new result on the descent property of the extragradient algorithm is proved, and for unconstrained problems, an estimate of the algorithm speed in terms of natural residuals is obtained under weaker conditions than known ones.

Authors

Institutions

Publication Details

Journal
Journal of Numerical and Applied Mathematics
Published
2025-01-01
DOI
https://doi.org/10.17721/2706-9699.2025.1.03
Primary Topic
Optimization and Variational Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

About descent property for extragradient algorithm

O. Yu. Kovalenko, Yu. V. Goncharenko
Journal of Numerical and Applied Mathematics
Optimization and Variational Analysis
article

About descent property for extragradient algorithm

O. Yu. Kovalenko, Yu. V. Goncharenko
article en

Abstract

A new result on the descent property of the extragradient algorithm is proved, and for unconstrained problems, an estimate of the algorithm speed in terms of natural residuals is obtained under weaker conditions than known ones.

Journal of Numerical and Applied Mathematics(1)
Taras Shevchenko National University of Kyiv (UA)
Openalex Percentile: Top 81%
Optimization and Variational Analysis
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

About descent property for extragradient algorithm — O. Yu. Kovalenko, Yu. V. Goncharenko · Journal of Numerical and Applied Mathematics (2025) | TGRS Research Map | TGRS