Regularized gradient-projection methods for the constrained convex minimization problem and the zero points of maximal monotone operator

DOI10.1186/s13663-015-0258-9zbMath1309.47084OpenAlexW2148886965WikidataQ59436072 ScholiaQ59436072MaRDI QIDQ2017669

Si-Wen Jiao, Ming Tian

Publication date: 23 March 2015

Published in: Fixed Point Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1186/s13663-015-0258-9

zbMATH Keywords

resolventstrong convergencefixed pointvariational inequalityiterative methodmaximal monotone operatorequilibrium problemconstrained convex minimization

Mathematics Subject Classification ID

Iterative procedures involving nonlinear operators (47J25) Contraction-type mappings, nonexpansive mappings, (A)-proper mappings, etc. (47H09) Variational inequalities (global problems) in infinite-dimensional spaces (58E35) Numerical solutions to equations with nonlinear operators (65J15)

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Regularized gradient-projection methods for finding the minimum-norm solution of the constrained convex minimization problem

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