Strong convergence results for solving non-monotone variational inequalities via inertial projection and contraction methods

This paper introduces some new conditions imposing on inertial projection and contraction methods for solving non-monotone variational inequalities in real Hilbert spaces. Under our new conditions, we prove that the sequences generated by our algorithms converge strongly to a solution of the origina...

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Vydané v:Communications in nonlinear science & numerical simulation Ročník 152; s. 109244
Hlavní autori: Dung, Vu Tien, Thong, Duong Viet, Thang, Hoang Van, Long, Luong Van
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.01.2026
Predmet:
Network equilibrium flow problem
Non-monotone mapping
Projection and contraction method
Strong convergence
Variational inequality problem
Strong convergence
47H10
65K15
47J20
68W10
Variational inequality problem
47H09
Network equilibrium flow problem
47J25
Non-monotone mapping
65Y05
Projection and contraction method
ISSN:1007-5704
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Popis
Shrnutí:This paper introduces some new conditions imposing on inertial projection and contraction methods for solving non-monotone variational inequalities in real Hilbert spaces. Under our new conditions, we prove that the sequences generated by our algorithms converge strongly to a solution of the original variational inequality problem. To demonstrate the efficacy and behavior of our proposed algorithms, we present comprehensive numerical experiments and comparisons with existing methods from the literature. Furthermore, we showcase the applicability of our approachs by addressing a network equilibrium flow problem.
ISSN:1007-5704
DOI:10.1016/j.cnsns.2025.109244