Relaxed-Inertial Proximal Point Algorithms for Nonconvex Equilibrium Problems with Applications
Abstract
We propose a relaxed-inertial proximal point algorithm for solving equilibrium problems involving bifunctions which satisfy in the second variable a generalized convexity notion called strong quasiconvexity, introduced by Polyak (Sov Math Dokl 7:7275, 1966). The method is suitable for solving mixed variational inequalities and inverse mixed variational inequalities involving strongly quasiconvex functions, as these can be written as special cases of equilibrium problems. Numerical experiments where the performance of the proposed algorithm outperforms one of the standard proximal point methods are provided, too. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Más información
| Título según WOS: | Relaxed-Inertial Proximal Point Algorithms for Nonconvex Equilibrium Problems with Applications |
| Título según SCOPUS: | Relaxed-Inertial Proximal Point Algorithms for Nonconvex Equilibrium Problems with Applications |
| Título de la Revista: | Journal of Optimization Theory and Applications |
| Volumen: | 203 |
| Número: | 3 |
| Editorial: | Springer |
| Fecha de publicación: | 2024 |
| Página de inicio: | 2233 |
| Página final: | 2262 |
| Idioma: | English |
| DOI: |
10.1007/s10957-023-02375-1 |
| Notas: | ISI, SCOPUS |