Um algoritmo proximal com quase-distância
In this work, based in [1, 18], we study the convergence of method of proximal point (MPP) regularized by a quasi-distance, applied to an optimization problem. The objective function considered not is necessarily convex and satisfies the property of Kurdyka- Lojasiewicz around by their generalized c...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Brasil |
| Institución: | Universidade Federal de Goiás (UFG) |
| Repositorio: | Repositório Institucional da UFG |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.bc.ufg.br:tede/4521 |
| Acceso en línea: | http://repositorio.bc.ufg.br/tede/handle/tede/4521 |
| Access Level: | acceso abierto |
| Palabra clave: | Algoritmo proximal Desigualdade de krdyka-lojasiewicz Proximal algorithm Inequality kurdyka-lojasiewicz CIENCIAS EXATAS E DA TERRA::MATEMATICA |
| Sumario: | In this work, based in [1, 18], we study the convergence of method of proximal point (MPP) regularized by a quasi-distance, applied to an optimization problem. The objective function considered not is necessarily convex and satisfies the property of Kurdyka- Lojasiewicz around by their generalized critical points. More specifically, we will show that any limited sequence, generated from MPP, converge the a generalized critical point. |
|---|