Imagen de Portada

Universal subgradient and proximal bundle methods for convex and strongly convex hybrid composite optimization

Guardado en:
Publicado en:arXiv.org (Aug 2, 2024), p. n/a
Autor principal: Guigues, Vincent
Otros Autores: Liang, Jiaming, Monteiro, Renato D C
Publicado:
Cornell University Library, arXiv.org
Materias:
Hybrid composites
Convexity
Parameters
Optimization
Acceso en línea:Citation/Abstract
Full text outside of ProQuest
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:This paper develops two parameter-free methods for solving convex and strongly convex hybrid composite optimization problems, namely, a composite subgradient type method and a proximal bundle type method. Both functional and stationary complexity bounds for the two methods are established in terms of the unknown strong convexity parameter. To the best of our knowledge, the two proposed methods are the first universal methods for solving hybrid strongly convex composite optimization problems that do not rely on any restart scheme nor require the knowledge of the optimal value.
ISSN:2331-8422
Fuente:Engineering Database