A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems

Miguel Angel Cano Lengua, Erik Alex Papa Quiroz

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The location problem is of great interest in order to establish different location demands in the state or private sector. The model of this problem is usually reduced to a mathematical optimization problem. In this paper we present a proximal method to solve location problems where the objective function is quasi-convex and non-differentiable. We prove that the iterations given by the method are well defined and under some assumptions on the objective function we prove the convergence of the method.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
EditorialICST
Páginas98-104
Número de páginas7
ISBN (versión digital)9781450377485
DOI
EstadoPublicada - 28 may 2020
Evento5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020 - Chengdu, China
Duración: 28 may 202030 may 2020

Serie de la publicación

NombrePervasiveHealth: Pervasive Computing Technologies for Healthcare
ISSN (versión impresa)2153-1633

Conferencia

Conferencia5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
País/TerritorioChina
CiudadChengdu
Período28/05/2030/05/20

Nota bibliográfica

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