A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems

Miguel Angel Cano Lengua, Erik Alex Papa Quiroz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
PublisherAssociation for Computing Machinery
Pages98-104
Number of pages7
ISBN (Electronic)9781450377485
DOIs
StatePublished - 28 May 2020
Externally publishedYes
Event5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020 - Chengdu, China
Duration: 28 May 202030 May 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
CountryChina
CityChengdu
Period28/05/2030/05/20

Keywords

  • Global convergence
  • Location theory
  • Proximal point method
  • Quasiconvex function

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