A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems

Miguel Angel Cano Lengua; Erik Alex Papa Quiroz

doi:10.1145/3404716.3404735

A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems

Miguel Angel Cano Lengua, Erik Alex Papa Quiroz

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión 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 original	Inglés
Título de la publicación alojada	Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
Editorial	Association for Computing Machinery
Páginas	98-104
Número de páginas	7
ISBN (versión digital)	9781450377485
DOI	https://doi.org/10.1145/3404716.3404735
Estado	Publicada - 28 may. 2020
Publicado de forma externa	Sí
Evento	5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020 - Chengdu, China Duración: 28 may. 2020 → 30 may. 2020

Serie de la publicación

Nombre	ACM International Conference Proceeding Series

Conferencia

Conferencia	5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
País/Territorio	China
Ciudad	Chengdu
Período	28/05/20 → 30/05/20

Nota bibliográfica

Publisher Copyright:
© 2020 ACM.

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10.1145/3404716.3404735

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Cano Lengua, M. A., & Quiroz, E. A. P. (2020). A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems. En Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020 (pp. 98-104). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3404716.3404735

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title = "A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems",

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.",

keywords = "Global convergence, Location theory, Proximal point method, Quasiconvex function",

author = "{Cano Lengua}, {Miguel Angel} and Quiroz, {Erik Alex Papa}",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020 ; Conference date: 28-05-2020 Through 30-05-2020",

year = "2020",

month = may,

day = "28",

doi = "10.1145/3404716.3404735",

language = "Ingl{\'e}s",

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Cano Lengua, MA & Quiroz, EAP 2020, A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems. En Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 98-104, 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020, Chengdu, China, 28/05/20. https://doi.org/10.1145/3404716.3404735

A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems. / Cano Lengua, Miguel Angel; Quiroz, Erik Alex Papa.
Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020. Association for Computing Machinery, 2020. p. 98-104 (ACM International Conference Proceeding Series).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems

AU - Cano Lengua, Miguel Angel

AU - Quiroz, Erik Alex Papa

PY - 2020/5/28

Y1 - 2020/5/28

N2 - 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.

AB - 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.

KW - Global convergence

KW - Location theory

KW - Proximal point method

KW - Quasiconvex function

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M3 - Contribución a la conferencia

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T3 - ACM International Conference Proceeding Series

SP - 98

EP - 104

BT - Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020

PB - Association for Computing Machinery

T2 - 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020

Y2 - 28 May 2020 through 30 May 2020

ER -

A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems

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