A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization

Erik Alex Papa Quiroz; Julio López Luis; Miguel Cano Lengua

doi:10.1145/3404716.3404734

A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization

Erik Alex Papa Quiroz, Julio López Luis, Miguel Cano Lengua

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

1 Cita (Scopus)

Resumen

This work is devoted to the study of a proximal decomposition algorithm for solving convex symmetric cone optimization with separable structures. The algorithm considered is based on a decomposition method and proximal distances. Under suitable assumptions, we prove that each limit point of the primal-dual sequences generated by the algorithm solves the problem. Finally, the global convergence is established.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
Editorial	ICST
Páginas	92-97
Número de páginas	6
ISBN (versión digital)	9781450377485
DOI	https://doi.org/10.1145/3404716.3404734
Estado	Publicada - 28 may 2020
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	PervasiveHealth: Pervasive Computing Technologies for Healthcare
ISSN (versión impresa)	2153-1633

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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10.1145/3404716.3404734

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Quiroz, E. A. P., Luis, J. L., & Lengua, M. C. (2020). A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization. En Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020 (pp. 92-97). (PervasiveHealth: Pervasive Computing Technologies for Healthcare). ICST. https://doi.org/10.1145/3404716.3404734

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title = "A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization",

abstract = "This work is devoted to the study of a proximal decomposition algorithm for solving convex symmetric cone optimization with separable structures. The algorithm considered is based on a decomposition method and proximal distances. Under suitable assumptions, we prove that each limit point of the primal-dual sequences generated by the algorithm solves the problem. Finally, the global convergence is established.",

keywords = "Descomposition method, Euclidean Jordan algebra, Proximal distance, Symmetric cone optimization",

author = "Quiroz, {Erik Alex Papa} and Luis, {Julio L{\'o}pez} and Lengua, {Miguel Cano}",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; null ; Conference date: 28-05-2020 Through 30-05-2020",

year = "2020",

month = may,

day = "28",

doi = "10.1145/3404716.3404734",

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

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Quiroz, EAP, Luis, JL & Lengua, MC 2020, A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization. En Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020. PervasiveHealth: Pervasive Computing Technologies for Healthcare, ICST, pp. 92-97, 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020, Chengdu, China, 28/05/20. https://doi.org/10.1145/3404716.3404734

A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization. / Quiroz, Erik Alex Papa; Luis, Julio López; Lengua, Miguel Cano.

Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020. ICST, 2020. p. 92-97 (PervasiveHealth: Pervasive Computing Technologies for Healthcare).

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

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T1 - A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization

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AU - Luis, Julio López

AU - Lengua, Miguel Cano

PY - 2020/5/28

Y1 - 2020/5/28

N2 - This work is devoted to the study of a proximal decomposition algorithm for solving convex symmetric cone optimization with separable structures. The algorithm considered is based on a decomposition method and proximal distances. Under suitable assumptions, we prove that each limit point of the primal-dual sequences generated by the algorithm solves the problem. Finally, the global convergence is established.

AB - This work is devoted to the study of a proximal decomposition algorithm for solving convex symmetric cone optimization with separable structures. The algorithm considered is based on a decomposition method and proximal distances. Under suitable assumptions, we prove that each limit point of the primal-dual sequences generated by the algorithm solves the problem. Finally, the global convergence is established.

KW - Descomposition method

KW - Euclidean Jordan algebra

KW - Proximal distance

KW - Symmetric cone optimization

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

PB - ICST

Y2 - 28 May 2020 through 30 May 2020

ER -

A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization

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