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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
Publisher	Association for Computing Machinery
Pages	92-97
Number of pages	6
ISBN (Electronic)	9781450377485
DOIs	https://doi.org/10.1145/3404716.3404734
State	Published - 28 May 2020
Event	5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020 - Chengdu, China Duration: 28 May 2020 → 30 May 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020
Country/Territory	China
City	Chengdu
Period	28/05/20 → 30/05/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

Descomposition method
Euclidean Jordan algebra
Proximal distance
Symmetric cone optimization

Access to Document

10.1145/3404716.3404734

Cite this

Quiroz, E. A. P., Luis, J. L., & Lengua, M. C. (2020). A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization. In Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2020 (pp. 92-97). (ACM International Conference Proceeding Series). Association for Computing Machinery. 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.; 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 = "10.1145/3404716.3404734",

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Quiroz, EAP, Luis, JL & Lengua, MC 2020, A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization. in 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. 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. Association for Computing Machinery, 2020. p. 92-97 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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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Y2 - 28 May 2020 through 30 May 2020

ER -

A Proximal Multiplier Method for Convex Separable Symmetric Cone Optimization

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Keywords

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