A proximal multiplier method for convex separable symmetric cone optimization
Fecha
2020-05-01Autor(es)
Papa Quiroz, Erik Alex
López Luis, Julio
Cano Lengua, Miguel Angel
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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.
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Cita bibliográfica
Papa, E. A., López, J., & Cano, M. A. (2020). A proximal multiplier method for convex separable symmetric cone optimization. ICMSSP 2020: Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing. https://doi.org/10.1145/3404716.3404734
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El texto completo de este trabajo no está disponible en el Repositorio Académico UPN por restricciones de la casa editorial donde ha sido publicado.
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