A semi-smoothing augmented Lagrange multiplier algorithm for low-rank Toeplitz matrix completion

Autor:	Ruiping Wen, Shuzhen Li, Yonghong Duan
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	Toeplitz matrix Completion Augmented Lagrange multiplier Data communication Mathematics QA1-939
Zdroj:	Journal of Inequalities and Applications, Vol 2019, Iss 1, Pp 1-16 (2019)
Druh dokumentu:	article
ISSN:	1029-242X
DOI:	10.1186/s13660-019-2033-7
Popis:	Abstract The smoothing augmented Lagrange multiplier (SALM) algorithm is a generalization of the augmented Lagrange multiplier algorithm for completing a Toeplitz matrix, which saves computational cost of the singular value decomposition (SVD) and approximates well the solution. However, the communication of numerous data is computationally demanding at each iteration step. In this paper, we propose an accelerated scheme to the SALM algorithm for the Toeplitz matrix completion (TMC), which will reduce the extra load coming from data communication under reasonable smoothing. It has resulted in a semi-smoothing augmented Lagrange multiplier (SSALM) algorithm. Meanwhile, we demonstrate the convergence theory of the new algorithm. Finally, numerical experiments show that the new algorithm is more effective/economic than the original algorithm.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/addcc7ff5cd44b30ad511f5acbf1650a Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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