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An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization
Joint Authors
Zhu, Lei
Yu, Zhanke
Ma, Feng
Ni, Mingfang
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-30
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Many application problems of practical interest can be posed as structured convex optimization models.
In this paper, we study a new first-order primaldual algorithm.
The method can be easily implementable, provided that the resolvent operators of the component objective functions are simple to evaluate.
We show that the proposed method can be interpreted as a proximal point algorithm with a customized metric proximal parameter.
Convergence property is established under the analytic contraction framework.
Finally, we verify the efficiency of the algorithm by solving the stable principal component pursuit problem.
American Psychological Association (APA)
Ma, Feng& Ni, Mingfang& Zhu, Lei& Yu, Zhanke. 2014. An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1013848
Modern Language Association (MLA)
Ma, Feng…[et al.]. An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization. Abstract and Applied Analysis No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1013848
American Medical Association (AMA)
Ma, Feng& Ni, Mingfang& Zhu, Lei& Yu, Zhanke. An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1013848
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1013848