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- Publisher Website: 10.1090/S0025-5718-2012-02598-1
- Scopus: eid_2-s2.0-84871450765
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Article: Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
Title | Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization |
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Authors | |
Keywords | Convex programming Linearized Low-rank Nuclear norm Alternating direction method Augmented Lagrangian method |
Issue Date | 2013 |
Citation | Mathematics of Computation, 2013, v. 82, n. 281, p. 301-329 How to Cite? |
Abstract | The nuclear norm is widely used to induce low-rank solutions for many optimization problems with matrix variables. Recently, it has been shown that the augmented Lagrangian method (ALM) and the alternating direction method (ADM) are very efficient for many convex programming problems arising from various applications, provided that the resulting subproblems are sufficiently simple to have closed-form solutions. In this paper, we are interested in the application of the ALM and the ADM for some nuclear norm involved minimization problems. When the resulting subproblems do not have closed-form solutions, we propose to linearize these subproblems such that closed-form solutions of these linearized subproblems can be easily derived. Global convergence results of these linearized ALM and ADM are established under standard assumptions. Finally, we verify the effectiveness and efficiency of these new methods by some numerical experiments. © 2012 American Mathematical Society. |
Persistent Identifier | http://hdl.handle.net/10722/251019 |
ISSN | 2023 Impact Factor: 2.2 2023 SCImago Journal Rankings: 1.460 |
DC Field | Value | Language |
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dc.contributor.author | Yang, Junfeng | - |
dc.contributor.author | Yuan, Xiaoming | - |
dc.date.accessioned | 2018-02-01T01:54:21Z | - |
dc.date.available | 2018-02-01T01:54:21Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | Mathematics of Computation, 2013, v. 82, n. 281, p. 301-329 | - |
dc.identifier.issn | 0025-5718 | - |
dc.identifier.uri | http://hdl.handle.net/10722/251019 | - |
dc.description.abstract | The nuclear norm is widely used to induce low-rank solutions for many optimization problems with matrix variables. Recently, it has been shown that the augmented Lagrangian method (ALM) and the alternating direction method (ADM) are very efficient for many convex programming problems arising from various applications, provided that the resulting subproblems are sufficiently simple to have closed-form solutions. In this paper, we are interested in the application of the ALM and the ADM for some nuclear norm involved minimization problems. When the resulting subproblems do not have closed-form solutions, we propose to linearize these subproblems such that closed-form solutions of these linearized subproblems can be easily derived. Global convergence results of these linearized ALM and ADM are established under standard assumptions. Finally, we verify the effectiveness and efficiency of these new methods by some numerical experiments. © 2012 American Mathematical Society. | - |
dc.language | eng | - |
dc.relation.ispartof | Mathematics of Computation | - |
dc.subject | Convex programming | - |
dc.subject | Linearized | - |
dc.subject | Low-rank | - |
dc.subject | Nuclear norm | - |
dc.subject | Alternating direction method | - |
dc.subject | Augmented Lagrangian method | - |
dc.title | Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1090/S0025-5718-2012-02598-1 | - |
dc.identifier.scopus | eid_2-s2.0-84871450765 | - |
dc.identifier.volume | 82 | - |
dc.identifier.issue | 281 | - |
dc.identifier.spage | 301 | - |
dc.identifier.epage | 329 | - |
dc.identifier.issnl | 0025-5718 | - |