ADMM algorithms for matrix completion problem in noisy settings
Matrix completion (MC) is a fundamental linear algebra problem to fully recover a low-rank matrix from its incomplete data. It is widely applied in machine learning and statistics, varied from wireless communication, image compression to collaborative filtering. Meanwhile, Alternating Direction Meth...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/148502 |
| _version_ | 1811685622571597824 |
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| author | Le, Tran Kien |
| author2 | Chua Chek Beng |
| author_facet | Chua Chek Beng Le, Tran Kien |
| author_sort | Le, Tran Kien |
| collection | NTU |
| description | Matrix completion (MC) is a fundamental linear algebra problem to fully recover a low-rank matrix from its incomplete data. It is widely applied in machine learning and statistics, varied from wireless communication, image compression to collaborative filtering. Meanwhile, Alternating Direction Method of Multiplier is a straightforward but effective algorithm for distributed convex optimization. In this work, we will study ADMM in application to matrix completion problem in the noisy setting. Two modified algorithms for noisy matrix completion problem are proposed. Convergence results of these algorithms will be discussed and numerical experiments are conducted to examine the performance of the new algorithms. |
| first_indexed | 2024-10-01T04:47:27Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/148502 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:47:27Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1485022023-02-28T23:14:06Z ADMM algorithms for matrix completion problem in noisy settings Le, Tran Kien Chua Chek Beng School of Physical and Mathematical Sciences CBChua@ntu.edu.sg Science::Mathematics Matrix completion (MC) is a fundamental linear algebra problem to fully recover a low-rank matrix from its incomplete data. It is widely applied in machine learning and statistics, varied from wireless communication, image compression to collaborative filtering. Meanwhile, Alternating Direction Method of Multiplier is a straightforward but effective algorithm for distributed convex optimization. In this work, we will study ADMM in application to matrix completion problem in the noisy setting. Two modified algorithms for noisy matrix completion problem are proposed. Convergence results of these algorithms will be discussed and numerical experiments are conducted to examine the performance of the new algorithms. Bachelor of Science in Mathematical Sciences and Economics 2021-04-28T05:01:00Z 2021-04-28T05:01:00Z 2021 Final Year Project (FYP) Le, T. K. (2021). ADMM algorithms for matrix completion problem in noisy settings. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148502 https://hdl.handle.net/10356/148502 en application/pdf Nanyang Technological University |
| spellingShingle | Science::Mathematics Le, Tran Kien ADMM algorithms for matrix completion problem in noisy settings |
| title | ADMM algorithms for matrix completion problem in noisy settings |
| title_full | ADMM algorithms for matrix completion problem in noisy settings |
| title_fullStr | ADMM algorithms for matrix completion problem in noisy settings |
| title_full_unstemmed | ADMM algorithms for matrix completion problem in noisy settings |
| title_short | ADMM algorithms for matrix completion problem in noisy settings |
| title_sort | admm algorithms for matrix completion problem in noisy settings |
| topic | Science::Mathematics |
| url | https://hdl.handle.net/10356/148502 |
| work_keys_str_mv | AT letrankien admmalgorithmsformatrixcompletionprobleminnoisysettings |