Accelerating Ill-conditioned Hankel Matrix Recovery via Structured Newton-like Descent
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866908789063548928 |
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| author | Cai, HanQin Huang, Longxiu Lu, Xiliang You, Juntao |
| author_facet | Cai, HanQin Huang, Longxiu Lu, Xiliang You, Juntao |
| contents | This paper studies the robust Hankel recovery problem, which simultaneously removes the sparse outliers and fulfills missing entries from the partial observation. We propose a novel non-convex algorithm, coined Hankel Structured Newton-Like Descent (HSNLD), to tackle the robust Hankel recovery problem. HSNLD is highly efficient with linear convergence, and its convergence rate is independent of the condition number of the underlying Hankel matrix. The recovery guarantee has been established under some mild conditions. Numerical experiments on both synthetic and real datasets show the superior performance of HSNLD against state-of-the-art algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07409 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Ill-conditioned Hankel Matrix Recovery via Structured Newton-like Descent Cai, HanQin Huang, Longxiu Lu, Xiliang You, Juntao Machine Learning Information Theory Signal Processing Optimization and Control 15A29, 15A83, 47B35, 90C17, 90C26, 90C53 This paper studies the robust Hankel recovery problem, which simultaneously removes the sparse outliers and fulfills missing entries from the partial observation. We propose a novel non-convex algorithm, coined Hankel Structured Newton-Like Descent (HSNLD), to tackle the robust Hankel recovery problem. HSNLD is highly efficient with linear convergence, and its convergence rate is independent of the condition number of the underlying Hankel matrix. The recovery guarantee has been established under some mild conditions. Numerical experiments on both synthetic and real datasets show the superior performance of HSNLD against state-of-the-art algorithms. |
| title | Accelerating Ill-conditioned Hankel Matrix Recovery via Structured Newton-like Descent |
| topic | Machine Learning Information Theory Signal Processing Optimization and Control 15A29, 15A83, 47B35, 90C17, 90C26, 90C53 |
| url | https://arxiv.org/abs/2406.07409 |