Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917900391022592 |
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| author | Bian, Fengmiao Cai, Jian-Feng Zhang, Xiaoqun Zhang, Yuanwei |
| author_facet | Bian, Fengmiao Cai, Jian-Feng Zhang, Xiaoqun Zhang, Yuanwei |
| contents | Low-rank tensor completion aims to recover a tensor from partially observed entries, and it is widely applicable in fields such as quantum computing and image processing. Due to the significant advantages of the tensor train (TT) format in handling structured high-order tensors, this paper investigates the low-rank tensor completion problem based on the TT-format. We proposed a preconditioned Riemannian gradient descent algorithm (PRGD) to solve low TT-rank tensor completion and establish its linear convergence. Experimental results on both simulated and real datasets demonstrate the effectiveness of the PRGD algorithm. On the simulated dataset, the PRGD algorithm reduced the computation time by two orders of magnitude compared to existing classical algorithms. In practical applications such as hyperspectral image completion and quantum state tomography, the PRGD algorithm significantly reduced the number of iterations, thereby substantially reducing the computational time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13385 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent Bian, Fengmiao Cai, Jian-Feng Zhang, Xiaoqun Zhang, Yuanwei Machine Learning Numerical Analysis Optimization and Control Low-rank tensor completion aims to recover a tensor from partially observed entries, and it is widely applicable in fields such as quantum computing and image processing. Due to the significant advantages of the tensor train (TT) format in handling structured high-order tensors, this paper investigates the low-rank tensor completion problem based on the TT-format. We proposed a preconditioned Riemannian gradient descent algorithm (PRGD) to solve low TT-rank tensor completion and establish its linear convergence. Experimental results on both simulated and real datasets demonstrate the effectiveness of the PRGD algorithm. On the simulated dataset, the PRGD algorithm reduced the computation time by two orders of magnitude compared to existing classical algorithms. In practical applications such as hyperspectral image completion and quantum state tomography, the PRGD algorithm significantly reduced the number of iterations, thereby substantially reducing the computational time. |
| title | Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent |
| topic | Machine Learning Numerical Analysis Optimization and Control |
| url | https://arxiv.org/abs/2501.13385 |