A Riemannian conjugate gradient method for solving the tensor fixed-rank least-squares problem
In this paper, we consider the tensor fixed-rank least-squares problem arising in image restoration. The Riemannian conjugate gradient method with an exact line search technique is designed to solve this problem. A convergence analysis of this method is given. Numerical experiments with synthetic data and real images demonstrate the feasibility and effectiveness of the new method.
Authors
- Yu Gu (ORCID: https://orcid.org/0000-0002-8099-4716)
- Peng Hu (ORCID: https://orcid.org/0000-0002-2144-9806)
- Chunmei Li (ORCID: https://orcid.org/0000-0002-4210-7864)
- Xuefeng Duan (ORCID: https://orcid.org/0000-0001-9894-4904)
Publication Details
- Journal
- ETNA - Electronic Transactions on Numerical Analysis
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1553/etna_vol63s231
- Primary Topic
- Statistical and numerical algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangxi Province