Low-Rank Toeplitz Matrix Restoration: Descent Cone Analysis and Structured Random Matrix
<|reference_start|>Low-Rank Toeplitz Matrix Restoration: Descent Cone Analysis and Structured Random Matrix: This note demonstrates that we can stably recover rank $r$ Toeplitz matrix $\pmb{X}\in\mathbb{R}^{n\times n}$ from a number of rank one subgaussian measurements on the order of $r\log^{2} n$ with an exponentially decreasing failure probability by employing a nuclear norm minimization program. Our approach utilizes descent cone analysis through Mendelson's small ball method with the Toeplitz constraint. The key ingredient is to determine the spectral norm of the random matrix of the Topelitz structure, which may be of independent interest.This improves upon earlier analyses and resolves the conjecture in Chen et al. (IEEE Transactions on Information Theory, 2015).<|reference_end|>
@article{huang2024low-rank,
    title={Low-Rank Toeplitz Matrix Restoration: Descent Cone Analysis and
  Structured Random Matrix},
    author={Gao Huang and Song Li},
    journal={arXiv preprint arXiv:2407.03175},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03175},
    primaryClass={cs.IT math.IT}
}