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arXiv · 2407.03175

Low-Rank Toeplitz Matrix Restoration: Descent Cone Analysis and Structured Random Matrix

Gao Huang·Song Li
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Abstract

This note demonstrates that we can stably recover all symmetric Toeplitz matrices $\pmb{X}_0\in\mathbb{R}^{n\times n}$ of rank at most $r$ 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 a random matrix with Toeplitz 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, 61(7):4034--4059, 2015).

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Gao Huang, Song Li. 2024-07-03. Low-Rank Toeplitz Matrix Restoration: Descent Cone Analysis and Structured Random Matrix. https://doi.org/10.1109/tit.2025.3550960

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