Sublinear Time Low-Rank Approximation of Hankel Matrices
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arXiv
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| Natura: | Preprint |
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2025
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| _version_ | 1866917106561318912 |
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| author | Kapralov, Michael Musco, Cameron Sheth, Kshiteej |
| author_facet | Kapralov, Michael Musco, Cameron Sheth, Kshiteej |
| contents | Hankel matrices are an important class of highly-structured matrices, arising across computational mathematics, engineering, and theoretical computer science. It is well-known that positive semidefinite (PSD) Hankel matrices are always approximately low-rank. In particular, a celebrated result of Beckermann and Townsend shows that, for any PSD Hankel matrix $H \in \mathbb{R}^{n \times n}$ and any $ε> 0$, letting $H_k$ be the best rank-$k$ approximation of $H$, $\|H-H_k\|_F \leq ε\|H\|_F$ for $k = O(\log n \log(1/ε))$. As such, PSD Hankel matrices are natural targets for low-rank approximation algorithms. We give the first such algorithm that runs in \emph{sublinear time}. In particular, we show how to compute, in $\polylog(n, 1/ε)$ time, a factored representation of a rank-$O(\log n \log(1/ε))$ Hankel matrix $\widehat{H}$ matching the error guarantee of Beckermann and Townsend up to constant factors. We further show that our algorithm is \emph{robust} -- given input $H+E$ where $E \in \mathbb{R}^{n \times n}$ is an arbitrary non-Hankel noise matrix, we obtain error $\|H - \widehat{H}\|_F \leq O(\|E\|_F) + ε\|H\|_F$. Towards this algorithmic result, our first contribution is a \emph{structure-preserving} existence result - we show that there exists a rank-$k$ \emph{Hankel} approximation to $H$ matching the error bound of Beckermann and Townsend. Our result can be interpreted as a finite-dimensional analog of the widely applicable AAK theorem, which shows that the optimal low-rank approximation of an infinite Hankel operator is itself Hankel. Armed with our existence result, and leveraging the well-known Vandermonde structure of Hankel matrices, we achieve our sublinear time algorithm using a sampling-based approach that relies on universal ridge leverage score bounds for Vandermonde matrices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sublinear Time Low-Rank Approximation of Hankel Matrices Kapralov, Michael Musco, Cameron Sheth, Kshiteej Data Structures and Algorithms Numerical Analysis Hankel matrices are an important class of highly-structured matrices, arising across computational mathematics, engineering, and theoretical computer science. It is well-known that positive semidefinite (PSD) Hankel matrices are always approximately low-rank. In particular, a celebrated result of Beckermann and Townsend shows that, for any PSD Hankel matrix $H \in \mathbb{R}^{n \times n}$ and any $ε> 0$, letting $H_k$ be the best rank-$k$ approximation of $H$, $\|H-H_k\|_F \leq ε\|H\|_F$ for $k = O(\log n \log(1/ε))$. As such, PSD Hankel matrices are natural targets for low-rank approximation algorithms. We give the first such algorithm that runs in \emph{sublinear time}. In particular, we show how to compute, in $\polylog(n, 1/ε)$ time, a factored representation of a rank-$O(\log n \log(1/ε))$ Hankel matrix $\widehat{H}$ matching the error guarantee of Beckermann and Townsend up to constant factors. We further show that our algorithm is \emph{robust} -- given input $H+E$ where $E \in \mathbb{R}^{n \times n}$ is an arbitrary non-Hankel noise matrix, we obtain error $\|H - \widehat{H}\|_F \leq O(\|E\|_F) + ε\|H\|_F$. Towards this algorithmic result, our first contribution is a \emph{structure-preserving} existence result - we show that there exists a rank-$k$ \emph{Hankel} approximation to $H$ matching the error bound of Beckermann and Townsend. Our result can be interpreted as a finite-dimensional analog of the widely applicable AAK theorem, which shows that the optimal low-rank approximation of an infinite Hankel operator is itself Hankel. Armed with our existence result, and leveraging the well-known Vandermonde structure of Hankel matrices, we achieve our sublinear time algorithm using a sampling-based approach that relies on universal ridge leverage score bounds for Vandermonde matrices. |
| title | Sublinear Time Low-Rank Approximation of Hankel Matrices |
| topic | Data Structures and Algorithms Numerical Analysis |
| url | https://arxiv.org/abs/2511.21418 |