Acceleration of the BSS Algorithm

Bartel, Schäfer and Ullrich investigated the BSS subsampling algorithm, which is based on the work by Batson, Spielman and Srivastava. While this algorithm works well, the bottleneck is its complexity of $\mathcal{O}(σ^2nm^3)$ for a frame of $n$ vectors of dimension $m$ and oversampling $σ^2$. We continue their work by showing that the algorithm can be implemented in $\mathcal{O}(σ^2nm^{2.5})$. Moreover, for some special cases, an even faster implementation is possible. In particular, BSS subsampling of Fourier-type frames on a full frequency cube can be implemented in essentially $\mathcal{O}(σ^2m^2(n+m^{1.5}))$.}

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-26
DOI
https://doi.org/10.48550/arxiv.2610.35474
Primary Topic
Sparse and Compressive Sensing Techniques
Type
preprint
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preprint

Acceleration of the BSS Algorithm

Tizian Sommerfeld
arXiv (Cornell University)
Sparse and Compressive Sensing Techniques
preprint

Acceleration of the BSS Algorithm

Tizian Sommerfeld
preprint en

Abstract

Bartel, Schäfer and Ullrich investigated the BSS subsampling algorithm, which is based on the work by Batson, Spielman and Srivastava. While this algorithm works well, the bottleneck is its complexity of $\mathcal{O}(σ^2nm^3)$ for a frame of $n$ vectors of dimension $m$ and oversampling $σ^2$. We continue their work by showing that the algorithm can be implemented in $\mathcal{O}(σ^2nm^{2.5})$. Moreover, for some special cases, an even faster implementation is possible. In particular, BSS subsampling of Fourier-type frames on a full frequency cube can be implemented in essentially $\mathcal{O}(σ^2m^2(n+m^{1.5}))$.}

arXiv (Cornell University)
Sparse and Compressive Sensing Techniques
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Acceleration of the BSS Algorithm — Tizian Sommerfeld · arXiv (Cornell University) (2026) | TGRS Research Map | TGRS