Superfast 1-Norm Estimation

Fuente: arXiv
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Main Authors: Go, Soo, Pan, Victor Y.
Format: Preprint
Published: 2025
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author Go, Soo
Pan, Victor Y.
author_facet Go, Soo
Pan, Victor Y.
contents A matrix algorithm is said to be superfast (that is, runs at sublinear cost) if it involves much fewer scalars and flops than the input matrix has entries. Such algorithms have been extensively studied and widely applied in modern computations for matrices with low displacement rank and more recently for low-rank approximation of matrices, even though they are known to fail on worst-case inputs in the latter application. We devise novel superfast algorithms that consistently produce accurate 1-norm estimates for real-world matrices and discuss some promising extensions of our surprisingly simple techniques. With further testing and refinement, our algorithms can potentially be adopted in practical computations.
format Preprint
id arxiv_https___arxiv_org_abs_2505_20528
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Superfast 1-Norm Estimation
Go, Soo
Pan, Victor Y.
Numerical Analysis
65F35, 65Y20, 68Q25
A matrix algorithm is said to be superfast (that is, runs at sublinear cost) if it involves much fewer scalars and flops than the input matrix has entries. Such algorithms have been extensively studied and widely applied in modern computations for matrices with low displacement rank and more recently for low-rank approximation of matrices, even though they are known to fail on worst-case inputs in the latter application. We devise novel superfast algorithms that consistently produce accurate 1-norm estimates for real-world matrices and discuss some promising extensions of our surprisingly simple techniques. With further testing and refinement, our algorithms can potentially be adopted in practical computations.
title Superfast 1-Norm Estimation
topic Numerical Analysis
65F35, 65Y20, 68Q25
url https://arxiv.org/abs/2505.20528