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Autori principali: Balister, Paul, Kronenberg, Gal, Scott, Alex, Tamitegama, Youri
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2401.05058
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author Balister, Paul
Kronenberg, Gal
Scott, Alex
Tamitegama, Youri
author_facet Balister, Paul
Kronenberg, Gal
Scott, Alex
Tamitegama, Youri
contents A matrix is given in ``shredded'' form if we are presented with the multiset of rows and the multiset of columns, but not told which row is which or which column is which. The matrix is reconstructible if it is uniquely determined by this information. Let $M$ be a random binary $n\times n$ matrix, where each entry independently is $1$ with probability $p=p(n)\le\frac12$. Atamanchuk, Devroye and Vicenzo introduced the problem and showed that $M$ is reconstructible with high probability for $p\ge (2+\varepsilon)\frac{1}{n}\log n$. Here we find that the sharp threshold for reconstructibility is at $p\sim\frac{1}{2n}\log n$.
format Preprint
id arxiv_https___arxiv_org_abs_2401_05058
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Reconstruction of shredded random matrices
Balister, Paul
Kronenberg, Gal
Scott, Alex
Tamitegama, Youri
Combinatorics
A matrix is given in ``shredded'' form if we are presented with the multiset of rows and the multiset of columns, but not told which row is which or which column is which. The matrix is reconstructible if it is uniquely determined by this information. Let $M$ be a random binary $n\times n$ matrix, where each entry independently is $1$ with probability $p=p(n)\le\frac12$. Atamanchuk, Devroye and Vicenzo introduced the problem and showed that $M$ is reconstructible with high probability for $p\ge (2+\varepsilon)\frac{1}{n}\log n$. Here we find that the sharp threshold for reconstructibility is at $p\sim\frac{1}{2n}\log n$.
title Reconstruction of shredded random matrices
topic Combinatorics
url https://arxiv.org/abs/2401.05058