A Poisson limit for the number of sub-matrices of random binary matrices satisfying the majority rule

We consider m × n random binary matrices, m = m ( n ) . For arbitrary odd integer k we investigate the asymptotic distribution of the random number of sub-matrices of size k × n for which the number of ones in every column satisfies the majority rule. We discuss possible impacts of our result and give examples of applications.

Authors

Institutions

Publication Details

Journal
Discrete Applied Mathematics
Published
2025-10-03
DOI
https://doi.org/10.1016/j.dam.2025.09.004
Primary Topic
Random Matrices and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Poisson limit for the number of sub-matrices of random binary matrices satisfying the majority rule

Andreas Wendemuth, Italo Simonelli
Discrete Applied Mathematics
Random Matrices and Applications
article

A Poisson limit for the number of sub-matrices of random binary matrices satisfying the majority rule

Andreas Wendemuth, Italo Simonelli
article en

Abstract

We consider m × n random binary matrices, m = m ( n ) . For arbitrary odd integer k we investigate the asymptotic distribution of the random number of sub-matrices of size k × n for which the number of ones in every column satisfies the majority rule. We discuss possible impacts of our result and give examples of applications.

Discrete Applied MathematicsVol. 380
Duke Kunshan University (CN), Otto-von-Guericke-Universität Magdeburg (DE)
Openalex Percentile: Top 74%
Random Matrices and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.